Fall Risk and Gait Analysis in Community-Dwelling Older Adults using World-Spaced 3D Human Mesh Recovery

Chitra Banarjee; Patrick Kwon; Ania Lipat; Rui Xie; Chen Chen; Ladda Thiamwong

cs.CVApr 13, 2026

Fall Risk and Gait Analysis in Community-Dwelling Older Adults using World-Spaced 3D Human Mesh Recovery

Authors: Chitra Banarjee, Patrick Kwon, Ania Lipat, Rui Xie, Chen Chen, Ladda Thiamwong

Organizations: College of Medicine, UCF · Institute of AI, UCF · College of Nursing, UCF · College of Sciences, UCF

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.

Figures & tables

Figure 1: Schematic of Timed Up and Go (TUG) test set up

Variable	Assessment	Significance/Interpretation
Self-Rated Fall Risk	Centers for Disease Control STEADI Score	12-item questionnaire quantifying self-rated fall risk, summed to produce the STEADI score, with higher scores indicating higher risk. Validated as indicator of fall risk [ 4 , 43 ] .
Fear of Falling (FoF)	Short Falls Efficacy Scale-International (FES-I) Score	7-item questionnaire evaluating FoF, with higher scores indicating higher FoF. Validated as indicator of fall risk [ 13 , 25 , 63 ] .
Postural Sway	BTrackS Balance Test Score	Static balance assessment in which participants stand on a balance plate for 20 seconds three times to evaluate postural sway, with higher measures indicating worse static balance. Validated as indicator of fall risk [ 31 , 45 , 19 ] .

Table 1: The measures in this table were completed by the participants to provide information about subjective and objective fall risk, specifically self-rated fall risk, fear of falling, and postural sway.

Figure 2: The overall pipeline of our study. Basing on the world trajectory extraction results from the TUG test using GVHMR [ 53 ] (Sec. 3.2), we analyze the gait factors (Sec. 3.3) and conduct statistical analysis (Sec. 3.4).

Component	Indicator
$\dot{y}_{\mathrm{hip}}$	Vertical hip trajectory indicates initiation of STS, weighted the highest.
$\dot{z}_{\mathrm{shoulder}}$	Anterior translation of the upper trunk during forward flexion; reliably marks the preparatory lean phase that precedes vertical rise.
$\dot{\theta}_{\mathrm{trunk}}$	Angular trunk flexion–extension is noisier and more sensitive to small movements, thus assigned a lower weight.
Hip Line Signal	Alternating signed extrema in hip-line velocity reflect rapid angular deceleration and reversal of pelvic rotation during turning.

Table 2: Components of signals for segmentation of TUG subtask (sit-to-stand, turns, stand-to-sit).

Figure 3: Automated Subtask Segmentation for three trials. Solid line refers to the hip line velocity and dashed line refers to sit-to-stand and stand-to-sit (STS) signal.

	STEADI Score
	Estimate (CI)	$p$
STS Duration	$1.23$ $(0.45,2.01)$	$\mathbf{0.002}$
Step Length (SL)	$-1.36$ $(-2.03,-0.68)$	$\mathbf{<0.001}$
SL Variability	$-19.62$ $(-30.44,-8.80)$	$\mathbf{<0.001}$
	Short FES-I Score
	Estimate (CI)	$p$

Table 3: Linear mixed effect model results of STEADI Score (self-rated fall risk) and short FES-I (fear of falling). ST refers to the video-derived step time. Estimate refers to the

\beta

coefficient, CI = confidence interval,

p

p

-value.

Figure 4: Scatterplots of mean step length (SL), SL variability, and sit-to-stand and stand-to-sit (STS) duration with fall risk factors (STEADI: self-rated fall risk, Short FES-I: fear of falling).

Variable	Mean $\pm$ Standard Deviation
Age	$74.2\pm 7.81$ years
STEADI Score	$2.95\pm 2.83$
Short FES-I Score	$9.40\pm 3.02$
BTrackS Balance Score	$29.3\pm 11.2$ cm

Table 4: Population characteristics.

Predictor	Estimate	CI	p
(Intercept)	296.47	[160.02, 432.92]	$\mathbf{<0.001}$
ste8	-0.14	[-5.34, 5.05]	0.956
age	-2.79	[-4.73, 0.86]	$\mathbf{0.005}$

Table 5: Step Width Variability

Panel of Figure 5 — Figure 5: XSENSOR Insole and IMU System

Figure 6: Snapshots of research assistant completing the TUG (left), with the camera-centric view overlay of the HMR (middle), and the global-centric view (right).

Figure 7: Detection of heel strike using peak detection, according to the detected joints using GVHMR.

	Model 1			Model 2			Model 3
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	49.74	[32.01, 67.46]	$\mathbf{<0.001}$	65.08	[48.45, 81.72]	$\mathbf{<0.001}$	65.69	[47.95, 83.44]	$\mathbf{<0.001}$
STEADI	-1.36	[-2.03, -0.68]	$\mathbf{<0.001}$	—	—	—	—	—	—
Age	-0.00	[-0.25, 0.25]	0.988	-0.14	[-0.37, 0.10]	0.264	-0.23	[-0.48, -0.01]	0.065
Short FES-I	—	—	—	-1.04	[-1.65, -0.43]	$\mathbf{0.001}$	—	—	—
BTrackS	—	—	—	—	—	—	-0.11	[-0.27, -0.05]	0.176

Table 6: Step Length (SL) Analysis

	Model 1			Model 2			Model 3
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	296.47	[160.02, 432.92]	$\mathbf{<0.001}$	301.74	[182.35, 421.13]	$\mathbf{<0.001}$	302.56	[182.97, 422.14]	$\mathbf{<0.001}$
STEADI	-0.14	[-5.34, 5.05]	0.956	—	—	—	—	—	—
Age	-2.79	[-4.73, -0.86]	$\mathbf{0.005}$	-2.72	[-4.43, -1.02]	$\mathbf{0.002}$	-2.95	[-4.61, -1.29]	$\mathbf{0.001}$
Short FES-I	—	—	—	-1.24	[-5.61, 3.13]	0.577	—	—	—
BTrackS	—	—	—	—	—	—	0.14	[-0.93, 1.22]	0.794

Table 7: SW Variability Analysis

	Model 1			Model 2			Model 3
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	393.99	[110.09, 677.89]	$\mathbf{0.007}$	594.80	[330.99, 858.61]	$\mathbf{<0.001}$	602.10	[330.27, 873.93]	$\mathbf{<0.001}$
STEADI	-19.62	[-30.44, -8.80]	$\mathbf{<0.001}$	—	—	—	—	—	—
Age	-0.59	[-4.61, 3.43]	0.772	-2.50	[-6.27, 1.27]	0.193	-3.44	[-7.21, 0.34]	0.074
Short FES-I	—	—	—	-12.76	[-22.43, -3.09]	$\mathbf{0.010}$	—	—	—
BTrackS Balance Score	—	—	—	—	—	—	-1.97	[-4.42, 0.47]	0.113

Table 8: SL Variability Analysis

	Model 1			Model 2			Model 3
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	26.82	[5.86, 47.79]	$\mathbf{0.012}$	13.39	[-7.16, 33.95]	0.200	13.69	[-7.46, 34.83]	0.203
STEADI	1.23	[0.45, 2.01]	$\mathbf{0.002}$	—	—	—	—	—	—
Age	0.21	[-0.08, 0.51]	0.157	0.36	[0.07, 0.65]	$\mathbf{0.016}$	0.44	[0.15, 0.74]	$\mathbf{0.003}$
Short FES-I	—	—	—	0.71	[-0.01, 1.42]	0.052	—	—	—
BTrackS Balance Score	—	—	—	—	—	—	0.00	[-0.19, 0.19]	0.989

Table 9: Sit-to-Stand and Stand-to-Sit (STS) Duration Analysis

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Feb 24, 2026J/K move · Enter open · S save

Automating Timed Up and Go Phase Segmentation and Gait Analysis via the tugturn Markerless 3D Pipeline

Abel Gonçalves Chinaglia, Guilherme Manna Cesar, Paulo Roberto Pereira Santiago

Biomechanics and Motor Control Laboratory, School of Physical Education and Sport of Ribeirao Preto, University of Sao Paulo, Brazil · Graduate Program in Rehabilitation and Functional Performance, Ribeirao Preto Medical School, University of Sao Paulo, Brazil · Department of Physical Therapy, Brooks College of Health, University of North Florida, USA

Gait Analysis Markerless Motion Capture

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Instrumented Timed Up and Go (TUG) analysis can support clinical and research decision-making, but robust and reproducible markerless pipelines are still limited. We present tugturn.py, a Python-based workflow for 3D markerless TUG processing that combines phase segmentation, gait-event detection, spatiotemporal metrics, intersegmental coordination, and dynamic stability analysis. The pipeline uses spatial thresholds to segment each trial into stand, first gait, turning, second gait, and sit phases, and applies a relative-distance strategy to detect heel-strike and toe-off events within valid gait windows. In addition to conventional kinematics, tugturn provides Vector Coding outputs and Extrapolated Center of Mass (XCoM)-based metrics. The software is configured through TOML files and produces reproducible artifacts, including HTML reports, CSV tables, and quality-assurance visual outputs. A complete runnable example is provided with test data and command-line instructions. This manuscript describes the implementation, outputs, and reproducibility workflow of tugturn as a focused software contribution for markerless biomechanical TUG analysis.

Abstract

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