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Related Experiment Video

Updated: Mar 9, 2026

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
08:56

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

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Estimation of Temporal Gait Parameters Using a Wearable Microphone-Sensor-Based System.

Cheng Wang1,2,3, Xiangdong Wang4,5, Zhou Long6,7,8

  • 1Pervasive Computing Research Center, Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), Beijing 100190, China. wangcheng01@ict.ac.cn.

Sensors (Basel, Switzerland)
|December 22, 2016
PubMed
Summary

This study introduces a novel wearable system using microphone sensors for gait analysis, achieving high accuracy in detecting footfalls and gait events. This innovative approach provides a low-cost, wireless method for estimating key temporal gait parameters.

Keywords:
footstep soundgait analysismicrophone sensortemporal parameter estimationwearable device

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Area of Science:

  • Biomechanics
  • Wearable Technology
  • Signal Processing

Background:

  • Existing wearable gait analysis predominantly relies on inertial sensors.
  • There is a need for low-cost, wireless, and accessible gait analysis solutions.
  • Footstep sound signals offer a novel data source for gait analysis.

Purpose of the Study:

  • To propose and validate a novel, low-cost, wireless wearable gait analysis system utilizing microphone sensors.
  • To develop and assess an algorithm for estimating temporal gait parameters from footstep sound signals.
  • To demonstrate the system's effectiveness compared to traditional methods.

Main Methods:

  • Development of a wearable system with microphone sensors to capture footstep sounds.
  • Implementation of a three-stage algorithm: footstep detection, heel-strike/toe-on event detection, and temporal parameter calculation.
  • Fusion of sound signals from both feet for comprehensive analysis.
  • Experimental validation with 15 healthy subjects and 1732 steps.

Main Results:

  • Achieved an average F1-measure of 0.955 for footstep detection.
  • Reached an average accuracy rate of 94.52% for heel-strike detection and 94.25% for toe-on detection.
  • Calculated nine temporal gait parameters consistent with normal values and labeled data.

Conclusions:

  • The proposed microphone-based wearable system is effective for low-cost, wireless gait analysis.
  • The developed algorithm accurately estimates temporal gait parameters using footstep sound signals.
  • This system represents a significant advancement in wearable gait analysis technology.