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Frailty-Focused Movement Monitoring: A Single-Camera System Using Joint Angles for Assessing Chair-Based Exercise

Teng Qi¹, Miyuki Iwamoto², Dongeun Choi³

¹Doctoral Program of Advanced Fibro-Science, Kyoto Institute of Technology, Kyoto 606-8585, Japan.

Sensors (Basel, Switzerland)

|July 12, 2025

Summary

Accurately assessing older adults' chair-based exercises (CBEs) is crucial. A single camera using MediaPipe pose estimation effectively distinguished correct from incorrect CBEs, achieving 97.77% accuracy, offering a low-cost solution for home rehabilitation.

Keywords:

chair-based exercise exercise quality monitoring frailty machine learning classification movement correctness older adults sEMG

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Area of Science:

Gerontology and Rehabilitation Engineering
Biomedical Signal Processing
Computer Vision for Healthcare

Background:

Ensuring correct form in chair-based exercises (CBEs) for older adults is vital for rehabilitation outcomes and injury prevention.
Objective and accurate assessment of exercise technique outside clinical settings remains a significant challenge.
Existing methods often lack the practicality for home or community-based monitoring.

Purpose of the Study:

To evaluate the feasibility of using a single-camera system with MediaPipe pose estimation for assessing the correctness of CBEs in older adults.
To compare joint angle kinematics and surface electromyography (sEMG) during proper and incorrect exercise executions.
To develop and validate a machine learning model for classifying movement quality.

Main Methods:

Twenty older adults performed eight CBEs, including both correct and incorrect variations.
A single camera captured joint angle data using MediaPipe pose estimation.
Surface electromyography (sEMG) simultaneously recorded muscle activity.
Statistical analyses compared kinematic and sEMG data; a support vector machine (SVM) was trained for classification.

Main Results:

Correct CBE executions demonstrated distinct joint angle patterns and significantly higher sEMG activity compared to incorrect ones (p < 0.001).
A support vector machine achieved a high classification accuracy of 97.77% across eight exercises after feature optimization.
Joint angle data proved more reliable than sEMG due to high inter-individual variability in muscle activity.

Conclusions:

A single-camera, pose-estimation-based approach can accurately differentiate between correct and incorrect CBEs in older adults.
This technology offers a low-cost, user-friendly solution for real-time feedback in unsupervised rehabilitation settings.
The findings support the potential for enhanced independent exercise adherence and reduced need for constant professional supervision.