Effective Prediction of Gait Phase for Assisted Walking by Means of Gait-Based Adaptive Oscillators

Abstract

How to optimally synchronize exoskeleton powered assistance remains a problem that limits the broad application of such devices. Kinematic change during frequent switching between go and stop, a common and representative activity of daily living (ADL), makes it challenging to predict the gait phase and deliver assistance due to unpredictable movements. Conventional adaptive oscillators (AO) have been verified to be effective in gait phase prediction in steady-state walking. When walking cadences are changed, it usually requires multiple walking strides to synchronize the assistance, causing inaccurate or even unwanted force disturbances that can be dangerous in some cases. To solve this problem, a gait-based AO is proposed in this paper. It has two-level AO systems. The high-level AO learns from last stride and updates the low-level AO, which is designed to estimate the gait phase of the current stride in real time. This approach significantly decreases the time required to learn walking kinematics, while simultaneously improving the accuracy of predictions. Experiments were conducted on seven participants, and the results showed that the proposed gait-based AO can predict the gait phase faster, more accurate, and more stable than the conventional one during non-steady-state walking. Note to Practitioners—This article developed a new gait phase prediction method called gait-based adaptive oscillator. It aims to provide fast and reliable gait phase prediction in situations characterized by frequent transitioning between stop and go (non-steady-state walking). This method has the potential to be an alternative to existing phase prediction methods for robotic exoskeleton assisted walking in daily life, as it requires no model training before use while can synchronize exoskeleton assistance within two strides.