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Related Experiment Video

Updated: Jan 16, 2026

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults
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Towards Sensor-Based Mobility Assessment for Older Adults: A Multimodal Framework Integrating PoseNet Gait Dynamics

Sinan Chen1,2, Lingqi Kong3, Zhaozhen Tong3

  • 1Center of Mathematical and Data Sciences, Kobe University, 1-1 Rokkodai-cho, Nada, Kobe 657-8501, Japan.

Sensors (Basel, Switzerland)
|September 27, 2025
PubMed
Summary
This summary is machine-generated.

This study links gait changes to body composition in older adults. Higher muscle mass and lower body fat correlate with better mobility, suggesting a new way to monitor elderly health.

Keywords:
composition metricscomputer visiongait analysishealth monitoringmobility and health correlation

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Last Updated: Jan 16, 2026

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Area of Science:

  • Gerontology and Biomedical Engineering
  • Focuses on aging population health and innovative monitoring solutions.

Background:

  • Global population aging necessitates advanced health monitoring for older adults.
  • Traditional methods for mobility assessment are often invasive or lab-based, limiting daily use.
  • Need for non-invasive, continuous monitoring of elderly mobility and health status.

Purpose of the Study:

  • To investigate the correlation between dynamic gait characteristics and static body metrics in elderly individuals.
  • To develop and validate a sensor-based framework for comprehensive elderly health monitoring.
  • To identify key gait and body composition indicators for assessing mobility and health.

Main Methods:

  • Implemented a sensor-based framework combining the Short Physical Performance Battery (SPPB) with PoseNet for dynamic gait analysis.
  • Utilized InBody bioelectrical impedance device for static body composition assessment (SMI, SMM, PBF, VFA, intracellular water).
  • Analyzed dynamic gait metrics like mean directional shift and correlated them with static body metrics using Pearson correlation analysis in 19 elderly participants.

Main Results:

  • Mean directional shift positively correlated with skeletal muscle index (SMI), skeletal muscle mass (SMM), and intracellular water (p<0.01).
  • Negative correlations were observed between mean directional shift and body fat percentage (PBF) and visceral fat area (VFA) (p<0.05).
  • Findings indicate muscle strength enhances movement adaptability, while higher fat mass impedes dynamic mobility.

Conclusions:

  • The multimodal framework effectively integrates dynamic gait and static body metrics for enhanced health monitoring.
  • Mean directional shift is identified as a valuable dynamic health indicator for the elderly.
  • The framework shows potential for early sarcopenia risk detection and comprehensive elderly health assessment.