Showing 1–50 of 85 results for abstract: "pressure map"

TacGooseBumps (TacGB): Retrofitting Normal-Only Tactile Sensors with Shear Encoding for Learning Contact-Rich Manipulation

Authors: Wenjie Li, Binyu Yang, Yuxin Chen, Ambrose Wang, Masayoshi Tomizuka

Abstract: …contact states. We introduce TacGooseBumps (TacGB), a passive domed film that mechanically encodes tangential loading as pattern changes in an existing sensor's pressure… ▽ More Contact-rich policies often fail because distinct physical states look alike yet require different actions. Cameras may not reveal whether a connector is aligned or fully seated, while many normal-only tactile sensors can miss the tangential interactions perpendicular to the grasping direction that distinguish these states. We ask whether a learning policy needs calibrated shear measurements, or only a repeatable observation that separates shear-dependent contact states. We introduce TacGooseBumps (TacGB), a passive domed film that mechanically encodes tangential loading as pattern changes in an existing sensor's pressure map. Tangential loading tilts each dome and redistributes pressure across its footprint; an end-to-end policy consumes the resulting maps without added electronics, force reconstruction, or taxel-level dome alignment. Across four imitation-learning tasks and two data-collection pipelines, TacGB improves goal attainment, efficiency, and contact quality: insertion success increases by up to 36 percentage points, and successful insertions are completed faster, while fragile-object placement becomes gentler and drawing becomes more continuous and straight. Signal, stage-wise, failure-mode, and trajectory analyses link these gains to contact regimes in which task-relevant tangential interactions are poorly resolved by vision and normal pressure alone. Together, these results show that shear need not be measured metrically to benefit robot learning; it can instead be mechanically encoded without changing the underlying tactile sensor or the policy's pressure-map input format. △ Less

Submitted 27 September, 2026; originally announced September 2026.

Comments: 9 pages, 8 figures. Wenjie Li and Binyu Yang contributed equally

Pharmacokinetic State Space Models for Unbiased Prediction of Haemodynamic Collapse

Authors: Rithin Nagaraj, Sudiksha Chindula, Bhaskarjyoti Das

Abstract: …to the full model. This is consistent with the hypothesis that pharmacokinetic trajectories encode impending haemodynamic changes before they manifest in the Mean Arterial Pressure (MAP). Additionally, this paper shows that training without lead-gap filtering degraded AUROC by 16.7%, empirically confirming that unfilte… ▽ More An Intraoperative Hypotension (IOH) event is a frequent complication during administration of general anaesthesia with serious downstream consequences, yet clinical management remains reactive and not predictive. Existing predictive models, however, ignore drug infusion history as a valuable signal for prediction despite its direct pharmacological relevance. Our model achieves an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.7360 and an Area Under the Precision-Recall Curve (AUPRC) of 0.1794, representing a 2.73-fold lift over the random guessing AUPRC baseline (0.0657), with the removal of propofol and remifentanil effect-site concentrations resulting in a 13.9% AUPRC drop compared to the full model. This is consistent with the hypothesis that pharmacokinetic trajectories encode impending haemodynamic changes before they manifest in the Mean Arterial Pressure (MAP). Additionally, this paper shows that training without lead-gap filtering degraded AUROC by 16.7%, empirically confirming that unfiltered models learn to detect ongoing hypotension rather than predict future events. Finally, a Mamba-based architecture achieves the aforementioned high prediction performance while maintaining a constant memory footprint across a range of sequence lengths, unlike the quadratic VRAM overhead typical of vanilla Transformers, making it the more practical choice for continuous intraoperative deployment. △ Less

Submitted 21 September, 2026; originally announced September 2026.

Comments: Published in Springer Nature after presenting at the International Conference on AI in Healthcare, London

Layered e-skin for Shear Sensing

Authors: Qingzheng Cong, Alexis W. M. Devillard, Abu Bakar Dawood, Xinxin Zhang, Wen Fan, Neri Niccolò Dei, Cem Suulker, Kaspar Althoefer, Etienne Burdet, Dandan Zhang

Abstract: This paper presents a stacked two-layer force-sensing resistor (FSR) array designed for robotic fingertips that combines high-resolution pressure mapping with shear-force estimation. A compliant lattice elastomer spacer converts shear loading into a measurable inter-layer displacement, producing relative center-of-pres… ▽ More This paper presents a stacked two-layer force-sensing resistor (FSR) array designed for robotic fingertips that combines high-resolution pressure mapping with shear-force estimation. A compliant lattice elastomer spacer converts shear loading into a measurable inter-layer displacement, producing relative center-of-pressure (CoP) shifts between layers. A physics-based moment balance links inter-layer CoP displacement to shear force, while an end-to-end CNN--GRU model captures nonlinear effects from load-dependent compression and contact redistribution. This model, with both layers as input, achieves coefficients of determination $R^2 = 0.914$ for $F_x$ and $R^2 = 0.944$ for $F_y$, consistently outperforming single-layer baselines for shear-force estimation. Robotic manipulation experiments show that, for contact-motion tracking, the deep layer tracks the translation and rotation imposed by the robot arm, whereas the superficial layer tracks the slip at the contact surface. Transient changes in the difference between the total pressure responses of the two layers provide the best slip-event detection performance among the tested cues. These results demonstrate that two-layer FSR arrays can provide three-axis force estimation, contact-motion tracking, and slip-event detection beyond conventional normal-force sensing. △ Less

Submitted 18 September, 2026; originally announced September 2026.

Comments: 8 pages, 5 figures. Submitted to IEEE Sensors Journal

Adaptive Uncertainty-Aware Modeling and Stochastic Radial Basis Function Predictive Control for Personalized Fluid Resuscitation

Authors: Elham Estiri, Hossein Mirinejad

Abstract: …fluid resuscitation. An uncertainty-aware variational autoencoder state-space model (UVAE-SSM) was first developed to capture the dynamical relationship between mean arterial pressure (MAP) and fluid infusion using limited data, while explicitly modeling aleatoric uncertainty (i.e., randomness in the measurements, such… ▽ More This paper presents a novel framework integrating Bayesian physiological modeling with optimal control strategies to achieve uncertainty-aware, personalized hemodynamic regulation during fluid resuscitation. An uncertainty-aware variational autoencoder state-space model (UVAE-SSM) was first developed to capture the dynamical relationship between mean arterial pressure (MAP) and fluid infusion using limited data, while explicitly modeling aleatoric uncertainty (i.e., randomness in the measurements, such as sensor noise). Then, a Bayesian nonlinear state-space model (BNSSM) was developed by utilizing Bayesian neural networks (BNNs) to capture epistemic uncertainty arising from physiological and patient-specific variability, enabling the creation of a virtual patient generator (VPG). Building on this uncertainty-aware modeling framework, a stochastic radial basis function model predictive control (sRBF-MPC) algorithm was designed to track the MAP target while satisfying physiological constraints. Finally, an online fine-tuning algorithm was developed to adapt the nominal UVAE-SSM using streaming VPG data, enabling progressive personalization during closed-loop therapy. Simulation results across unseen animal subjects and an independent human clinical dataset demonstrated the strong predictive accuracy and cross-population generalizability of the UVAE-SSM and BNSSM models. Closed-loop evaluations confirmed that the proposed sRBF-MPC framework achieved stable MAP regulation while providing better risk-aware control compared to quadratic MPC (Q-MPC) and stochastic quadratic MPC (sQ-MPC). Overall, the proposed framework accounts for inter- and intra-patient variability through online model adaptation, offering a promising step toward uncertainty-aware, personalized hemodynamic modeling and control in critical care. △ Less

Submitted 18 September, 2026; originally announced September 2026.

Multimodal Visual Sensing of Temperature and Pressure: From Spectroscopic Readout to Multiple Linear Regression-Enhanced RGB Analysis

Authors: Maja Szymczak, Yufan Meng, Guanjun Xiao, Miguel A. Hernández-Rodríguez, Iga Sawaryn, Bo Zou, Lukasz Marciniak

Abstract: …luminescence, chromaticity coordinates, spectral shifts, and visible color changes. Importantly, these color changes were translated into quantitative temperature and pressure maps using RGB imaging combined with multiple linear regression (MLR). By simultaneously exploiting multiple color channels, MLR increased the m… ▽ More Transforming a single luminescent host into distinct temperature- and pressure-sensing systems through composition control provides a powerful route toward multifunctional optical sensors. Here, we introduce a concentration-tunable KGaGeO4:Bi3+,Eu3+ platform whose sensing function can be selectively directed toward thermometry or manometry by adjusting the dopant balance. Spectroscopically distinct Bi3+ centers and Eu3+ emission exhibit differentiated responses to temperature and pressure, enabling multimodal readout through ratiometric luminescence, chromaticity coordinates, spectral shifts, and visible color changes. Importantly, these color changes were translated into quantitative temperature and pressure maps using RGB imaging combined with multiple linear regression (MLR). By simultaneously exploiting multiple color channels, MLR increased the maximum relative thermal sensitivity from approximately 1% K-1 for conventional RGB ratios to 8.8% K-1 and the pressure sensitivity from 169% GPa-1 to nearly 721% GPa-1. This work presents the first application of MLR-assisted RGB analysis for quantitative luminescence pressure sensing. The proposed strategy integrates composition-controlled functionality, multimodal spectroscopic sensing, direct visual readout, and data-assisted imaging, demonstrating the synergy between material engineering and multivariate analysis for highly sensitive multifunctional optical sensing. △ Less

Submitted 18 September, 2026; originally announced September 2026.

The Magnetic-Resonance-Based, Omni-Directional Hydrophone

Authors: Nathan Thyberg, Davi Cavinatto, Katia Oler, Elijah Oxborrow, Hyrum Mangum, Steven P. Allen

Abstract: …relative to the MRI bore-axis. The method reconstructed pressure maps at all three angles of propagation, including features such as peak pressure, partial reflections, and beamwidth. The MRbOH method measured an average beamwidth of 16.2 mm while the hydrophone measured a beamwidth of 14.5 mm. Average hydrophone-meas… ▽ More This study aims to improve the directional sensitivity of magnetic resonance hydrophone (MRH) methods and allow for detection and quantification of waves traveling at any angle relative to the MRI bore-axis. Doing so may better integrate MRH technology into transcranial focused ultrasound neuromodulation therapies. Existing MRH implementations only encode acoustic waves propagating along the MRI bore-axis, precluding use with wide aperture transducers or temporal skull windows. This study expands the MRH technique into a MR-based Omni-directional Hydrophone (MRbOH) that can quantify the magnitude pressure and phase of waves propagating at any angle. The MRbOH method formulates a system of equations around uniquely sensitized images that, when solved, introduce sensitivity to oblique acoustic waves. The method was tested experimentally in a gel phantom that was insonated at $0^o$, $45^o$, and $90^o$ relative to the MRI bore-axis. The method reconstructed pressure maps at all three angles of propagation, including features such as peak pressure, partial reflections, and beamwidth. The MRbOH method measured an average beamwidth of 16.2 mm while the hydrophone measured a beamwidth of 14.5 mm. Average hydrophone-measured pressure within a region of interest was 416.19 $\boldsymbol{\pm}$ 78.07 kPa. Average MRbOH measured pressure in the same region at $0^o$, $45^o$, and $90^o$, respectively, were: 285.5 $\boldsymbol{\pm}$ 49.5 kPa, 193.13 $\boldsymbol{\pm}$ 43.55 kPa, 194.7 $\boldsymbol{\pm}$ 47.38 kPa. In addition, the study presents and experimentally verifies a statistical noise model that predicted the mean with $R^2 = 0.85$, and standard deviation with $R^2 = 0.83$. △ Less

Submitted 15 September, 2026; originally announced September 2026.

Comments: 18 pages, 10 figures, This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Learning a Continuous Sepsis Severity Score Without Hour-by-Hour Supervision: A Two-Site Retrospective Study

Authors: Kevin Zhu, Ryan Zhang, Baraa Abed, Tilendra Choudhary, Malvern Madondo, Mehak Arora, Yixuan Yang, Alasdair Gent, Aditya Nagori, Omer T. Inan, Krista L. Haines, Patrick Georgoff, Suresh M. Agarwal, Vijay Krishnamoorthy, Tetsu Ohnuma, Mihai V. Podgoreanu, Michael R. Pinsky, Gilles Clermont, Craig M. Coopersmith, Craig S. Jabaley, Rishikesan Kamaleswaran

Abstract: …scored 1.19-1.64 points higher than survivors on a 0-10 scale within all strata of baseline SOFA-2, with similar results stratifying within lactate, mean arterial pressure (MAP), and creatinine. Within-patient change in the index correlated with change in lactate (Spearman rho = 0.39; n = 1,854). Similar, weaker correl… ▽ More Currently used sepsis severity indices rely on fixed variables and weights established decades ago, which are coarsely discretized and calibrated to a cohort that no longer reflects contemporary critical care. No alternative learned directly from patient trajectories is in routine use. We conducted a retrospective two-cohort study on a total of 29,116 and 7,691 adult patients meeting Sepsis-3 criteria from two hospital systems in Massachusetts and Georgie, respectively. We developed a sepsis index using 43 routinely charted variables over a 72-hour treatment window. Unlike previous studies, we use mortality as a treatment-level ranking signal rather than a per-state target, allowing credit to be redistributed non-uniformly across timesteps. Evaluation was done on a permanent 20% test holdout, using clinical vignettes and Spearman correlation. Uncertainty intervals were obtained by bootstrap resampling of whole patients. Under this ranking scheme, non-survivors scored 1.19-1.64 points higher than survivors on a 0-10 scale within all strata of baseline SOFA-2, with similar results stratifying within lactate, mean arterial pressure (MAP), and creatinine. Within-patient change in the index correlated with change in lactate (Spearman rho = 0.39; n = 1,854). Similar, weaker correlations were found for MAP and creatinine. On a cohort level, cross-institutional agreement measured by Spearman correlation between models trained on different sites, were 70-77% of same-site correlation. External within-patient correlations were 0.54 and 0.59 against ceilings of 0.92 and 0.90. Our index also correlated with established indices, while null controls stayed near zero. Our index demonstrated hourly prognostic information that meaningfully separates patient outcomes and is consistent with clinical expectation, indicating potential as a decision support tool complementing clinical judgement. △ Less

Submitted 27 August, 2026; originally announced August 2026.

Deformable In-Hand Slip-Aware Tactile Sensor with Integrated Velocity, Force/Torque, and Pressure Map Sensing

Authors: Gabriel Arslan Waltersson, Yiannis Karayiannidis

Abstract: This paper introduces a novel tactile sensor for in-hand manipulation with slip-aware control that integrates velocity, force/torque, and pressure map sensing into a single device with a deformable contact pad. To the best of our knowledge, this is the first sensor to combine these sensing modalities within a single co… ▽ More This paper introduces a novel tactile sensor for in-hand manipulation with slip-aware control that integrates velocity, force/torque, and pressure map sensing into a single device with a deformable contact pad. To the best of our knowledge, this is the first sensor to combine these sensing modalities within a single compliant structure. The sensor features a deformable contact surface and can robustly track both flat and curved surfaces across a wide range of diffuse surface materials. Its performance is evaluated through a comprehensive set of experiments that highlight both its capabilities and limitations. The sensor is designed for rapid and low-cost fabrication using a combination of standard PCB manufacturing and rapid prototyping techniques. △ Less

Submitted 12 August, 2026; v1 submitted 10 June, 2026; originally announced June 2026.

DRIFT: Direct-Recursive Intervention-Conditioned Forecasting of ICU Physiological Trajectories

Authors: Weixin Liu, Juming Xiong, Congning Ni, Yanfan Zhu, Xingtao Lin, Bradley A. Malin, Zhijun Yin

Abstract: …admissions from MIMIC-IV and 8,345 admissions from eICU-CRD. Averaged across the 8-, 24-, and 48-hour forecast endpoints, DRIFT reduces mean absolute error for mean arterial pressure (MAP) by 0.673% relative to an action-conditioned Temporal Fusion Transformer (TFT-action) on MIMIC-IV and achieves the lowest correspond… ▽ More Many time-series forecasts depend not only on prior observations but also on actions specified during the forecast period. In intensive care units (ICUs), future vital signs and laboratory values are influenced by treatments such as vasopressors. However, models that predict the full future sequence all at once make little use of these treatments, whereas autoregressive models can accumulate errors. We introduce DRIFT, a hybrid framework in which a direct model produces the primary forecast and a recursive, action-conditioned model contributes constrained corrections. We evaluate DRIFT on 6,046 admissions from MIMIC-IV and 8,345 admissions from eICU-CRD. Averaged across the 8-, 24-, and 48-hour forecast endpoints, DRIFT reduces mean absolute error for mean arterial pressure (MAP) by 0.673% relative to an action-conditioned Temporal Fusion Transformer (TFT-action) on MIMIC-IV and achieves the lowest corresponding error among the compared models on eICU-CRD. Although the overall accuracy improvement is modest, a MIMIC-IV audit restricted to windows in which the supplied treatment sequence was altered showed that DRIFT achieved lower observed-target MAP error than TFT-action at 8 and 24 hours. Treatment-sequence alteration increased DRIFT's MAP error by 0.21-0.26 mmHg more than it increased TFT-action's error, with prediction changes occurring primarily after the supplied paths diverged. In a separate robustness experiment, the MAP advantage persisted under three shared checkpoint-selection rules emphasizing overall endpoint error, MAP error, or both equally. △ Less

Submitted 28 July, 2026; originally announced July 2026.

Comments: 34 pages, 1 figure; extended technical appendices included

EgoPressDiff: Multimodal Video Diffusion for Egocentric UV-Domain Hand-Pressure Estimation

Authors: Yuan Zeng, Zilue Gao, Yujia Shi, Zongqing Lu, Wenming Yang, QingMin Liao

Abstract: …frames independently, leading to quantization errors and temporal inconsistencies. We present \emph{EgoPressDiff}, a conditional video diffusion framework that generates UV-pressure maps from visual input. The core of our approach is a multi-modal conditioning strategy, introducing a PoseNet and a Vertex Encoder to eff… ▽ More Estimating hand-surface contact pressure from an egocentric view is crucial for AR/VR devices, robotic imitation, and ergonomic analysis. Existing methods often discretize pressure signal and process frames independently, leading to quantization errors and temporal inconsistencies. We present \emph{EgoPressDiff}, a conditional video diffusion framework that generates UV-pressure maps from visual input. The core of our approach is a multi-modal conditioning strategy, introducing a PoseNet and a Vertex Encoder to efficiently extract features from hand pose and 3D mesh vertices. These signals, along with depth information, guide the generative process to ensure the pressure fields are physically grounded. To effectively fuse these heterogeneous features, we further propose a Distribution-Calibrated Spatial Layer, which aligns their statistical properties before combination. Evaluated on the EgoPressure ego-view setting, EgoPressDiff achieves state-of-the-art results, improving Volumetric IoU by over 34\% relative to prior baseline, while reducing MAE and maintaining high temporal accuracy. Our project page is at https://egopressdiff.github.io/. △ Less

Submitted 4 June, 2026; originally announced June 2026.

Comments: Accepted to IEEE ICASSP 2026

TouchAnything: A Dataset and Framework for Bimanual Tactile Estimation from Egocentric Video

Authors: Jianyi Zhou, Ziteng Gao, Feiyang Hong, Zirui Liu, Guannan Zhang, Weisheng Dai, Ruichen Zhen, Chuqiao Lyu, Haotian Wu, Yinian Mao, Xushi Wang, Yuxiang Jiang, Wenbo Ding, Shuo Yang

Abstract: …in diverse indoor and outdoor environments, with synchronized multi-view RGB (head-mounted egocentric and dual wrist-mounted cameras), bimanual 3D hand pose, and continuous pressure maps from wearable tactile sensors. Building on EgoTouch, we introduce TouchAnything, a baseline multi-view vision-to-touch prediction fra… ▽ More Egocentric human video data, which captures rich human-environment interactions and can be collected at scale, has become a key driver of embodied intelligence research. However, existing egocentric datasets typically lack tactile sensing, a critical modality that provides direct cues about contact, force, and pressure in human-object interaction. Without such signals, models struggle to learn physically grounded representations of real-world interaction dynamics. While tactile sensors provide these cues, deploying high-quality tactile hardware at scale remains expensive and cumbersome. This raises a central question: can tactile feedback be inferred directly from visual observations, enabling scalable tactile supervision for egocentric video data and supporting physically grounded embodied learning? To enable research in this direction, we introduce EgoTouch, a large-scale multi-view egocentric dataset with dense tactile supervision for bimanual hand-object interaction. EgoTouch comprises 208 manipulation tasks spanning 1,891 episodes in diverse indoor and outdoor environments, with synchronized multi-view RGB (head-mounted egocentric and dual wrist-mounted cameras), bimanual 3D hand pose, and continuous pressure maps from wearable tactile sensors. Building on EgoTouch, we introduce TouchAnything, a baseline multi-view vision-to-touch prediction framework that uses the egocentric view as the primary input and flexibly leverages available wrist-mounted views at inference time. Experiments show that incorporating wrist-mounted views generally improves tactile prediction over egocentric-only input, achieving up to 5.0% relative improvement in Contact IoU and 6.1% relative improvement in Volumetric IoU. We will publicly release the dataset, code, and benchmark. △ Less

Submitted 13 May, 2026; originally announced May 2026.

Multi-modal 3D Pose and Shape Estimation with Computed Tomography

Authors: Mingxiao Tu, Hoijoon Jung, Alireza Moghadam, Jineel Raythatha, Lachlan Allan, Jeremy Hsu, Andre Kyme, Jinman Kim

Abstract: …reality-based surgical navigation, and mitigating risks of prolonged immobility during recovery. Conventional PSE methods relying on modalities such as RGB-D, infrared, or pressure maps often struggle with occlusions caused by bedding and complex patient positioning, leading to inaccurate estimation that can affect cli… ▽ More In perioperative care, precise in-bed 3D patient pose and shape estimation (PSE) can be vital in optimizing patient positioning in preoperative planning, enabling accurate overlay of medical images for augmented reality-based surgical navigation, and mitigating risks of prolonged immobility during recovery. Conventional PSE methods relying on modalities such as RGB-D, infrared, or pressure maps often struggle with occlusions caused by bedding and complex patient positioning, leading to inaccurate estimation that can affect clinical outcomes. To address these challenges, we present the first multi-modal in-bed patient 3D PSE network that fuses detailed geometric features extracted from routinely acquired computed tomography (CT) scans with depth maps (mPSE-CT). mPSE-CT incorporates a shape estimation module that utilizes probabilistic correspondence alignment, a pose estimation module with a refined neural network, and a final parameters mixing module. This multi-modal network robustly reconstructs occluded body regions and enhances the accuracy of the estimated 3D human mesh model. We validated mPSE-CT using proprietary whole-body rigid phantom and volunteer datasets in clinical scenarios. mPSE-CT outperformed the best-performing prior method by 23% and 49.16% in pose and shape estimation respectively, demonstrating its potential for improving clinical outcomes in challenging perioperative environments. △ Less

Submitted 17 March, 2026; v1 submitted 25 March, 2025; originally announced March 2025.

Improving the Plausibility of Pressure Distributions Synthesized from Depth Image through Generative Modeling

Authors: Neevkumar Manavar, Hanno Gerd Meyer, Joachim Waßmuth, Barbara Hammer, Axel Schneider

Abstract: Monitoring contact pressure in hospital beds is essential for preventing pressure ulcers and enabling real-time patient assessment. Current methods can predict pressure maps but often lack physical plausibility, limiting clinical reliability. This work proposes a framework that enhances plausibility via Informed Latent… ▽ More Monitoring contact pressure in hospital beds is essential for preventing pressure ulcers and enabling real-time patient assessment. Current methods can predict pressure maps but often lack physical plausibility, limiting clinical reliability. This work proposes a framework that enhances plausibility via Informed Latent Space (ILS) and Weight Optimization Loss (WOL) with conditional generative modeling to produce high-fidelity, physically consistent pressure estimates. This study also applies diffusion based conditional Brownian Bridge Diffusion Model (BBDM) and proposes training strategy for its latent counterpart Latent Brownian Bridge Diffusion Model (LBBDM) tailored for pressure synthesis in lying postures. Experiment results shows proposed method improves physical plausibility and performance over baselines: BBDM with ILS delivers highly detailed maps at higher computational cost and large inference time, whereas LBBDM provides faster inference with competitive performance. Overall, the approach supports non-invasive, vision-based, real-time patient monitoring in clinical environments. △ Less

Submitted 12 February, 2026; v1 submitted 15 December, 2025; originally announced December 2025.

Baryonification: An alternative to hydrodynamical simulations for cosmological studies

Authors: Aurel Schneider, Michael Kovač, Jozef Bucko, Andrina Nicola, Robert Reischke, Sambit K. Giri, Romain Teyssier, Tilman Tröster, Alexandre Refregier, Matthieu Schaller, Joop Schaye

Abstract: …-body simulation and a full hydrodynamical run from the TNG simulation suite. Visual inspection of dark matter, gas, and stellar density fields, along with the integrated pressure map, shows promising agreement. Further work is needed to quantify the accuracy at the level of observables. ▽ More We present an improved baryonification (BFC) model that modifies dark-matter-only $N$-body simulations to generate particle-level outputs for gas, dark matter, and stars. Unlike previous implementations, our approach first splits each simulation particle into separate dark matter and baryonic components, which are then displaced individually using the BFC technique. By applying the hydrostatic and ideal gas equations, we assign pressure and temperature values to individual gas particles. The model is validated against hydrodynamical simulations from the FLAMINGO and TNG suites (which feature varied feedback prescriptions) showing good agreement at the level of density and pressure profiles across a wide range of halo masses. As a further step, we calibrate the BFC model parameters to gas and stellar mass ratio profiles from the hydrodynamical simulations. Based on these calibrations, we baryonify $N$-body simulations and compare the resulting total matter power spectrum suppressions to the ones from the same hydrodynamical simulation. Carrying out this test of the BFC method at each redshift individually, we obtain a 2 percent agreement up to $k=5\,h$/Mpc across all tested feedback scenarios. We also define a reduced, 2+1 parameter BFC model that simultaneously accounts for feedback variations (2 parameters) and redshift evolution (1 parameter). The 2+1 parameter model agrees with the hydrodynamical simulations to better than 2.5 percent over the scales and redshifts relevant for cosmological surveys. Finally, we present a map-level comparison between a baryonified $N$-body simulation and a full hydrodynamical run from the TNG simulation suite. Visual inspection of dark matter, gas, and stellar density fields, along with the integrated pressure map, shows promising agreement. Further work is needed to quantify the accuracy at the level of observables. △ Less

Submitted 10 December, 2025; v1 submitted 10 July, 2025; originally announced July 2025.

Comments: 43 pages, 18 figures, published version

Cuffless Blood Pressure Estimation from Six Wearable Sensor Modalities in Multi-Motion-State Scenarios

Authors: Yiqiao Chen, Fazheng Xu, Zijian Huang, Juchi He, Zhenghui Feng

Abstract: …absolute errors (MAE) of 3.60 mmHg for systolic BP (SBP) and 3.01 mmHg for diastolic BP (DBP). From a clinical perspective, it attains Grade A for SBP, DBP, and mean arterial pressure (MAP) according to the British Hypertension Society (BHS) protocol and meets the numerical criteria of the Association for the Advanceme… ▽ More Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide, and sustained hypertension is an often silent risk factor, making cuffless continuous blood pressure (BP) monitoring with wearable devices important for early screening and long-term management. Most existing cuffless BP estimation methods use only photoplethysmography (PPG) and electrocardiography (ECG) signals, alone or in combination. These models are typically developed under resting or quasi-static conditions and struggle to maintain robust accuracy in multi-motion-state scenarios. In this study, we propose a six-modal BP estimation framework that jointly leverages ECG, multi-channel PPG, attachment pressure, sensor temperature, and triaxial acceleration and angular velocity. Each modality is processed by a lightweight branch encoder, contrastive learning enforces cross-modal semantic alignment, and a mixture-of-experts (MoE) regression head adaptively maps the fused features to BP across motion states. Comprehensive experiments on the public Pulse Transit Time PPG Dataset, which includes running, walking, and sitting data from 22 subjects, show that the proposed method achieves mean absolute errors (MAE) of 3.60 mmHg for systolic BP (SBP) and 3.01 mmHg for diastolic BP (DBP). From a clinical perspective, it attains Grade A for SBP, DBP, and mean arterial pressure (MAP) according to the British Hypertension Society (BHS) protocol and meets the numerical criteria of the Association for the Advancement of Medical Instrumentation (AAMI) standard for mean error (ME) and standard deviation of error (SDE). △ Less

Submitted 1 December, 2025; originally announced December 2025.

Comments: 13 pages, 7 figures

Galaxy cluster temperature maps from joint X-ray and SZ maps with The Three Hundred hydrodynamical simulations

Authors: R. Wicker, M. De Petris, A. Ferragamo, I. Bartalucci, G. Yepes, E. Rasia, R. Adam, W. Cui, F. Mayet, L. Perotto, M. Muñoz-Echeverría

Abstract: …This has enabled the implementation of new methods to infer and map the cluster temperature in individual clusters, using the combination of density maps from X-ray data and pressure maps from millimetre data. In this paper, we present the first systematic validation of this approach on a large sample of synthetic clu… ▽ More Galaxy clusters can be used as powerful cosmological probes, provided one can obtain accurate mass estimates, which requires a precise knowledge of the underlying astrophysics of galaxy clusters. For these purposes, spatially resolved measurements of the thermodynamic properties of intra-cluster medium (ICM), such as density and temperature, are necessary. In particular, temperature estimates are traditionally obtained through spatially resolved X-ray spectroscopy. Such measurements suffer from their sensitivity to the chosen energy calibration, may exhibit inherent biases, and are especially hard to perform at high redshift as they require deep observations. In recent years however, millimetre wavelength data with high spatial resolution, comparable to the one of current X-ray telescopes, have begun to be available. This has enabled the implementation of new methods to infer and map the cluster temperature in individual clusters, using the combination of density maps from X-ray data and pressure maps from millimetre data. In this paper, we present the first systematic validation of this approach on a large sample of synthetic clusters generated in The Three Hundred hydrodynamical simulations. We show that we are able to recover theoretical estimates of the temperature, namely the mass-weighted and spectroscopic-like temperatures, within biases of the order of $\lesssim 1\%$ in the best cases, up to $\sim 10\%$ in average, with scatters of the order of $10\%$. To prepare the application of this approach to observed data, we discuss the modelling of the effective length $l_\mathrm{eff}$, a key quantity necessary for the combination of X-ray and SZ projected data. In particular we provide templates calibrated on simulations for this quantity, and investigate their impact in the recovery of the temperature map, compared to other standard models. △ Less

Submitted 11 November, 2025; originally announced November 2025.

Comments: 13 pages, 12 figures, 2 tables. Submitted to A&A, comments are welcome

Journal ref: A&A 710, A2 (2026)

Real-time Prediction of Urban Sound Propagation with Conditioned Normalizing Flows

Authors: Achim Eckerle, Martin Spitznagel, Janis Keuper

Abstract: …such time-critical, iterative "what-if" studies. We evaluate conditional Normalizing Flows (Full-Glow) for generating for generating standards-compliant urban sound-pressure maps from 2D urban layouts in real time per 256x256 map on a single RTX 4090), enabling interactive exploration directly on commodity hard… ▽ More Accurate and fast urban noise prediction is pivotal for public health and for regulatory workflows in cities, where the Environmental Noise Directive mandates regular strategic noise maps and action plans, often needed in permission workflows, right-of-way allocation, and construction scheduling. Physics-based solvers are too slow for such time-critical, iterative "what-if" studies. We evaluate conditional Normalizing Flows (Full-Glow) for generating for generating standards-compliant urban sound-pressure maps from 2D urban layouts in real time per 256x256 map on a single RTX 4090), enabling interactive exploration directly on commodity hardware. On datasets covering Baseline, Diffraction, and Reflection regimes, our model accelerates map generation by >2000 times over a reference solver while improving NLoS accuracy by up to 24% versus prior deep models; in Baseline NLoS we reach 0.65 dB MAE with high structural fidelity. The model reproduces diffraction and interference patterns and supports instant recomputation under source or geometry changes, making it a practical engine for urban planning, compliance mapping, and operations (e.g., temporary road closures, night-work variance assessments). △ Less

Submitted 6 October, 2025; originally announced October 2025.

Outlier Detection in Plantar Pressure: Human-Centered Comparison of Statistical Parametric Mapping and Explainable Machine Learning

Authors: Carlo Dindorf, Jonas Dully, Steven Simon, Dennis Perchthaler, Stephan Becker, Hannah Ehmann, Kjell Heitmann, Bernd Stetter, Christian Diers, Michael Fröhlich

Abstract: Plantar pressure mapping is essential in clinical diagnostics and sports science, yet large heterogeneous datasets often contain outliers from technical errors or procedural inconsistencies. Statistical Parametric Mapping (SPM) provides interpretable analyses but is sensitive to alignment and its capacity for robust ou… ▽ More Plantar pressure mapping is essential in clinical diagnostics and sports science, yet large heterogeneous datasets often contain outliers from technical errors or procedural inconsistencies. Statistical Parametric Mapping (SPM) provides interpretable analyses but is sensitive to alignment and its capacity for robust outlier detection remains unclear. This study compares an SPM approach with an explainable machine learning (ML) approach to establish transparent quality-control pipelines for plantar pressure datasets. Data from multiple centers were annotated by expert consensus and enriched with synthetic anomalies resulting in 798 valid samples and 2000 outliers. We evaluated (i) a non-parametric, registration-dependent SPM approach and (ii) a convolutional neural network (CNN), explained using SHapley Additive exPlanations (SHAP). Performance was assessed via nested cross-validation; explanation quality via a semantic differential survey with domain experts. The ML model reached high accuracy and outperformed SPM, which misclassified clinically meaningful variations and missed true outliers. Experts perceived both SPM and SHAP explanations as clear, useful, and trustworthy, though SPM was assessed less complex. These findings highlight the complementary potential of SPM and explainable ML as approaches for automated outlier detection in plantar pressure data, and underscore the importance of explainability in translating complex model outputs into interpretable insights that can effectively inform decision-making. △ Less

Submitted 29 September, 2025; v1 submitted 26 September, 2025; originally announced September 2025.

A scaling law for large-deformation contact in soft materials

Authors: Tong Mu, Shizhuo Weng, Changhong Linghu, Ruozhang Li, Jingyi Yu, Zhonghao Xu, Yingjie Fu, Lin Yang, Domenico Campolo, Yanju Liu, Jinsong Leng, K. Jimmy Hsia, Huajian Gao

Abstract: …across diverse shapes and even irregular soft objects such as gummy candies. Leveraging this principle, we design a vision-based tactile sensor that reconstructs real-time pressure maps and enables delicate robotic manipulation of fragile items. By bridging nonlinear contact mechanics with practical sensing, this work… ▽ More Compression of soft bodies is central to biology, materials science, and robotics, yet existing contact theories break down at large deformations. Here, we develop a general framework for soft-body compression by extending the method of dimensionality reduction into the nonlinear regime. Analytical solutions for contact force and radius are derived and validated against finite element simulations and experiments on canonical geometries (cone, hemisphere, cylinder), achieving high accuracy up to 50% compression. From this framework emerges a universal scaling law that unifies the nonlinear force-displacement response across diverse shapes and even irregular soft objects such as gummy candies. Leveraging this principle, we design a vision-based tactile sensor that reconstructs real-time pressure maps and enables delicate robotic manipulation of fragile items. By bridging nonlinear contact mechanics with practical sensing, this work both advances fundamental understanding of large-strain mechanics and opens a route to robust tactile technologies for soft robotics and biomedical applications. △ Less

Submitted 22 September, 2025; originally announced September 2025.

SitLLM: Large Language Models for Sitting Posture Health Understanding via Pressure Sensor Data

Authors: Jian Gao, Fufangchen Zhao, Yiyang Zhang, Danfeng Yan

Abstract: …and personalized health-oriented response generation. SitLLM comprises three key components: (1) a \textit{Gaussian-Robust Sensor Embedding Module} that partitions pressure maps into spatial patches and injects local noise perturbations for robust feature extraction; (2) a \textit{Prompt-Driven Cross-Modal Alignment Mo… ▽ More Poor sitting posture is a critical yet often overlooked factor contributing to long-term musculoskeletal disorders and physiological dysfunctions. Existing sitting posture monitoring systems, although leveraging visual, IMU, or pressure-based modalities, often suffer from coarse-grained recognition and lack the semantic expressiveness necessary for personalized feedback. In this paper, we propose \textbf{SitLLM}, a lightweight multimodal framework that integrates flexible pressure sensing with large language models (LLMs) to enable fine-grained posture understanding and personalized health-oriented response generation. SitLLM comprises three key components: (1) a \textit{Gaussian-Robust Sensor Embedding Module} that partitions pressure maps into spatial patches and injects local noise perturbations for robust feature extraction; (2) a \textit{Prompt-Driven Cross-Modal Alignment Module} that reprograms sensor embeddings into the LLM's semantic space via multi-head cross-attention using the pre-trained vocabulary embeddings; and (3) a \textit{Multi-Context Prompt Module} that fuses feature-level, structure-level, statistical-level, and semantic-level contextual information to guide instruction comprehension. △ Less

Submitted 16 September, 2025; originally announced September 2025.

ChairPose: Pressure-based Chair Morphology Grounded Sitting Pose Estimation through Simulation-Assisted Training

Authors: Lala Shakti Swarup Ray, Vitor Fortes Rey, Bo Zhou, Paul Lukowicz, Sungho Suh

Abstract: …seated pose estimation system that relies solely on pressure sensing and operates independently of chair geometry. ChairPose employs a two stage generative model trained on pressure maps captured from a thin, chair agnostic sensing mattress. Unlike prior approaches, our method explicitly incorporates chair morphology i… ▽ More Prolonged seated activity is increasingly common in modern environments, raising concerns around musculoskeletal health, ergonomics, and the design of responsive interactive systems. Existing posture sensing methods such as vision-based or wearable approaches face limitations including occlusion, privacy concerns, user discomfort, and restricted deployment flexibility. We introduce ChairPose, the first full body, wearable free seated pose estimation system that relies solely on pressure sensing and operates independently of chair geometry. ChairPose employs a two stage generative model trained on pressure maps captured from a thin, chair agnostic sensing mattress. Unlike prior approaches, our method explicitly incorporates chair morphology into the inference process, enabling accurate, occlusion free, and privacy preserving pose estimation. To support generalization across diverse users and chairs, we introduce a physics driven data augmentation pipeline that simulates realistic variations in posture and seating conditions. Evaluated across eight users and four distinct chairs, ChairPose achieves a mean per joint position error of 89.4 mm when both the user and the chair are unseen, demonstrating robust generalization to novel real world generalizability. ChairPose expands the design space for posture aware interactive systems, with potential applications in ergonomics, healthcare, and adaptive user interfaces. △ Less

Submitted 3 August, 2025; originally announced August 2025.

TxP: Reciprocal Generation of Ground Pressure Dynamics and Activity Descriptions for Improving Human Activity Recognition

Authors: Lala Shakti Swarup Ray, Lars Krupp, Vitor Fortes Rey, Bo Zhou, Sungho Suh, Paul Lukowicz

Abstract: …(1) Text2Pressure, converting activity text descriptions into pressure sequences, and (2) Pressure2Text, generating activity descriptions and classifications from dynamic pressure maps. Leveraging pre-trained models like CLIP and LLaMA 2 13B Chat, TxP is trained on our synthetic PressLang dataset, containing over 81,10… ▽ More Sensor-based human activity recognition (HAR) has predominantly focused on Inertial Measurement Units and vision data, often overlooking the capabilities unique to pressure sensors, which capture subtle body dynamics and shifts in the center of mass. Despite their potential for postural and balance-based activities, pressure sensors remain underutilized in the HAR domain due to limited datasets. To bridge this gap, we propose to exploit generative foundation models with pressure-specific HAR techniques. Specifically, we present a bidirectional Text$\times$Pressure model that uses generative foundation models to interpret pressure data as natural language. TxP accomplishes two tasks: (1) Text2Pressure, converting activity text descriptions into pressure sequences, and (2) Pressure2Text, generating activity descriptions and classifications from dynamic pressure maps. Leveraging pre-trained models like CLIP and LLaMA 2 13B Chat, TxP is trained on our synthetic PressLang dataset, containing over 81,100 text-pressure pairs. Validated on real-world data for activities such as yoga and daily tasks, TxP provides novel approaches to data augmentation and classification grounded in atomic actions. This consequently improved HAR performance by up to 12.4\% in macro F1 score compared to the state-of-the-art, advancing pressure-based HAR with broader applications and deeper insights into human movement. △ Less

Submitted 4 May, 2025; originally announced May 2025.

The logarithmic relationship between mean arterial pressure and heart rate

Authors: Maximilian Nguyen

Abstract: The classic heuristic formula for calculating the mean arterial blood pressure (MAP) in the human cardiac cycle using 1/3 of the systolic pressure (P_S) plus 2/3 of the diastolic pressure (P_D) importantly neglects the nonlinear effect of heart rate (HR) on blood pressure. Regression analysis on blood pressure data fro… ▽ More The classic heuristic formula for calculating the mean arterial blood pressure (MAP) in the human cardiac cycle using 1/3 of the systolic pressure (P_S) plus 2/3 of the diastolic pressure (P_D) importantly neglects the nonlinear effect of heart rate (HR) on blood pressure. Regression analysis on blood pressure data from patients experiencing a variety of heart rates captured a logarithmic relationship between mean arterial pressure and heart rate, F_S=0.086 *ln(HR-41.2)+0.079, where F_S is the MAP form factor corresponding to the fraction of time the cardiac cycle spends in ventricular systole. The logarithmic relationship is qualitatively distinct from the linear and exponential relationships previously suggested in the literature yet better aligns with the known human physiology. This heart rate-dependent formula for the mean arterial pressure recapitulates the classic intuition as a special case (HR=60bpm implies F_S =1/3). In addition, the model suggests several other MAP heuristics: HR=45bpm implies F_S = 1/5, HR=50bpm implies F_S = 1/4, HR=175bpm implies F_S = 1/2. A calculator for the MAP can be found at https://mngu265.shinyapps.io/map\_calculator/ △ Less

Submitted 29 March, 2025; originally announced March 2025.

Comments: 6 pages, 2 figures

Convolutional Deep Operator Networks for Learning Nonlinear Focused Ultrasound Wave Propagation in Heterogeneous Spinal Cord Anatomy

Authors: Avisha Kumar, Xuzhe Zhi, Zan Ahmad, Minglang Yin, Amir Manbachi

Abstract: …distort and attenuate the FUS signal. Current approaches rely on computer simulations to solve the governing wave propagation equations and compute patient-specific pressure… ▽ More Focused ultrasound (FUS) therapy is a promising tool for optimally targeted treatment of spinal cord injuries (SCI), offering submillimeter precision to enhance blood flow at injury sites while minimizing impact on surrounding tissues. However, its efficacy is highly sensitive to the placement of the ultrasound source, as the spinal cord's complex geometry and acoustic heterogeneity distort and attenuate the FUS signal. Current approaches rely on computer simulations to solve the governing wave propagation equations and compute patient-specific pressure maps using ultrasound images of the spinal cord anatomy. While accurate, these high-fidelity simulations are computationally intensive, taking up to hours to complete parameter sweeps, which is impractical for real-time surgical decision-making. To address this bottleneck, we propose a convolutional deep operator network (DeepONet) to rapidly predict FUS pressure fields in patient spinal cords. Unlike conventional neural networks, DeepONets are well equipped to approximate the solution operator of the parametric partial differential equations (PDEs) that govern the behavior of FUS waves with varying initial and boundary conditions (i.e., new transducer locations or spinal cord geometries) without requiring extensive simulations. Trained on simulated pressure maps across diverse patient anatomies, this surrogate model achieves real-time predictions with only a 2% loss on the test set, significantly accelerating the modeling of nonlinear physical systems in heterogeneous domains. By facilitating rapid parameter sweeps in surgical settings, this work provides a crucial step toward precise and individualized solutions in neurosurgical treatments. △ Less

Submitted 20 December, 2024; originally announced December 2024.

Comments: Accepted for oral presentation at AAAI Conference on Artificial Intelligence: AI for Accelerating Science and Engineering Workshop 2025

A direct proof of the existence of MME for finite horizon Sinai billiards

Authors: Jérôme Carrand

Abstract: …periodic Lorentz gaz, is a discontinuous map. Assuming finite horizon and another condition we introduce -- namely \emph{negligible singularities} -- we prove that the metric pressure map associated with the billiard map $T$ is upper semi-continuous, as well as the compactness of the set of $T$-invariant measures. In p… ▽ More The Sinai billiard map $T$ on the two-torus, i.e., the periodic Lorentz gaz, is a discontinuous map. Assuming finite horizon and another condition we introduce -- namely \emph{negligible singularities} -- we prove that the metric pressure map associated with the billiard map $T$ is upper semi-continuous, as well as the compactness of the set of $T$-invariant measures. In particular, for the potentials $g \equiv 0$ and $g = -h_{\rm top}(Φ^1) τ$, we recover the recent results of the existence of measures of maximal entropy (MME) for both the billiard map and flow $Φ^t$, due to Baladi and Demers for $T$, jointly with Carrand for $Φ^t$. We prove that the negligible singularities condition is generic among the billiard table with $C^{3+α}$ boundary, with respect to the $C^{3+α}$ topology. For finite horizon Sinai billiards, we provide bounds on the defect of upper semi-continuity of the metric pressure map and on the topological tail entropy. Assuming that singularities are negligible and sparse recurrence hold, we deduce the equidistribution of the periodic orbits with respect to the unique MME. We provide examples of billiard tables satisfying both conditions. △ Less

Submitted 4 December, 2024; v1 submitted 20 March, 2024; originally announced March 2024.

MSC Class: 37C83 (Primary) 37D35; 37A35; 37B40 (Secondary)

Pressure at infinity on countable Markov shifts

Authors: Anibal Velozo

Abstract: …potentials. Additionally, we study the pressure at infinity of suspension flows defined over countable Markov shifts and prove an upper semi-continuity result for the pressure map. ▽ More In this article, we study the pressure at infinity of potentials defined over countable Markov shifts. We establish an upper semi-continuity result concerning the limiting behaviour of the pressure of invariant probability measures, where the escape of mass is controlled by the pressure at infinity. As a consequence, we establish criteria for the existence of equilibrium states and maximizing measures for uniformly continuous potentials. Additionally, we study the pressure at infinity of suspension flows defined over countable Markov shifts and prove an upper semi-continuity result for the pressure map. △ Less

Submitted 21 October, 2024; originally announced October 2024.

Comments: 39 pages

MSC Class: 37D35; 37A35

Journal ref: Ergod. Th. Dynam. Sys. 46 (2026) 1043-1085

All-optical in vivo photoacoustic tomography by adaptive multilayer temporal backpropagation

Authors: Taeil Yoon, Hakseok Ko, Jeongmyo Im, Euiheon Chung, Wonshik Choi, Byeong Ha Lee

Abstract: …the system maps surface displacements over a 10 mm*10 mm area with 0.5 nm sensitivity in 1 second. A temporal backpropagation algorithm reconstructs 3D images from a single pressure map, allowing rapid, depth-selective imaging. With adaptive multilayer backpropagation, the system achieves imaging depths of up to 5 mm,… ▽ More Photoacoustic tomography (PAT) offers high optical contrast with acoustic imaging depth, making it essential for biomedical applications. While many all-optical systems have been developed to address limitations of ultrasound transducers, such as limited spatial sampling and optical path obstructions, measuring surface displacements on rough and dynamic tissues remains challenging. Existing methods often lack sensitivity for in vivo imaging or are complex and time-consuming. Here, we present an all-optical PAT system that enables fast, high-resolution volumetric imaging in live tissues. Using full-field holographic microscopy combined with a soft cover layer and coherent averaging, the system maps surface displacements over a 10 mm*10 mm area with 0.5 nm sensitivity in 1 second. A temporal backpropagation algorithm reconstructs 3D images from a single pressure map, allowing rapid, depth-selective imaging. With adaptive multilayer backpropagation, the system achieves imaging depths of up to 5 mm, with lateral and axial resolutions of 158 micrometer and 92 micrometer as demonstrated through in vivo imaging of mouse vasculature. △ Less

Submitted 10 October, 2024; originally announced October 2024.

BeadSight: An Inexpensive Tactile Sensor Using Hydro-Gel Beads

Authors: Abraham George, Yibo Chen, Atharva Dikshit, Peter Pak, Amir Barati Farimani

Abstract: …is observed using a rear camera. This observation is then passed through a U-net Neural Network to predict the forces acting on the surface of the bead bag, in the form of a pressure… ▽ More In robotic manipulation, tactile sensors are indispensable, especially when dealing with soft objects, objects of varying dimensions, or those out of the robot's direct line of sight. Traditional tactile sensors often grapple with challenges related to cost and durability. To address these issues, our study introduces a novel approach to visuo-tactile sensing with an emphasis on economy and replacablity. Our proposed sensor, BeadSight, uses hydro-gel beads encased in a vinyl bag as an economical, easily replaceable sensing medium. When the sensor makes contact with a surface, the deformation of the hydrogel beads is observed using a rear camera. This observation is then passed through a U-net Neural Network to predict the forces acting on the surface of the bead bag, in the form of a pressure map. Our results show that the sensor can accurately predict these pressure maps, detecting the location and magnitude of forces applied to the surface. These abilities make BeadSight an effective, inexpensive, and easily replaceable tactile sensor, ideal for many robotics applications. △ Less

Submitted 21 May, 2024; originally announced May 2024.

Comments: BeadSight code is available at: https://github.com/Abraham190137/BeadSight 7 pages, 8 figures

The Required Spatial Resolution to Assess Imbalance using Plantar Pressure Mapping

Authors: Kelsey Detels, Shanni Zhou, Harrison Wilson, Jessica Rosendorf, Ghazal Shabestanipour, Elias Ben Mellouk, David Shin, Joseph Schwab, Hamid Ghaednia

Abstract: …particularly for imbalance. Therefore, quantification of imbalance is being studied extensively in recent years. In this study we investigate the feasibility of plantar pressure… ▽ More Roughly 1/3 of adults older than 65 fall each year, resulting in more than 3 million emergency room visits, thousands of deaths, and over $50 Billion in direct costs. The Centers for Disease Control and Prevention (CDC) estimate that 1/3 of falls are preventable with effective mitigation strategies, particularly for imbalance. Therefore, quantification of imbalance is being studied extensively in recent years. In this study we investigate the feasibility of plantar pressure mapping in balance assessment through a healthy human subject study. We used an in-house plantar pressure mapping device with high precision based on Frustrated Total Internal Reflection to measure subjects sway during the Romberg test. Through the measurements obtained from all subjects, we measured the minimum spatial resolution required for plantar pressure mapping devices in assessment of balance. We conclude that most of the current devices in the market lack the requirements for imbalance measurements. △ Less

Submitted 18 April, 2024; v1 submitted 8 January, 2024; originally announced January 2024.

BodyMAP -- Jointly Predicting Body Mesh and 3D Applied Pressure Map for People in Bed

Authors: Abhishek Tandon, Anujraaj Goyal, Henry M. Clever, Zackory Erickson

Abstract: Accurately predicting the 3D human posture and the pressure exerted on the body for people resting in bed, visualized as a body mesh (3D pose & shape) with a 3D pressure… ▽ More Accurately predicting the 3D human posture and the pressure exerted on the body for people resting in bed, visualized as a body mesh (3D pose & shape) with a 3D pressure map, holds significant promise for healthcare applications, particularly, in the prevention of pressure ulcers. Current methods focus on singular facets of the problem -- predicting only 2D/3D poses, generating 2D pressure images, predicting pressure only for certain body regions instead of the full body, or forming indirect approximations to the 3D pressure map. In contrast, we introduce BodyMAP, which jointly predicts the human body mesh and 3D applied pressure map across the entire human body. Our network leverages multiple visual modalities, incorporating both a depth image of a person in bed and its corresponding 2D pressure image acquired from a pressure-sensing mattress. The 3D pressure map is represented as a pressure value at each mesh vertex and thus allows for precise localization of high-pressure regions on the body. Additionally, we present BodyMAP-WS, a new formulation of pressure prediction in which we implicitly learn pressure in 3D by aligning sensed 2D pressure images with a differentiable 2D projection of the predicted 3D pressure maps. In evaluations with real-world human data, our method outperforms the current state-of-the-art technique by 25% on both body mesh and 3D applied pressure map prediction tasks for people in bed. △ Less

Submitted 3 April, 2024; originally announced April 2024.

Comments: Accepted at CVPR 2024 Project Website: https://bodymap3d.github.io/ Code: https://github.com/RCHI-Lab/BodyMAP

Player Pressure Map -- A Novel Representation of Pressure in Soccer for Evaluating Player Performance in Different Game Contexts

Authors: Chaoyi Gu, Jiaming Na, Yisheng Pei, Varuna De Silva

Abstract: …this paper is to leverage both tracking and event data and game footage to capture the pressure experienced by the possession team in a soccer game scene. We propose a player pressure map to represent a given game scene, which lowers the dimension of raw data and still contains rich contextual information. Not only doe… ▽ More In soccer, contextual player performance metrics are invaluable to coaches. For example, the ability to perform under pressure during matches distinguishes the elite from the average. Appropriate pressure metric enables teams to assess players' performance accurately under pressure and design targeted training scenarios to address their weaknesses. The primary objective of this paper is to leverage both tracking and event data and game footage to capture the pressure experienced by the possession team in a soccer game scene. We propose a player pressure map to represent a given game scene, which lowers the dimension of raw data and still contains rich contextual information. Not only does it serve as an effective tool for visualizing and evaluating the pressure on the team and each individual, but it can also be utilized as a backbone for accessing players' performance. Overall, our model provides coaches and analysts with a deeper understanding of players' performance under pressure so that they make data-oriented tactical decisions. △ Less

Submitted 7 March, 2024; v1 submitted 29 January, 2024; originally announced January 2024.

Text me the data: Generating Ground Pressure Sequence from Textual Descriptions for HAR

Authors: Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh, Lars Krupp, Vitor Fortes Rey, Paul Lukowicz

Abstract: …auto regressive strategy allows us to obtain high-quality generated pressure sequences from textual descriptions with the help of discrete latent correlation between text and pressure maps. We achieved comparable performance on the consistency between text and generated motion with an R squared value of 0.722, Masked R… ▽ More In human activity recognition (HAR), the availability of substantial ground truth is necessary for training efficient models. However, acquiring ground pressure data through physical sensors itself can be cost-prohibitive, time-consuming. To address this critical need, we introduce Text-to-Pressure (T2P), a framework designed to generate extensive ground pressure sequences from textual descriptions of human activities using deep learning techniques. We show that the combination of vector quantization of sensor data along with simple text conditioned auto regressive strategy allows us to obtain high-quality generated pressure sequences from textual descriptions with the help of discrete latent correlation between text and pressure maps. We achieved comparable performance on the consistency between text and generated motion with an R squared value of 0.722, Masked R squared value of 0.892, and FID score of 1.83. Additionally, we trained a HAR model with the the synthesized data and evaluated it on pressure dynamics collected by a real pressure sensor which is on par with a model trained on only real data. Combining both real and synthesized training data increases the overall macro F1 score by 5.9 percent. △ Less

Submitted 22 February, 2024; originally announced February 2024.

Comments: PerCom2024WiP

A Self-supervised Pressure Map human keypoint Detection Approch: Optimizing Generalization and Computational Efficiency Across Datasets

Authors: Chengzhang Yu, Xianjun Yang, Wenxia Bao, Shaonan Wang, Zhiming Yao

Abstract: In environments where RGB images are inadequate, pressure… ▽ More In environments where RGB images are inadequate, pressure maps is a viable alternative, garnering scholarly attention. This study introduces a novel self-supervised pressure map keypoint detection (SPMKD) method, addressing the current gap in specialized designs for human keypoint extraction from pressure maps. Central to our contribution is the Encoder-Fuser-Decoder (EFD) model, which is a robust framework that integrates a lightweight encoder for precise human keypoint detection, a fuser for efficient gradient propagation, and a decoder that transforms human keypoints into reconstructed pressure maps. This structure is further enhanced by the Classification-to-Regression Weight Transfer (CRWT) method, which fine-tunes accuracy through initial classification task training. This innovation not only enhances human keypoint generalization without manual annotations but also showcases remarkable efficiency and generalization, evidenced by a reduction to only $5.96\%$ in FLOPs and $1.11\%$ in parameter count compared to the baseline methods. △ Less

Submitted 21 February, 2024; originally announced February 2024.

Comments: 5pages, 6figures

In-Bed Pose Estimation: A Review

Authors: Ziya Ata Yazıcı, Sara Colantonio, Hazım Kemal Ekenel

Abstract: …to estimate in-bed human poses. The unimodal studies generally employ RGB images, whereas the multimodal studies use modalities including RGB, long-wavelength infrared, pressure map, and depth map. Multimodal studies have the advantage of using modalities in addition to RGB that might capture information useful to cope… ▽ More Human pose estimation, the process of identifying joint positions in a person's body from images or videos, represents a widely utilized technology across diverse fields, including healthcare. One such healthcare application involves in-bed pose estimation, where the body pose of an individual lying under a blanket is analyzed. This task, for instance, can be used to monitor a person's sleep behavior and detect symptoms early for potential disease diagnosis in homes and hospitals. Several studies have utilized unimodal and multimodal methods to estimate in-bed human poses. The unimodal studies generally employ RGB images, whereas the multimodal studies use modalities including RGB, long-wavelength infrared, pressure map, and depth map. Multimodal studies have the advantage of using modalities in addition to RGB that might capture information useful to cope with occlusions. Moreover, some multimodal studies exclude RGB and, this way, better suit privacy preservation. To expedite advancements in this domain, we conduct a review of existing datasets and approaches. Our objectives are to show the limitations of the previous studies, current challenges, and provide insights for future works on the in-bed human pose estimation field. △ Less

Submitted 1 February, 2024; originally announced February 2024.

Comments: Accepted at HCCS24 Workshop @ International Conference on Pervasive Computing and Communications (PerCom 2024)

Clinical Applications of Plantar Pressure Measurement

Authors: Kelsey Detels, David Shin, Harrison Wilson, Shanni Zhou, Andrew Chen, Jessica Rosendorf, Atta Taseh, Bardiya Akhbari, Joseph H. Schwab, Hamid Ghaednia

Abstract: …overview of the existing technologies their advantages and disadvantages and provide application examples for each. Moreover, we suggest new areas in orthopaedic that plantar pressure mapping technology can be utilized for increased quality of care. ▽ More Plantar pressure measurements can provide valuable insight into various health characteristics in patients. In this study, we describe different plantar pressure devices available on the market and their clinical relevance. Current devices are either platform-based or wearable and consist of a variety of sensor technologies: resistive, capacitive, piezoelectric, and optical. The measurements collected from any of these sensors can be utilized for a range of clinical applications including patients with diabetes, trauma, deformity and cerebral palsy, stroke, cervical myelopathy, ankle instability, sports injuries, and Parkinsons disease. However, the proper technology should be selected based on the clinical need and the type of tests being performed on the device. In this review we provide the reader with a simple overview of the existing technologies their advantages and disadvantages and provide application examples for each. Moreover, we suggest new areas in orthopaedic that plantar pressure mapping technology can be utilized for increased quality of care. △ Less

Submitted 9 January, 2024; originally announced January 2024.

A Novel Local-Global Feature Fusion Framework for Body-weight Exercise Recognition with Pressure Mapping Sensors

Authors: Davinder Pal Singh, Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh, Paul Lukowicz

Abstract: We present a novel local-global feature fusion framework for body-weight exercise recognition with floor-based dynamic pressure… ▽ More We present a novel local-global feature fusion framework for body-weight exercise recognition with floor-based dynamic pressure maps. One step further from the existing studies using deep neural networks mainly focusing on global feature extraction, the proposed framework aims to combine local and global features using image processing techniques and the YOLO object detection to localize pressure profiles from different body parts and consider physical constraints. The proposed local feature extraction method generates two sets of high-level local features consisting of cropped pressure mapping and numerical features such as angular orientation, location on the mat, and pressure area. In addition, we adopt a knowledge distillation for regularization to preserve the knowledge of the global feature extraction and improve the performance of the exercise recognition. Our experimental results demonstrate a notable 11 percent improvement in F1 score for exercise recognition while preserving label-specific features. △ Less

Submitted 14 September, 2023; originally announced September 2023.

Hybrid-Supervised Deep Learning for Domain Transfer 3D Protoacoustic Image Reconstruction

Authors: Yankun Lang, Zhuoran Jiang, Leshan Sun, Liangzhong Xiang, Lei Ren

Abstract: …strategy for protoacoustic imaging to address the limited view issue. Specifically, in the Recon-stage, a transformer-based network was developed to reconstruct initial pressure… ▽ More Protoacoustic imaging showed great promise in providing real-time 3D dose verification of proton therapy. However, the limited acquisition angle in protoacoustic imaging induces severe artifacts, which significantly impairs its accuracy for dose verification. In this study, we developed a deep learning method with a Recon- Enhance two-stage strategy for protoacoustic imaging to address the limited view issue. Specifically, in the Recon-stage, a transformer-based network was developed to reconstruct initial pressure maps from radiofrequency signals. The network is trained in a hybrid-supervised approach, where it is first trained using supervision by the iteratively reconstructed pressure map and then fine-tuned using transfer learning and self-supervision based on the data fidelity constraint. In the Enhance-stage, a 3D U-net is applied to further enhance the image quality with supervision from the ground truth pressure map. The final protoacoustic images are then converted to dose for proton verification. The results evaluated on a dataset of 126 prostate cancer patients achieved an average RMSE of 0.0292, and an average SSIM of 0.9618, significantly out-performing related start-of-the-art methods. Qualitative results also demonstrated that our approach addressed the limit-view issue with more details reconstructed. Dose verification achieved an average RMSE of 0.018, and an average SSIM of 0.9891. Gamma index evaluation demonstrated a high agreement (94.7% and 95.7% for 1%/3 mm and 1%/5 mm) between the predicted and the ground truth dose maps. Notably, the processing time was reduced to 6 seconds, demonstrating its feasibility for online 3D dose verification for prostate proton therapy. △ Less

Submitted 11 August, 2023; originally announced August 2023.

PressureTransferNet: Human Attribute Guided Dynamic Ground Pressure Profile Transfer using 3D simulated Pressure Maps

Authors: Lala Shakti Swarup Ray, Vitor Fortes Rey, Bo Zhou, Sungho Suh, Paul Lukowicz

Abstract: …pressure profiles for specific activities by leveraging existing pressure data from different individuals. PressureTransferNet is an encoder-decoder model taking a source pressure… ▽ More We propose PressureTransferNet, a novel method for Human Activity Recognition (HAR) using ground pressure information. Our approach generates body-specific dynamic ground pressure profiles for specific activities by leveraging existing pressure data from different individuals. PressureTransferNet is an encoder-decoder model taking a source pressure map and a target human attribute vector as inputs, producing a new pressure map reflecting the target attribute. To train the model, we use a sensor simulation to create a diverse dataset with various human attributes and pressure profiles. Evaluation on a real-world dataset shows its effectiveness in accurately transferring human attributes to ground pressure profiles across different scenarios. We visually confirm the fidelity of the synthesized pressure shapes using a physics-based deep learning model and achieve a binary R-square value of 0.79 on areas with ground contact. Validation through classification with F1 score (0.911$\pm$0.015) on physical pressure mat data demonstrates the correctness of the synthesized pressure maps, making our method valuable for data augmentation, denoising, sensor simulation, and anomaly detection. Applications span sports science, rehabilitation, and bio-mechanics, contributing to the development of HAR systems. △ Less

Submitted 1 August, 2023; originally announced August 2023.

Comments: Activity and Behavior Computing 2023

MassNet: A Deep Learning Approach for Body Weight Extraction from A Single Pressure Image

Authors: Ziyu Wu, Quan Wan, Mingjie Zhao, Yi Ke, Yiran Fang, Zhen Liang, Fangting Xie, Jingyuan Cheng

Abstract: …body weight estimation task are mainly vision-based, using 2D/3D, depth, or infrared images, facing problems in illumination, occlusions, and especially privacy issues. The pressure mapping mattress is a non-invasive and privacy-preserving tool to obtain the pressure distribution image over the bed surface, which stron… ▽ More Body weight, as an essential physiological trait, is of considerable significance in many applications like body management, rehabilitation, and drug dosing for patient-specific treatments. Previous works on the body weight estimation task are mainly vision-based, using 2D/3D, depth, or infrared images, facing problems in illumination, occlusions, and especially privacy issues. The pressure mapping mattress is a non-invasive and privacy-preserving tool to obtain the pressure distribution image over the bed surface, which strongly correlates with the body weight of the lying person. To extract the body weight from this image, we propose a deep learning-based model, including a dual-branch network to extract the deep features and pose features respectively. A contrastive learning module is also combined with the deep-feature branch to help mine the mutual factors across different postures of every single subject. The two groups of features are then concatenated for the body weight regression task. To test the model's performance over different hardware and posture settings, we create a pressure image dataset of 10 subjects and 23 postures, using a self-made pressure-sensing bedsheet. This dataset, which is made public together with this paper, together with a public dataset, are used for the validation. The results show that our model outperforms the state-of-the-art algorithms over both 2 datasets. Our research constitutes an important step toward fully automatic weight estimation in both clinical and at-home practice. Our dataset is available for research purposes at: https://github.com/USTCWzy/MassEstimation. △ Less

Submitted 17 March, 2023; originally announced March 2023.

Journal ref: PerCom 2023

Estimating Pose from Pressure Data for Smart Beds with Deep Image-based Pose Estimators

Authors: Vandad Davoodnia, Saeed Ghorbani, Ali Etemad

Abstract: …or by re-training them on two pressure datasets. We also explore other strategies utilizing a learnable pre-processing domain adaptation step, which transforms the vague pressure… ▽ More In-bed pose estimation has shown value in fields such as hospital patient monitoring, sleep studies, and smart homes. In this paper, we explore different strategies for detecting body pose from highly ambiguous pressure data, with the aid of pre-existing pose estimators. We examine the performance of pre-trained pose estimators by using them either directly or by re-training them on two pressure datasets. We also explore other strategies utilizing a learnable pre-processing domain adaptation step, which transforms the vague pressure maps to a representation closer to the expected input space of common purpose pose estimation modules. Accordingly, we used a fully convolutional network with multiple scales to provide the pose-specific characteristics of the pressure maps to the pre-trained pose estimation module. Our complete analysis of different approaches shows that the combination of learnable pre-processing module along with re-training pre-existing image-based pose estimators on the pressure data is able to overcome issues such as highly vague pressure points to achieve very high pose estimation accuracy. △ Less

Submitted 13 June, 2022; originally announced June 2022.

Comments: The version of record of this article, first published in Applied Intelligence, is available online at Publisher's website https://doi.org/10.1007/s10489-021-02418-y. arXiv admin note: substantial text overlap with arXiv:1908.08919

Report number: 1573-7497

Journal ref: Applied Intelligence (2021): 1-15

Wearable Haptic Device for Individuals with Congenital Absence of Proprioception

Authors: Sreela Kodali, Allison M. Okamura, Thomas C. Bulea, Alexander T. Chesler, Carsten G. Bönnemann

Abstract: …elbow movement to deep pressure applied to the forearm. The prototype applies up to 18 N, includes an embedded force sensor, and is programmable to allow for various angle-to-pressure mappings. Future work includes comparing proprioceptive acuity and movement ability with and without the device in healthy and PIEZO2-LO… ▽ More A rare genetic condition, PIEZO2 loss of function (LOF) is characterized by absence of proprioception and light touch, which makes functional tasks (e.g., walking, manipulation) difficult. There are no pharmacological treatments or assistive technologies available for individuals with PIEZO2-LOF. We propose a sensory substitution device that communicates proprioceptive feedback via detectable haptic stimuli. We created a wearable prototype that maps measurements of elbow movement to deep pressure applied to the forearm. The prototype applies up to 18 N, includes an embedded force sensor, and is programmable to allow for various angle-to-pressure mappings. Future work includes comparing proprioceptive acuity and movement ability with and without the device in healthy and PIEZO2-LOF individuals, developing low-profile devices using soft robotics, providing sensory substitution for multiple joints simultaneously, and encoding additional aspects of joint dynamics. △ Less

Submitted 7 June, 2022; originally announced June 2022.

Metallicity, ionization parameter, and pressure variations of HII regions in the TYPHOON spiral galaxies

Authors: K. Grasha, Q. H. Chen, A. J. Battisti, A. Acharyya, S. Ridolfo, E. Poehler, S. Mably, A. A. Verma, K. L. Hayward, A. Kharbanda, H. Poetrodjojo, M. Seibert, J. A. Rich, B. F. Madore, L. J. Kewley

Abstract: We present a spatially-resolved HII region study of the gas-phase metallicity, ionization parameter, and ISM pressure… ▽ More We present a spatially-resolved HII region study of the gas-phase metallicity, ionization parameter, and ISM pressure maps of 6 local star-forming and face-on spiral galaxies from the TYPHOON program. Self-consistent metallicity, ionization parameter, and pressure maps are calculated simultaneously through an iterative process to provide useful measures of the local chemical abundance and its relation to localized ISM properties. We constrain the presence of azimuthal variations in metallicity by measuring the residual metallicity offset $Δ$(O/H) after subtracting the linear fits to the radial metallicity profiles. We however find weak evidence of azimuthal variations in most of the galaxies, with small (mean 0.03 dex) scatter. The galaxies instead reveal that HII regions with enhanced and reduced abundances are found distributed throughout the disk. While the spiral pattern plays a role in organizing the ISM, it alone does not establish the relatively uniform azimuthal variations we observe. Differences in the metal abundances are more likely driven by the strong correlations with the local physical conditions. We find a strong and positive correlation between the ionization parameter and the local abundances as measured by the relative metallicity offset $Δ$(O/H), indicating a tight relationship between local physical conditions and their localized enrichment of the ISM. Additionally, we demonstrate the impact of unresolved observations on the measured ISM properties by rebinning the datacubes to simulate low-resolution (1 kpc) observations, typical of large IFU surveys. We find that the ionization parameter and ISM pressure diagnostics are impacted by the loss of resolution such that their measured values are larger relative to the measured values on sub-HII region scales. △ Less

Submitted 4 March, 2022; originally announced March 2022.

Comments: 31 pages, 14 figures. Accepted for publication in ApJ

Privacy-Preserving In-Bed Pose Monitoring: A Fusion and Reconstruction Study

Authors: Thisun Dayarathna, Thamidu Muthukumarana, Yasiru Rathnayaka, Simon Denman, Chathura de Silva, Akila Pemasiri, David Ahmedt-Aristizabal

Abstract: …caused by bedding. In this paper we explore the effective use of images from multiple non-visual and privacy-preserving modalities such as depth, long-wave infrared (LWIR) and pressure maps for the task of in-bed pose estimation in two settings. First, we explore the effective fusion of information from different imagi… ▽ More Recently, in-bed human pose estimation has attracted the interest of researchers due to its relevance to a wide range of healthcare applications. Compared to the general problem of human pose estimation, in-bed pose estimation has several inherent challenges, the most prominent being frequent and severe occlusions caused by bedding. In this paper we explore the effective use of images from multiple non-visual and privacy-preserving modalities such as depth, long-wave infrared (LWIR) and pressure maps for the task of in-bed pose estimation in two settings. First, we explore the effective fusion of information from different imaging modalities for better pose estimation. Secondly, we propose a framework that can estimate in-bed pose estimation when visible images are unavailable, and demonstrate the applicability of fusion methods to scenarios where only LWIR images are available. We analyze and demonstrate the effect of fusing features from multiple modalities. For this purpose, we consider four different techniques: 1) Addition, 2) Concatenation, 3) Fusion via learned modal weights, and 4) End-to-end fully trainable approach; with a state-of-the-art pose estimation model. We also evaluate the effect of reconstructing a data-rich modality (i.e., visible modality) from a privacy-preserving modality with data scarcity (i.e., long-wavelength infrared) for in-bed human pose estimation. For reconstruction, we use a conditional generative adversarial network. We conduct ablative studies across different design decisions of our framework. This includes selecting features with different levels of granularity, using different fusion techniques, and varying model parameters. Through extensive evaluations, we demonstrate that our method produces on par or better results compared to the state-of-the-art. △ Less

Submitted 22 February, 2022; originally announced February 2022.

Journal ref: Expert Systems with Applications, Volume 213, Part C, 1 March 2023, 119139

Pressure Eye: In-bed Contact Pressure Estimation via Contact-less Imaging

Authors: Shuangjun Liu, Sarah Ostadabbas

Abstract: …mats. Our PEye network is configured in a dual encoding shared decoding form to fuse visual cues and some relevant physical parameters in order to reconstruct high resolution pressure maps (PMs). We also present a pixel-wise resampling approach based on Naive Bayes assumption to further enhance the PM regression perfor… ▽ More Computer vision has achieved great success in interpreting semantic meanings from images, yet estimating underlying (non-visual) physical properties of an object is often limited to their bulk values rather than reconstructing a dense map. In this work, we present our pressure eye (PEye) approach to estimate contact pressure between a human body and the surface she is lying on with high resolution from vision signals directly. PEye approach could ultimately enable the prediction and early detection of pressure ulcers in bed-bound patients, that currently depends on the use of expensive pressure mats. Our PEye network is configured in a dual encoding shared decoding form to fuse visual cues and some relevant physical parameters in order to reconstruct high resolution pressure maps (PMs). We also present a pixel-wise resampling approach based on Naive Bayes assumption to further enhance the PM regression performance. A percentage of correct sensing (PCS) tailored for sensing estimation accuracy evaluation is also proposed which provides another perspective for performance evaluation under varying error tolerances. We tested our approach via a series of extensive experiments using multimodal sensing technologies to collect data from 102 subjects while lying on a bed. The individual's high resolution contact pressure data could be estimated from their RGB or long wavelength infrared (LWIR) images with 91.8% and 91.2% estimation accuracies in $PCS_{efs0.1}$ criteria, superior to state-of-the-art methods in the related image regression/translation tasks. △ Less

Submitted 27 January, 2022; originally announced January 2022.

BP-Net: Efficient Deep Learning for Continuous Arterial Blood Pressure Estimation using Photoplethysmogram

Authors: Rishi Vardhan K, Vedanth S, Poojah G, Abhishek K, Nitish Kumar M, Vineeth Vijayaraghavan

Abstract: …(PPG) signals. To circumvent this, we present an end-to-end deep learning solution, BP-Net, that uses PPG waveform to estimate Systolic BP (SBP), Mean Average Pressure (MAP), and Diastolic BP (DBP) through intermediate continuous Arterial BP (ABP) waveform. Under the terms of the British Hypertension Society (BHS) stan… ▽ More Blood pressure (BP) is one of the most influential bio-markers for cardiovascular diseases and stroke; therefore, it needs to be regularly monitored to diagnose and prevent any advent of medical complications. Current cuffless approaches to continuous BP monitoring, though non-invasive and unobtrusive, involve explicit feature engineering surrounding fingertip Photoplethysmogram (PPG) signals. To circumvent this, we present an end-to-end deep learning solution, BP-Net, that uses PPG waveform to estimate Systolic BP (SBP), Mean Average Pressure (MAP), and Diastolic BP (DBP) through intermediate continuous Arterial BP (ABP) waveform. Under the terms of the British Hypertension Society (BHS) standard, BP-Net achieves Grade A for DBP and MAP estimation and Grade B for SBP estimation. BP-Net also satisfies Advancement of Medical Instrumentation (AAMI) criteria for DBP and MAP estimation and achieves Mean Absolute Error (MAE) of 5.16 mmHg and 2.89 mmHg for SBP and DBP, respectively. Further, we establish the ubiquitous potential of our approach by deploying BP-Net on a Raspberry Pi 4 device and achieve 4.25 ms inference time for our model to translate the PPG waveform to ABP waveform. △ Less

Submitted 29 November, 2021; originally announced November 2021.

Comments: 6 pages, 2 figures, Accepted at 20th IEEE International Conference on Machine Learning and Applications (ICMLA) 2021

SALT: Sea lice Adaptive Lattice Tracking -- An Unsupervised Approach to Generate an Improved Ocean Model

Authors: Ju An Park, Vikram Voleti, Kathryn E. Thomas, Alexander Wong, Jason L. Deglint

Abstract: …data for Hardangerfjord, Norway. The proposed SALT technique shows promise for enhancing proactive aquaculture management through predictive modelling of sea lice infestation pressure maps in a changing climate. ▽ More Warming oceans due to climate change are leading to increased numbers of ectoparasitic copepods, also known as sea lice, which can cause significant ecological loss to wild salmon populations and major economic loss to aquaculture sites. The main transport mechanism driving the spread of sea lice populations are near-surface ocean currents. Present strategies to estimate the distribution of sea lice larvae are computationally complex and limit full-scale analysis. Motivated to address this challenge, we propose SALT: Sea lice Adaptive Lattice Tracking approach for efficient estimation of sea lice dispersion and distribution in space and time. Specifically, an adaptive spatial mesh is generated by merging nodes in the lattice graph of the Ocean Model based on local ocean properties, thus enabling highly efficient graph representation. SALT demonstrates improved efficiency while maintaining consistent results with the standard method, using near-surface current data for Hardangerfjord, Norway. The proposed SALT technique shows promise for enhancing proactive aquaculture management through predictive modelling of sea lice infestation pressure maps in a changing climate. △ Less

Submitted 24 June, 2021; originally announced June 2021.

Comments: 5 pages, 3 figures, 3 tables

In-bed Pressure-based Pose Estimation using Image Space Representation Learning

Authors: Vandad Davoodnia, Saeed Ghorbani, Ali Etemad

Abstract: …pose estimator with a deep trainable neural network, which pre-processes and prepares the pressure data for subsequent pose estimation. Our model transforms the ambiguous pressure… ▽ More Recent advances in deep pose estimation models have proven to be effective in a wide range of applications such as health monitoring, sports, animations, and robotics. However, pose estimation models fail to generalize when facing images acquired from in-bed pressure sensing systems. In this paper, we address this challenge by presenting a novel end-to-end framework capable of accurately locating body parts from vague pressure data. Our method exploits the idea of equipping an off-the-shelf pose estimator with a deep trainable neural network, which pre-processes and prepares the pressure data for subsequent pose estimation. Our model transforms the ambiguous pressure maps to images containing shapes and structures similar to the common input domain of the pre-existing pose estimation methods. As a result, we show that our model is able to reconstruct unclear body parts, which in turn enables pose estimators to accurately and robustly estimate the pose. We train and test our method on a manually annotated public pressure map dataset using a combination of loss functions. Results confirm the effectiveness of our method by the high visual quality in the generated images and the high pose estimation rates achieved. △ Less

Submitted 18 May, 2021; v1 submitted 20 August, 2019; originally announced August 2019.

Comments: \c{opyright}2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Journal ref: 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 3965-3969). IEEE

Identity and Posture Recognition in Smart Beds with Deep Multitask Learning

Authors: Vandad Davoodnia, Ali Etemad

Abstract: …we propose a robust deep learning model capable of accurately detecting subjects and their sleeping postures using the publicly available data acquired from a commercial pressure mapping system. A combination of loss functions is used to discriminate subjects and their sleeping postures simultaneously. The experimenta… ▽ More Sleep posture analysis is widely used for clinical patient monitoring and sleep studies. Earlier research has revealed that sleep posture highly influences symptoms of diseases such as apnea and pressure ulcers. In this study, we propose a robust deep learning model capable of accurately detecting subjects and their sleeping postures using the publicly available data acquired from a commercial pressure mapping system. A combination of loss functions is used to discriminate subjects and their sleeping postures simultaneously. The experimental results show that our proposed method can identify the patients and their in-bed posture with almost no errors in a 10-fold cross-validation scheme. Furthermore, we show that our network achieves an average accuracy of up to 99% when faced with new subjects in a leave-one-subject-out validation procedure on the three most common sleeping posture categories. We demonstrate the effects of the combined cost function over its parameter and show that learning both tasks simultaneously improves performance significantly. Finally, we evaluate our proposed pipeline by testing it over augmented images of our dataset. The proposed algorithm can ultimately be used in clinical and smart home environments as a complementary tool with other available automated patient monitoring systems. △ Less

Submitted 5 April, 2021; originally announced April 2021.

Comments: \c{opyright} 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Journal ref: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

Multimodal In-bed Pose and Shape Estimation under the Blankets

Authors: Yu Yin, Joseph P. Robinson, Yun Fu

Abstract: …sensors. Specifically, the two most informative modalities (i.e., depth and infrared images) are first fused to generate good initial pose and shape estimation. Then pressure map and RGB images are further fused one by one to refine the result by providing occlusion-invariant information for the covered part, and accur… ▽ More Humans spend vast hours in bed -- about one-third of the lifetime on average. Besides, a human at rest is vital in many healthcare applications. Typically, humans are covered by a blanket when resting, for which we propose a multimodal approach to uncover the subjects so their bodies at rest can be viewed without the occlusion of the blankets above. We propose a pyramid scheme to effectively fuse the different modalities in a way that best leverages the knowledge captured by the multimodal sensors. Specifically, the two most informative modalities (i.e., depth and infrared images) are first fused to generate good initial pose and shape estimation. Then pressure map and RGB images are further fused one by one to refine the result by providing occlusion-invariant information for the covered part, and accurate shape information for the uncovered part, respectively. However, even with multimodal data, the task of detecting human bodies at rest is still very challenging due to the extreme occlusion of bodies. To further reduce the negative effects of the occlusion from blankets, we employ an attention-based reconstruction module to generate uncovered modalities, which are further fused to update current estimation via a cyclic fashion. Extensive experiments validate the superiority of the proposed model over others. △ Less

Submitted 12 December, 2020; originally announced December 2020.

A single oblate spheroid settling in unbounded ambient fluid: a benchmark for simulations in steady and unsteady wake regimes

Authors: Manuel Moriche, Markus Uhlmann, Jan Dušek

Abstract: …steady oblique, vertical periodic and chaotic). The high-fidelity data provided includes particle quantities (statistics in the chaotic case), as well as flow profiles and pressure maps. The reference data can be used as an additional benchmark for other numerical approaches, where a careful grid convergence study for… ▽ More We have performed spectral/spectral-element simulations of a single oblate spheroid with small geometrical aspect ratio settling in an unbounded ambient fluid, for a range of Galileo numbers covering the various regimes of motion (steady vertical, steady oblique, vertical periodic and chaotic). The high-fidelity data provided includes particle quantities (statistics in the chaotic case), as well as flow profiles and pressure maps. The reference data can be used as an additional benchmark for other numerical approaches, where a careful grid convergence study for a specific target parameter point is often useful. We further describe an extension of a specific immersed boundary method (Uhlmann, J. Comput. Phys, 209(2):448--476, 2005) to enable the tracking of non-spherical particles. Finally, the reference cases are computed with this immersed boundary method at various spatial and temporal resolutions, and grid convergence is discussed over the various regimes of spheroidal particle motion. The cross-validation results can serve as a guideline for the design of simulations with the aid of similar non-conforming methods, involving spheroidal particles with Galileo numbers of ${\cal O}(100)$. △ Less

Submitted 15 November, 2020; originally announced November 2020.

Comments: 44 pages, 25 figures. Accepted in International Journal of Multiphase Flow, 2020