An adaptive and robust online method to predict gait events

Abstract

Accurate timing of interventions during the gait cycle are critical for optimal efficacy of assistive devices, e.g., to reduce the metabolic cost of walking. However, timing control generally relies on methods that can neither account for changes in the stride duration over time due to different walking speeds, nor reject isolated abnormal strides, which could be caused by stumbling or obstacle avoidance for example. In order to address these issues, a method, named the Gait Phase Estimator (GPE), is proposed to predict temporal gait events and stride duration. Predictions are based on the weighted forward moving-average of stride duration. Prediction performance in steady-state walking, robustness to stride disturbances, and adaptation to speed changes were evaluated in an experiment with three subjects walking on a treadmill at three different speeds. Results suggest that, on average, the GPE produces better predictions than a predefined estimate. On top, it automatically adapts to changes in speed, while offering the benefit of robustness to irregular strides unlike a conventional moving-average. Thus, the proposed GPE has the potential to improve and greatly simplify the process of obtaining stride duration estimates, which could benefit gait-assistive devices and experimental protocols.