A Modified Method of Wearable Gait Analysis for Stroke Patients Based on the Peak Width Threshold and Phase Re-Segmentation

Wearable gait analysis technology, offering flexibility and cost-effectiveness, holds significant potential for quantifying the gait functions of stroke patients. It remains a key challenge that how to further reduce cumulative errors by using wearable technology. An algorithm for re-segmenting the gait phase was proposed to improve the precision of zero-velocity detection and gait parameters. The adaptive threshold method was used to separate the stationary phase and the nonstationary phase of the gait cycle. Next, an efficient method called the peak width threshold (PWT) for phase re-segmentation was developed to obtain effective peaks from acceleration and angular velocity data, and the peaks which were incorrectly segmented would be adjusted between the stationary and nonstationary phase. The precision of feet position detection was improved by using zero update potential and timing (ZUPT) algorithm and dual-foot constraints. Finally, four typical gait parameters were calculated from feet position and phase information. The results demonstrated that the wearable inertial sensors system displays a good agreement compared to the foot pressure-based gait analysis system, and the mean absolute errors (MAEs) of the stride length, step length, stride velocity, and cadence are $1.77~\pm ~1.22$ cm, $2.40~\pm ~1.83$ cm, $0.02~\pm ~0.01$ m/s, and $1.16~\pm ~1.37$ steps/min, respectively. This proposed method improves the accuracy of gait phase and feet position detection, which is helpful to objectively evaluate the gait functions of stroke patients.