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Updated: Jun 13, 2026
07:24
Home-Based Monitor for Gait and Activity Analysis
Published on: August 8, 2019
Can triaxial accelerometry accurately recognize inclined walking terrains?
Ning Wang1, Stephen J Redmond, Eliathamby Ambikairajah
1School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, N.S.W. 2052, Australia. ning.wang@unsw.edu.au
IEEE Transactions on Bio-Medical Engineering
|May 13, 2010
Summary
Accurate energy expenditure (EE) estimation for walking on different inclines is improved by automatically recognizing gait and surface grade. This study enhances EE accuracy for daily physical activities using accelerometry data.
Area of Science:
- Biomechanics
- Human Movement Analysis
- Wearable Technology
Background:
- Standard accelerometry methods struggle to accurately estimate energy expenditure (EE) during walking on varied inclines.
- Recognizing the walking surface grade is crucial for improving EE estimation accuracy in daily activities.
Purpose of the Study:
- To investigate the benefits of automatic gait analysis for classifying walking surface gradients.
- To enhance the accuracy of energy expenditure (EE) estimates by incorporating terrain inclination data.
Main Methods:
- Collected triaxial accelerometry data from 12 subjects walking on seven different gradient surfaces (0° to ±28.03°).
- Employed step-by-step gait segmentation and heel-strike recognition algorithms.
- Utilized a Gaussian mixture model classifier with 13 selected features for pattern recognition, validated by sixfold cross-validation.
Main Results:
- Achieved high agreement between automated and manual/video-based gait markers.
- Identified 13 optimal features from 57 extracted features to maximize classification accuracy.
- Attained an overall walking pattern-recognition accuracy of 82.46% across seven inclined terrains.
Conclusions:
- Automatic gait analysis, including heel-strike recognition and gait segmentation, significantly improves the ability to classify walking surface gradients.
- This approach enhances the accuracy of daily energy expenditure (EE) estimates by accounting for terrain inclinations.
