Silhouette-Based Gaitomics for Frailty Assessment in Older Adults

Laura McDaniel; Ryan Roemmich; Crystal Szczesny; Peter Abadir; Rama Chellappa; Basudha Pal

cs.CVOct 28, 2026

Silhouette-Based Gaitomics for Frailty Assessment in Older Adults

Authors: Laura McDaniel, Ryan Roemmich, Crystal Szczesny, Peter Abadir, Rama Chellappa, Basudha Pal

Organizations: Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA. · Center for Movement Studies, Kennedy Krieger Institute, Baltimore, MD, USA. · Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD, USA. · Division of Geriatrics and Gerontology Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. ·

Abstract

Walking offers a window into physical function that may support interpretable video-based frailty assessment. We developed silhouette-based "gaitomics" to connect frailty predictions with measurable patterns of body configuration and movement. In 66 older adults assessed using the Fried Frailty Phenotype, standardized silhouettes and gait energy images yielded 90 descriptors of shape, temporal variation, motion, and texture. Minimum redundancy maximum relevance selection using the mutual information difference criterion prioritized informative features while limiting redundancy. Each classifier was evaluated using nested stratified cross-validation with five outer and three inner folds at the participant level, with feature ranking and feature-count selection confined to training partitions. Shapley additive explanations examined contributions to held-out predictions. Bilateral silhouette difference led the aggregate feature-selection rankings for three-class classification and both comparisons involving frail participants. Attribution profiles differed across tasks, with texture and consecutive-frame silhouette differences prominent in three-class classification. Among classifier-specific evaluations, CatBoost achieved the highest mean three-class micro-AUC of 0.647 \pm 0.085. Leading pairwise mean micro-AUCs were 0.833 \pm 0.249 for frail versus nonfrail, 0.765 \pm 0.168 for frail versus prefrail, and 0.554 \pm 0.129 for prefrail versus nonfrail classification. These findings connect model predictions to explicit gait characteristics while revealing task-dependent feature priorities and limited separation of prefrail and nonfrail participants. The framework provides an interpretable basis for further investigation, with larger independent studies needed to establish reliability and clinical relevance.

Figures & tables

Characteristic	Nonfrail	Prefrail	Frail	Total
	( $n=25$ )	( $n=24$ )	( $n=17$ )	( $n=66$ )
Age, years	77.4	79.7	79.8	78.8
Sex, $n$ (%)
Female	11 (44.0)	13 (54.2)	15 (88.2)	39 (59.1)
Male	14 (56.0)	11 (45.8)	2 (11.8)	27 (40.9)
Race, $n$ (%)

Table 1: Demographic and clinical characteristics of the study cohort, stratified by Fried frailty status. Values are mean or

n

(%).

Feature family	Source	Count	Description
Shape	ROI sequence	49	These first-order features describe contour geometry and its sequence-level variation using area, perimeter, solidity, compactness, aspect ratio, and Hu moments.
Temporal	ROI sequence	14	These first-order features describe image-derived center-of-mass position and motion, foreground-area variability, consecutive-mask overlap, sequence-derived velocity, and bilateral silhouette differences.
Spatiotemporal	ROI sequence	9	These features describe consecutive-frame change, spatial gradients of that change, and optical flow. Three are first-order and six are second-order.
GEI first-order	GEI	6	These first-order features summarize intensity mean, standard deviation, skewness, excess kurtosis, energy, and entropy.
GEI texture	GEI	12	These second-order features summarize GLCM contrast, dissimilarity, homogeneity, energy, ASM, and correlation using their means and standard deviations across orientations.
Total		90	The candidate catalogue combines direct silhouette measurements with derived spatial and motion descriptors.

Table 2: Gaitomic feature families extracted from the standardized ROI sequence and GEI. The catalogue contains 72 first-order and 18 second-order descriptors under the study’s operational taxonomy. ROI denotes the region of interest. GEI denotes the gait energy image. GLCM denotes the gray-level co-occurrence matrix. ASM denotes angular second moment. Complete feature identifiers are provided in the Supplementary Material.

Rank	Feature	Family	Order	Measure
1	temp_sym_absdiff_mean	Temporal	First-order	Mean absolute bilateral silhouette difference
2	shape_peri_max	Shape	First-order	Maximum contour perimeter over frames
3	temp_area_std	Temporal	First-order	Temporal standard deviation of foreground-pixel area
4	st_flow_fx_mean	Spatiotemporal	Second-order	Mean signed horizontal optical-flow component
5	shape_solidity_mean	Shape	First-order	Temporal mean of contour solidity
6	st_flow_vert_ratio_mean	Spatiotemporal	Second-order	Mean vertical-to-horizontal optical-flow ratio

Table 3: Ten highest-priority features for three-class frailty classification among 66 participants. Features are ordered by their mean position across five mRMR-MID rankings, each computed using only the corresponding outer-training participants and labels. Ties are resolved by feature name. These aggregate rankings summarize feature priority across training partitions and do not represent a single input set shared by all fitted models. Feature-construction categories follow the Methods.

Rank	Feature	Family	Order	Measure
1	temp_sym_absdiff_mean	Temporal	First-order	Mean absolute bilateral silhouette difference
2	st_fdt_sobel_mean	Spatiotemporal	Second-order	Mean spatial Sobel-gradient magnitude of consecutive-frame silhouette differences
3	shape_peri_max	Shape	First-order	Maximum contour perimeter over frames
4	st_flow_fx_mean	Spatiotemporal	Second-order	Mean signed horizontal optical-flow component
5	shape_aspect_std	Shape	First-order	Temporal standard deviation of silhouette aspect ratio
6	temp_area_cv	Temporal	First-order	Relative temporal variation in foreground-pixel area

Table 4: Ten highest-priority features for frail versus nonfrail classification among 42 participants. Features are ordered by mean position across five mRMR-MID rankings computed using only the corresponding outer-training participants and labels. Ties are resolved by feature name. The list summarizes training-partition rankings rather than a fixed input set used in every fold.

Rank	Feature	Family	Order	Measure
1	temp_sym_absdiff_mean	Temporal	First-order	Mean absolute bilateral silhouette difference
2	st_flow_fx_mean	Spatiotemporal	Second-order	Mean signed horizontal optical-flow component
3	gei_tex_ASM_mean	GEI texture	Second-order	GLCM angular second moment (mean across orientations)
4	gei_tex_energy_mean	GEI texture	Second-order	GLCM energy (mean across orientations)
5	shape_hu1_mean	Shape	First-order	Temporal mean of log-transformed Hu descriptor indexed 1
6	shape_peri_max	Shape	First-order	Maximum contour perimeter over frames

Table 5: Ten highest-priority features for frail versus prefrail classification among 41 participants. Features are ordered by mean position across five mRMR-MID rankings computed using only the corresponding outer-training participants and labels. Ties are resolved by feature name. GEI denotes gait energy image, GLCM denotes gray-level co-occurrence matrix, and ASM denotes angular second moment.

Rank	Feature	Family	Order	Measure
1	shape_area_max	Shape	First-order	Maximum contour-based silhouette area over frames
2	temp_area_std	Temporal	First-order	Temporal standard deviation of foreground-pixel area
3	shape_hu6_min	Shape	First-order	Minimum log-transformed Hu descriptor indexed 6 over frames
4	st_flow_vert_ratio_mean	Spatiotemporal	Second-order	Mean vertical-to-horizontal optical-flow ratio
5	shape_hu6_mean	Shape	First-order	Temporal mean of log-transformed Hu descriptor indexed 6
6	st_flow_fx_mean	Spatiotemporal	Second-order	Mean signed horizontal optical-flow component

Table 6: Ten highest-priority features for prefrail versus nonfrail classification among 49 participants. Features are ordered by mean position across five mRMR-MID rankings computed using only the corresponding outer-training participants and labels. Ties are resolved by feature name. The list summarizes training-partition rankings rather than a fixed input set used in every fold.

Comparison	Rank	Model	$K$ by outer fold	$\mu$ AUC	$\kappa$
3-class (F/PF/NF) ( $n=66$ )	1	CatBoost	10, 20, 40, 30, 10	$0.647\pm 0.085$	$0.265\pm 0.117$
	2	Linear SVM	10, 10, 10, 10, 20	$0.640\pm 0.119$	$0.355\pm 0.297$
	3	Random Forest	30, 10, 10, 60, 10	$0.611\pm 0.059$	$0.135\pm 0.283$
	4	XGBoost	40, 10, 20, 50, 10	$0.591\pm 0.099$	$0.193\pm 0.294$
	5	LightGBM	10, 10, 10, 10, 10	$0.548\pm 0.055$	$0.035\pm 0.153$
F vs NF ( $n=42$ )	1	Linear SVM	10, 80, 10, 10, 10	$0.833\pm 0.249$	$0.608\pm 0.363$

Table 7: Classifier-specific nested cross-validation using five outer and three inner stratified folds at the participant level. Within each classifier and outer fold,

K

maximized mean inner-validation

\mu

AUC over

K\in\{10,20,\ldots,90\}

, with ties resolved by smaller

K

. mRMR-MID was fitted on inner-training partitions for selection and refitted on the complete outer-training partition before final model fitting. The feature-count column lists

K

for outer folds one through five. Metrics are mean

\pm

standard deviation across five held-out outer-test folds. Rows are ordered descriptively by mean outer-test

\mu

AUC, with no additional distinction implied for equal displayed values. All classifiers were unweighted. F denotes frail, PF denotes prefrail, and NF denotes nonfrail. Cohen’s

\kappa

used quadratic weighting for three-class classification and no weighting for binary classification.

Figure 1: SHAP feature importance for the classifier-specific three-class CatBoost analysis. Inner-validation selection yielded 10, 20, 40, 30, and 10 features across outer folds one through five. The corresponding mean outer-test $\mu$ AUC was $0.647\pm 0.085$ . Bars show pooled mean absolute contributions from held-out outer-test participants explained using their corresponding outer-training models. The ten highest-importance features are displayed, while explanations retain each fold’s complete fitted input set. Larger values indicate greater average contribution magnitude and do not indicate contribution direction.

Figure 2: SHAP feature importance for the leading classifier-specific pairwise summaries. The upper panel shows frail versus nonfrail linear SVM, with outer-fold feature counts of 10, 80, 10, 10, and 10 and mean outer-test $\mu$ AUC $0.833\pm 0.249$ . The middle panel shows frail versus prefrail linear SVM, with feature counts of 10, 60, 10, 10, and 40 and $\mu$ AUC $0.765\pm 0.168$ . The lower panel shows prefrail versus nonfrail Random Forest, with feature counts of 40, 30, 80, 20, and 10 and $\mu$ AUC $0.554\pm 0.129$ . Bars show pooled mean absolute contributions from held-out outer-test participants. Each panel displays ten features, while explanations use each fold’s complete fitted input set. Panels have separate horizontal scales, and magnitudes should be interpreted within the corresponding model output scale.

Frequency	Feature	Measure	Comparisons
4/4	st_flow_fx_mean	Mean signed horizontal optical-flow component	Three-class, F vs NF, F vs PF, PF vs NF
3/4	shape_peri_max	Maximum contour perimeter over frames	Three-class, F vs NF, F vs PF
3/4	shape_solidity_mean	Temporal mean of contour solidity	Three-class, F vs NF, PF vs NF
3/4	st_fdt_sobel_mean	Mean spatial Sobel-gradient magnitude of consecutive-frame silhouette differences	Three-class, F vs NF, PF vs NF
3/4	st_flow_vert_ratio_mean	Mean vertical-to-horizontal optical-flow ratio	Three-class, F vs PF, PF vs NF
3/4	temp_sym_absdiff_mean	Mean absolute bilateral silhouette difference	Three-class, F vs NF, F vs PF

Table 8: Features appearing in the aggregate top-ten mRMR-MID lists of at least two tasks. Each task-specific list is based on mean feature position across five outer-training rankings. Frequency counts membership across the four task-specific lists and is not a fold-selection frequency. All ten recurrent features are shown. Alphabetical ordering within a frequency group does not imply an additional importance ranking. F denotes frail, PF denotes prefrail, and NF denotes nonfrail.

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Gait Analysis Transfer Learning

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Frailty is a condition in aging medicine characterized by diminished physiological reserve and increased vulnerability to stressors. However, frailty assessment remains subjective, heterogeneous, and difficult to scale in clinical practice. Gait is a sensitive marker of biological aging, capturing multisystem decline before overt disability. Yet the application of modern computer vision to gait-based frailty assessment has been limited by small, imbalanced datasets and a lack of clinically representative benchmarks. In this work, we introduce a publicly available silhouette-based frailty gait dataset collected in a clinically realistic setting, spanning the full frailty spectrum and including older adults who use walking aids. Using this dataset, we evaluate how pretrained gait recognition models can be adapted for frailty classification under limited data conditions. We study both convolutional and hybrid attention-based architectures and show that predictive performance depends primarily on how pretrained representations are transferred rather than architectural complexity alone. Across models, selectively freezing low-level gait representations while allowing higher-level features to adapt yields more stable and generalizable performance than either full fine-tuning or rigid freezing. Conservative handling of class imbalance further improves training stability, and combining complementary learning objectives enhances discrimination between clinically adjacent frailty states. Interpretability analyses reveal consistent model attention to lower-limb and pelvic regions, aligning with established biomechanical correlates of frailty. Together, these findings establish gait-based representation learning as a scalable, non-invasive, and interpretable framework for frailty assessment and support the integration of modern biometric modeling approaches into aging research and clinical practice.

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