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Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit.
Hyeonjong Kim1, Ji-Won Kim2,3, Junghyuk Ko1
1Division of Mechanical Engineering, (National) Korea Maritime and Ocean University, Busan 49112, Republic of Korea.
Sensors (Basel, Switzerland)
|July 29, 2023
Summary
This study developed an adaptive gait detection and classification rate (GDCR) control algorithm for Parkinson's disease patients. The new algorithm effectively maintains gait parameters, improving rehabilitation assistance.
Area of Science:
- Biomedical Engineering
- Rehabilitation Technology
- Movement Science
Background:
- Cueing and feedback training are vital for gait maintenance in Parkinson's disease (PD).
- Previous research developed a rehabilitation device for gait detection during the swing phase.
- Optimizing gait detection and classification rate (GDCR) is crucial for effective PD rehabilitation.
Purpose of the Study:
- To analyze factors influencing GDCR in a gait detection algorithm.
- To develop and validate an adaptive GDCR control algorithm for gait rehabilitation.
- To compare the effectiveness of acceleration-based versus angular velocity-based control methods.
Main Methods:
- Collected acceleration and angular velocity data from 25 participants using a novel rehabilitation device.
- Developed an adaptive GDCR control algorithm based on statistical analysis of gait data.
- Tested the algorithm using virtual exercise scenarios with acceleration and angular velocity control methods.
Main Results:
- The acceleration threshold demonstrated superior effectiveness in controlling GDCR compared to the gyroscopic threshold (Spearman correlation -0.9996, p < 0.001).
- The adaptive control algorithm significantly outperformed other methods in maintaining target GDCR (p < 0.001), with an average error of 0.10.
- The developed algorithm exhibits good scalability for future gait analysis applications.
Conclusions:
- An adaptive GDCR control algorithm shows significant promise for enhancing gait rehabilitation in individuals with Parkinson's disease.
- Acceleration-based control is more effective than angular velocity-based control for optimizing GDCR.
- The algorithm's adaptability and scalability support its integration into future gait monitoring and therapeutic systems.
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