An IMU-Based Real-Time Gait Detection Method for Intelligent Control of Knee Assistive Devices

Adaptive gait-event detection is essential for intelligent and autonomous control of walking assistive devices. Inertial measurement units (IMUs) based algorithms have been widely adopted but mostly suffered from technical limitations, such as time-delays and/or calculation burden, diverse gait parameters involving different speeds, and other factors in real-world applications. Those can lead to the mismatching of gait between the wearer and the device, further affecting wearing comfort and safety. To reduce the delay and improve the robustness of gait-event detection for knee assistive devices, a lightweight adaptive gait detection method based on an onboard machine learning approach is developed and preliminarily evaluated in this work. The method consists of a two-level algorithm working independently of each other. The first-level algorithm outputs the current detection results at the frequency of 100 Hz, and the second-level algorithm trains and updates the parameters of the gait model for detection every 10 seconds. A total of six events were detected in real-time on a portable Raspberry Pi with two IMUs on thigh and foot, including two specific knee-related events for eliminating detection delay with heel strike and toe off phases in walking. The proposed algorithm exhibits consistently high-performance scores (F1-score of events ≥ 0.92) and early detection capability (≤ 39 ± 22ms) at different walking speeds, in particular, the event prediction for heel strike and toe off were about 77 ± 10ms and 141 ± 10ms in advance, respectively. Given the simple and convenient hardware requirements, this method is especially suitable for intelligent knee assistive device applications.