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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

Walking-Speed-Adaptive Gait Phase Estimation for Wearable Robots.

Sanguk Choi¹, Chanyoung Ko¹, Kyoungchul Kong¹

¹Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.

Sensors (Basel, Switzerland)

|October 14, 2023

Summary

This study presents a new algorithm for precise gait phase estimation, improving accuracy during walking speed changes. The Gait Phase Estimation Module (GPEM) offers smoother, more reliable gait analysis for robotic applications.

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Area of Science:

Robotics
Biomechanics
Biomedical Engineering

Background:

Accurate gait phase estimation is crucial for controlling assistive robots.
Existing methods struggle with dynamic speed changes and real-world variability.

Purpose of the Study:

To introduce a novel Gait Phase Estimation Module (GPEM) with a speed-adaptive online algorithm.
To enable continuous and monotonic gait phase estimation across various walking speeds and dynamic conditions.

Main Methods:

Development of a speed-adaptive online gait phase estimation algorithm.
Integration of the algorithm into the Gait Phase Estimation Module (GPEM).
Experimental validation comparing the proposed method against phase portrait and time-based estimations.

Keywords:

gait phase estimation inertial measurement unit wearable robots

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Last Updated: Jul 13, 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

Home-Based Monitor for Gait and Activity Analysis

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

Main Results:

The proposed method demonstrated smoother, continuous, and repetitive gait phase estimation.
Achieved a 48% reduction in gait phase deviation compared to time-based estimation.
Achieved a 48.29% reduction in gait phase deviation compared to the phase portrait method.

Conclusions:

The GPEM algorithm provides robust and efficient gait phase estimation.
The method is suitable for controlling gait assistive robots without significant computational overhead.
Contributes to advancing gait analysis techniques for robotic applications.