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Maximum-Entropy-Rate Selection of Features for Classifying Changes in Knee and Ankle Dynamics During Running
IEEE Journal of Biomedical and Health Informatics
|July 4, 2018
Summary
This study monitored running biomechanics using wearable sensors to detect knee and ankle changes. Machine learning accurately estimated running distance and energy expenditure from joint motion data.
Area of Science:
- Biomechanics
- Wearable technology
- Machine learning
Background:
- Running involves complex lower limb dynamics.
- Monitoring joint motion can reveal biomechanical changes.
- Wearable systems offer a practical approach to track these changes.
Purpose of the Study:
- Investigate deteriorations in knee and ankle dynamics during running.
- Analyze changes in lower limb accelerations using a wearable system.
- Classify joint stiffness and identify relevant biomechanical features.
Main Methods:
- Utilized a wearable musculoskeletal monitoring system.
- Employed a machine-learning technique for joint stiffness classification.
- Developed a maximum-entropy-rate method for feature selection.
Main Results:
- Observed changes in knee and ankle motions during running.
- Successfully classified joint stiffness using the developed system.
- Demonstrated that distance and energy expended can be estimated from motion data.
Conclusions:
- Wearable monitoring systems can effectively track running biomechanics.
- Machine learning enhances the analysis of joint dynamics.
- Estimated running parameters provide insights into performance and potential fatigue.
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