Fall Risk and Gait Analysis in Community-Dwelling Older Adults using World-Spaced 3D Human Mesh Recovery
- URL: http://arxiv.org/abs/2604.11961v1
- Date: Mon, 13 Apr 2026 18:52:12 GMT
- Title: Fall Risk and Gait Analysis in Community-Dwelling Older Adults using World-Spaced 3D Human Mesh Recovery
- Abstract summary: We use a 3D Human Mesh Recovery (HMR) model to extract gait parameters from recordings of older adults completing the Timed Up and Go test.<n>We found that ecologically-derived step time was significantly correlated with IMU-based insole measurements.<n>Shorter, more variable step lengths and longer sit-to-stand durations were predicted by higher self-rated fall risk and fear of falling.
- Score: 7.0435068163687475
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Gait assessment is a key clinical indicator of fall risk and overall health in older adults. However, standard clinical practice is largely limited to stopwatch-measured gait speed. We present a pipeline that leverages a 3D Human Mesh Recovery (HMR) model to extract gait parameters from recordings of older adults completing the Timed Up and Go (TUG) test. From videos recorded across different community centers, we extract and analyze spatiotemporal gait parameters, including step time, sit-to-stand duration, and step length. We found that video-derived step time was significantly correlated with IMU-based insole measurements. Using linear mixed effects models, we confirmed that shorter, more variable step lengths and longer sit-to-stand durations were predicted by higher self-rated fall risk and fear of falling. These findings demonstrate that our pipeline can enable accessible and ecologically valid gait analysis in community settings.
Related papers
- TRACE-PCa: Predicting Prostate Cancer Progression from Longitudinal MRI During Active Surveillance [3.0383869408727864]
Active surveillance is the preferred strategy for favorable-risk prostate cancer.<n>Current protocols rely on scheduled repeat biopsies, most of which reveal no progression and are unnecessary.<n>We propose an end-to-end temporal and multimodal model for predicting pathological progression during AS without lesion segmentation.
arXiv Detail & Related papers (2026-07-15T07:00:50Z) - SurgLQA: Scalable Long-Horizon Surgical Video Question Answering [44.46045775428032]
Surgical Video Question Answering (VideoQA) provides a promising paradigm for dynamic intraoperative interpretation.<n>Existing approaches are predominantly restricted to images or short clips.<n>We propose SurgLQA, a unified long-horizon VideoQA framework for scalable surgical reasoning.
arXiv Detail & Related papers (2026-05-18T06:24:07Z) - Towards Athlete Fatigue Assessment from Association Football Videos [53.20057980542362]
This paper builds on state-of-theart Game State Reconstruction methods to extract player trajectories in pitch coordinates.<n>We then construct acceleration--speed profiles from these signals and analyze their behavior as fatigue-related performance indicators.<n>Our results indicate that monocular broadcast videos can recover kinematic patterns that are compatible with A-S-temporal while also revealing sensitivity to trajectory noise, calibration errors, and temporal discontinuities inherent to broadcast footage.
arXiv Detail & Related papers (2026-04-07T09:36:28Z) - Q-PART: Quasi-Periodic Adaptive Regression with Test-time Training for Pediatric Left Ventricular Ejection Fraction Regression [45.69922532213079]
We address the challenge of adaptive pediatric Left Ventricular Ejection Fraction (LVEF) assessment.<n>We propose a novel textbfQuasi-textbfPeriodic textbfAdaptive textbfRegression with textbfTest-time Training (Q-PART) framework.
arXiv Detail & Related papers (2025-03-06T06:24:51Z) - VidSole: A Multimodal Dataset for Joint Kinetics Quantification and Disease Detection with Deep Learning [10.033899828022525]
Internal joint loading is critical for diagnosing gait-related diseases such as knee osteoarthritis.<n>Our novel instrumented insole measures the tri-axial forces and moments across five high-pressure points under the foot.<n>VidSole consists of the forces and moments measured by these insoles along with corresponding RGB video from two viewpoints, 3D body motion capture, and force plate data.
arXiv Detail & Related papers (2025-01-28T15:18:00Z) - Time-to-Event Pretraining for 3D Medical Imaging [44.46415168541444]
We introduce time-to-event pretraining, a pretraining framework for 3D medical imaging models.<n>We use a dataset of 18,945 CT scans (4.2 million 2D images) and time-to-event distributions across thousands of EHR-derived tasks.<n>Our method improves outcome prediction, achieving an average AUROC increase of 23.7% and a 29.4% gain in Harrell's C-index across 8 benchmark tasks.
arXiv Detail & Related papers (2024-11-14T11:08:54Z) - GaitMotion: A Multitask Dataset for Pathological Gait Forecasting [8.305371944195384]
We introduce GaitMotion, a dataset leveraging wearable sensors to capture the patients' real-time movement with pathological gait.
This dataset offers extensive ground-truth labeling for multiple tasks, including step/stride segmentation and step/stride length prediction.
The wearable gait analysis suit captures the gait cycle, pattern, and parameters for both normal and pathological subjects.
arXiv Detail & Related papers (2024-05-09T14:45:02Z) - Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting [46.63798583414426]
Long-term time series forecasting (LTSF) represents a critical frontier in time series analysis.
Our study demonstrates, through both analytical and empirical evidence, that decomposition is key to containing excessive model inflation.
Remarkably, by tailoring decomposition to the intrinsic dynamics of time series data, our proposed model outperforms existing benchmarks.
arXiv Detail & Related papers (2024-01-22T13:15:40Z) - Step length measurement in the wild using FMCW radar [81.9433966586583]
Radar-based step length measurement system for the home is proposed.
method was evaluated in a clinical environment, involving 35 frail older adults, to establish its validity.
arXiv Detail & Related papers (2024-01-03T18:23:30Z) - Pose2Gait: Extracting Gait Features from Monocular Video of Individuals
with Dementia [3.2739089842471136]
Video-based ambient monitoring of gait for older adults with dementia has the potential to detect negative changes in health.
Computer vision-based pose tracking models can process video data automatically and extract joint locations.
These models are not optimized for gait analysis on older adults or clinical populations.
arXiv Detail & Related papers (2023-08-22T14:59:17Z) - Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee
Joint Trajectory from sEMG [24.608475386117426]
This paper proposes a model integrating two gait cycle-inspired learning strategies to mitigate the challenge for predicting human knee joint trajectory.
By learning through separate network entities, the model manages to capture both the common and personalized gait features.
Experimental results indicate that our model could predict knee angles with the average root mean square error (RMSE) of 3.03(0.49) degrees and 50ms ahead of time.
arXiv Detail & Related papers (2023-07-25T02:23:58Z) - Last Iterate Risk Bounds of SGD with Decaying Stepsize for
Overparameterized Linear Regression [122.70478935214128]
gradient descent (SGD) has been demonstrated to generalize well in many deep learning applications.
This paper provides problem-dependent analysis on the last iterate risk bounds of SGD with decaying stepsize.
arXiv Detail & Related papers (2021-10-12T17:49:54Z) - Deep Recurrent Model for Individualized Prediction of Alzheimer's
Disease Progression [4.034948808542701]
Alzheimer's disease (AD) is one of the major causes of dementia and is characterized by slow progression over several years.
We propose a novel computational framework that can predict the phenotypic measurements of MRI biomarkers and trajectories of clinical status.
arXiv Detail & Related papers (2020-05-06T08:08:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.