Real-Time Gait Phase Detection on Wearable Devices for Real-World Free-Living Gait

Jiaen Wu*, Barna Becsek, Alessandro Schaer, Henrik Maurenbrecher, George Chatzipirpiridis, Olgac Ergeneman, Salvador Pane, Hamdi Torun, Bradley J. Nelson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Detecting gait phases with wearables unobtrusively and reliably in real-time is important for clinical gait rehabilitation and early diagnosis of neurological diseases. Due to hardware limitations of microcontrollers in wearable devices (e.g., memory and computation power), reliable real-time gait phase detection on the microcontrollers remains a challenge, especially for long-term real-world free-living gait. In this work, a novel algorithm based on a reduced support vector machine (RSVM) and a finite state machine (FSM) is developed to address this. The RSVM is developed by exploiting the cascaded K-means clustering to reduce the model size and computation time of a standard SVM by 88% and a factor of 36, with only minor degradation in gait phase prediction accuracy of around 4%. For each gait phase prediction from the RSVM, the FSM is designed to validate the prediction and correct misclassifications. The developed algorithm is implemented on a microcontroller of a wearable device and its real-time (on the fly) classification performance is evaluated by twenty healthy subjects walking along a predefined real-world route with uncontrolled free-living gait. It shows a promising real-time performance with an accuracy of 91.51%, a sensitivity of 91.70%, and a specificity of 95.77%. The algorithm also demonstrates its robustness with varying walking conditions.
Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Early online date12 Dec 2022
DOIs
Publication statusE-pub ahead of print - 12 Dec 2022

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