Real-Time Detection of Actual and Early Gait Events During Level-Ground and Ramp Walking

Abstract

In recent studies on gait event detection, the inertial sensor-based approaches were found to be more suitable for long term use compared to force sensors. However, inertial sensor-based algorithms exhibit some time delay. This time delay can affect the stability of the powered prosthetic and assistive devices (PPADs). Hence, several methods were proposed to reduce the same. However, it is impossible to completely eliminate the effect of delay using the existing methods. In this study, we proposed a novel early detection strategy to tackle the issue discussed above. The strategy involved the detection of both actual as well as early gait events. Thus, a total of nine gait events were detected in real-time using inertial sensors and a set of rules. The proposed method was tested for ten control subjects during both level ground as well as ramp walking, and the detection accuracy was found to be equal to 100%. Moreover, the time delay for the detection of actual gait events using the proposed method was found to be similar to that of some state-of-the-art methods and better than a few. The average early detection time was found to be equal to 123 ms that can be utilized to improve the response of PPADs.