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Related Experiment Video

Updated: Apr 1, 2026

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Video-Based Gait Assessment Using Machine Learning to Classify Age and Sex in Low-Resource Settings: Cross-Sectional

Chanchanok Aramrat1,2, Poppy Alice Carson Mallinson3, Papangkorn Inkaew4

  • 1Department of Family Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.

JMIR Formative Research
|March 30, 2026
PubMed
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Smartphone gait analysis can classify sex and age, offering a low-cost tool for health risk assessment in older adults, especially in resource-limited settings. This method extracts key pose parameters for reliable classification.

Area of Science:

  • Biomedical Engineering
  • Gerontology
  • Machine Learning

Background:

  • Gait assessment is crucial for identifying health risks in older adults but is underutilized in low-resource settings.
  • Smartphone video capture offers a feasible, low-cost method for gait analysis.
  • Sex and age are fundamental biological factors influencing health and aging outcomes.

Purpose of the Study:

  • To determine if machine learning models can classify age and sex using pose parameters from smartphone-based gait videos.
  • To assess the feasibility of using a simple walking protocol with smartphone video for gait signal extraction.

Main Methods:

  • A cross-sectional study involved 155 participants from Thailand and India.
  • Pose estimation using MediaPipe extracted 109 gait features from smartphone videos.
Keywords:
age and sex classificationgait assessmentlow-resource settingmachine learningsmartphone video recording

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  • Elastic-net logistic regression and gradient boosting classifiers analyzed sex and age group classification (≥65 vs <65 years).

    • Main Results:

    • Pose parameters were successfully extracted from 93.5% of recordings.
    • Sex classification achieved an area under the curve (AUC) of ~0.90; age classification achieved an AUC of ~0.70.
    • Classification accuracy depended on feature count, clothing, and pose estimation quality.

    Conclusions:

    • A simple smartphone-based gait protocol can extract meaningful pose parameters for classifying biological features like age and sex.
    • This approach shows potential for disease screening, risk stratification, and health monitoring.
    • Further research is needed to explore its utility in clinical applications.