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

• Published in: IEEE journal of biomedical and health informatics 2023-03, Vol.27 (3), p.1295-1306
• Main Authors: Wu, Jiaen, Becsek, Barna, Schaer, Alessandro, Maurenbrecher, Henrik, Chatzipirpiridis, George, Ergeneman, Olgac, Pane, Salvador, Torun, Hamdi, Nelson, Bradley J.
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Cluster analysis; Clustering; Computing time; embedded system algorithms; Finite state machines; Footwear; Gait; gait rehabilitation; Legged locomotion; Memory devices; Microcontrollers; Neurological diseases; Performance evaluation; Phase detection; Predictions; Real time; Real-time gait phase detection; Real-time systems; real-world free-living walking; Rehabilitation; Support vector machines; Vector quantization; Walking; Wearable computers; wearable sensors; Wearable technology
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2022.3228329

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.