Swing limb detection using a convolutional neural network and a sequential hypothesis test based on foot pressure data during gait initialization in individuals with Parkinson’s disease

<i>Objective</i>. Start hesitation is a key issue for individuals with Parkinson's disease (PD) during gait initiation. Visual cues have proven effective in enhancing gait initiation. When applied to laser-light shoes, swing-limb detection efficiently activates the laser on the side of the stance limb, prompting the opposite swing limb to initiate stepping.<i>Approach</i>. This paper presents the development of two models for this purpose: a convolutional neural network that predicts the swing limb's side using center of pressure data, and a swing onset detection model based on sequential hypothesis test using foot pressure data.<i>Main results</i>. Our findings demonstrate an accuracy rate of 85.4% in predicting the swing limb's side, with 82.4% of swing onsets correctly detected within 0.05 s.<i>Significance</i>. This study demonstrates the efficiency of swing-limb detection based on foot pressures. Future research aims to comprehensively assess the impact of this method on improving gait initiation in individuals with PD.