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

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 realworld 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.

Novel Real-time In-step Gait Anomaly Detection Algorithms

“…Existing real-time algorithms are used to detect gait events such as heel-strike and toe-off in healthy young and elderly persons, stroke patients, and patients with Parkinson's disease [24], [25], [26], as well as patients with other impairments [27], [28], by means of e.g. recurrent neural networks (RNN), heuristics, thresholds, support vector machine (SVM), reduced support vector machine (RSVM), finite state machine (FSM) algorithms.…”