Machine Learning Based Abnormal Gait Classification with IMU Considering Joint Impairment
Soree Hwang1,2, Jongman Kim1, Sumin Yang1
1Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea.
Sensors (Basel, Switzerland)
|September 14, 2024
Summary
An inertial measurement unit (IMU) system accurately classified abnormal gaits due to joint impairments (over 91% accuracy). This offers a promising tool for rehabilitation and elderly care, outperforming traditional walkway systems.
Area of Science:
- Biomechanics
- Rehabilitation Engineering
- Medical Technology
Background:
- Gait analysis is crucial for evaluating motor function in rehabilitation and elderly care.
- Existing systems face challenges in accurately identifying specific joint impairments.
Purpose of the Study:
- To develop and optimize an abnormal gait classification algorithm using inertial measurement units (IMUs) and walkway systems.
- To differentiate between normal and impaired gaits, and to identify specific joint disorders (knee and ankle).
Main Methods:
- Ten healthy participants simulated normal, knee-impaired, and ankle-impaired gaits under varying bracing conditions.
- Feature extraction utilized Recursive Feature Elimination with Cross-Validation (RFECV).
- Classification models were built using Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB).
Main Results:
- The IMU-based system achieved over 91% accuracy in classifying three gait types.
- The walkway system achieved less than 77% accuracy, struggling to distinguish between knee and ankle impairments.
- IMU data provided superior discrimination for gait abnormalities.
Conclusions:
- The IMU-based system demonstrates significant potential for accurate gait assessment in individuals with joint impairments.
- This technology could enhance rehabilitation strategies and patient management.
- Further research is recommended for clinical application refinement.
