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Updated: Feb 20, 2026
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An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
Published on: May 26, 2020
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Gait speed estimation using Kalman Filtering on inertial measurement unit data
Summary
This study developed an algorithm using inertial motion sensors to accurately measure gait speed for motor disorder diagnosis and rehabilitation. The algorithm, enhanced with a Kalman Filter, achieved a low average error rate of 0.23 m/h.
Area of Science:
- Biomechanics
- Rehabilitation Engineering
- Wearable Technology
Background:
- Gait speed is a critical indicator for diagnosing motor disorders and assessing rehabilitation progress.
- Accurate gait speed measurement traditionally requires specialized equipment, limiting its accessibility.
- Inertial Motion Sensors (IMDs) offer a portable and potentially more accessible solution for gait analysis.
Purpose of the Study:
- To develop and validate an algorithm for measuring gait speed using inertial motion sensors (tri-axial accelerometer and gyroscope).
- To assess the accuracy and performance of the proposed algorithm across various walking speeds.
- To evaluate the effectiveness of a Kalman Filter in refining gait speed estimations.
Main Methods:
- An algorithm was designed to process data from a tri-axial accelerometer and gyroscope.
- Gait speed was measured using a treadmill at four controlled speeds: 0.5, 1, 2, and 3 miles/hour.
- A Kalman Filter was implemented to tune and improve the accuracy of the calculated gait speed.
- Algorithm performance was evaluated by comparing estimated speeds against actual treadmill speeds using mean square error.
Main Results:
- The proposed algorithm demonstrated reasonable accuracy in estimating gait speed across different treadmill settings.
- The average error rate between the estimated and actual speed was found to be 0.23 m/h.
- Optimal algorithm performance was observed at a treadmill speed of 1 mile/hour.
- The Kalman Filter effectively reduced uncertainty, providing a more precise gait speed approximation.
Conclusions:
- The developed algorithm offers a viable method for gait speed measurement using inertial motion sensors.
- The algorithm shows potential for aiding in the diagnosis of motor disorders and monitoring rehabilitation.
- Kalman Filter integration significantly enhances the reliability and accuracy of gait speed estimation from IMD data.
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