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Related Experiment Video

Updated: Nov 26, 2025

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

Identifying Fatigue Indicators Using Gait Variability Measures: A Longitudinal Study on Elderly Brisk Walking.

Guoxin Zhang¹, Ivy Kwan-Kei Wong¹, Tony Lin-Wei Chen¹

¹Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China.

Sensors (Basel, Switzerland)

|December 10, 2020

Summary

Detecting elderly fatigue during walking using inertial measurement units (IMU) can predict fall risks. A single heel-worn IMU effectively monitored gait variability changes, indicating fatigue and instability in older adults.

Keywords:

brisk walking fatigue gait inertial measurement unit kinematics

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Published on: May 12, 2016

Area of Science:

Biomechanics and Gerontology
Wearable Sensor Technology
Gait Analysis

Background:

Fatigue in the elderly increases fall risk, often assessed via kinematic parameters.
Real-time fatigue detection is crucial for identifying stability issues in older adults during physical activity.

Discussion:

This study investigated gait variability changes using inertial measurement units (IMU) during a 60-minute brisk walk in 18 elderly participants.
A single IMU placed on the heel measured heel motion in three anatomical axes.
Significant differences were observed in acceleration, angular velocity variability, and angle range, particularly in the medial-lateral direction.

Key Insights:

A single heel-mounted IMU can detect changes in kinematic parameters indicative of fatigue in the elderly during prolonged walking.
Significant variations in heel acceleration, angular velocity, and angle range were identified.
These kinematic changes correlate with reported fatigue and potential walking instability.

Outlook:

Findings support the development of fatigue-detecting wearable sensors for the elderly.
This technology can aid in fall prevention strategies by monitoring gait stability.
Further research could explore multi-sensor integration for enhanced fatigue assessment.