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Vision-Based Assessment of Skeletal Muscle Decline: Correlating Gait Variance with SPPB Performance.

Zhaozhen Tong¹, Sinan Chen², Yuko Yamaguchi³

¹Graduate School of Economics, Faculty of Economics, Kobe University, 2-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan.

Healthcare (Basel, Switzerland)

|June 26, 2025

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Computer vision quantifies elderly gait, revealing gait variability may indicate better physical vitality and aid fall risk assessment. This technology offers potential for long-term health monitoring.

Area of Science:

Gerontology
Biomedical Engineering
Computer Vision

Background:

Aging population presents increasing health challenges and fall risks.
Traditional fall risk assessments like the Short Physical Performance Battery (SPPB) have limitations.
Computer vision offers potential for objective fall risk assessment but requires refinement.

Purpose of the Study:

Quantify elderly gait movement features using computer vision.
Analyze correlations between gait features, SPPB scores, and test duration.
Develop a solution for long-term monitoring and efficient fall risk assessment.

Main Methods:

Analyzed gait and SPPB data from 19 elderly Japanese subjects.
Utilized Python for data processing, movement analysis, and index construction.

Keywords:

elderly fall risk gait analysis computer non-invasive assessment short physical performance battery vision

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Last Updated: Sep 18, 2025

Methods to Quantify Pharmacologically Induced Alterations in Motor Function in Human Incomplete SCI

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Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

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Published on: November 7, 2014

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Performed correlation analysis and Principal Component Analysis (PCA).

Main Results:

Gait variability negatively correlated with SPPB Test 2 completion time, suggesting better vitality.
PCA identified 'Muscle-Control Reserve' and 'Learning-Fatigue Response' related to test duration.
A comprehensive index showed a non-significant positive correlation with SPPB scores.

Conclusions:

Established correlations between elderly gait features and SPPB performance.
Innovated in data collection and analysis for fall risk assessment.
Recommended expanding sample size, parameters, and deep learning for future studies.