IoT Assisted Kernel Linear Discriminant Analysis Based Gait Phase Detection Algorithm for Walking With Cognitive Tasks

Abstract

Gait phase detection is an important procedure in the application of many lower limb auxiliary robots. The gait phase detection algorithms that utilize surface electromyography (sEMG) signals have been developed to overcome unnatural walking. However, traditional studies mostly consider precise gait walking events when the subject is focusing only on walking, and the accuracy of traditional algorithms is susceptible to more realistic gait scenarios, such as walking with cognitive tasks. In this paper, the gait phase detection is considered in more realistic and challenging scenarios, in which the subject is walking while performing cognitive tasks. A kernel linear discriminant analysis (LDA)-based nonlinear fusion model is proposed for gait recognition, which can effectively reduce the error caused by cognitive tasks, making it an ideal model for gait phase detection while cognitive tasks are performed in the process of walking. Furthermore, the Internet of Things (IoT) framework is incorporated to reduce the gait phase detection algorithm's process time by offloading the data from local sEMG sensors to the IoT server with powerful computation capability. The experiments have been conducted to validate our proposed algorithms, demonstrating that the boundaries between the stance period and swing period are more blurred when walking with cognitive tasks.