Unsupervised learning for real-time and continuous gait phase detection
Issued Date
2024-11-01
Resource Type
eISSN
19326203
Scopus ID
2-s2.0-85207857511
Journal Title
PLoS ONE
Volume
19
Issue
11
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SCOPUS
Bibliographic Citation
PLoS ONE Vol.19 No.11 (2024)
Suggested Citation
Anopas D., Wongsawat Y., Arnin J. Unsupervised learning for real-time and continuous gait phase detection. PLoS ONE Vol.19 No.11 (2024). doi:10.1371/journal.pone.0312761 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/101950
Title
Unsupervised learning for real-time and continuous gait phase detection
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Abstract
Individuals with lower limb impairment after a stroke or spinal cord injury require rehabilitation, but traditional methods can be challenging for both patients and therapists. Robotic systems have been developed to help; however, they currently cannot detect the continuous gait phase in real time, hindering their effectiveness. To address this limitation, researchers have attempted to develop gait phase detection in general using fuzzy logic algorithms and neural networks. However, there is a paucity of research on real-time and continuous gait phase detection. In light of this gap, we propose an unsupervised learning method for real-time and continuous gait phase detection. This method employs windows of real-time trajectories and a pre-trained model, utilizing trajectories from treadmill walking data, to detect the real-time and continuous gait phase of human on overground locomotion. The neural network model that we have developed exhibits an average time error of less than 11.51 ms across all walking conditions, indicating its suitability for real-time applications. Specifically, the average time error during overground walking at different speeds is 11.20 ms, which is comparatively lower than the average time error observed during treadmill walking, where it is 12.42 ms. By utilizing this method, we can predict the real-time phase using a pre-trained model from treadmill walking data collected with a full motion capture system, which can be performed in a laboratory setting, thereby eliminating the need for overground walking data, which can be more challenging to obtain due to the complexity of the setting.