Adaptive windowing for gait phase discrimination in Parkinsonian gait using 3-axis acceleration signals.

In order to robustly analyze the gait of Parkinson's disease (PD) patients, a new gait phase discrimination method was developed for analyzing the three-axis accelerations of the ankle during walking. The magnitude of acceleration was compared with the lowpass-filtered signal of itself and pseudo foot-flat phases were determined. Four narrow windows were made sequentially and adaptively from the pseudo foot-flat phases. Each window contained a characteristic peak that discriminated the gait phases. From these windows, the initial contact (IC) point and end contact (EC) point were determined by finding the maximal point in the proximal-distal acceleration. Seven healthy individuals and 17 PD patients were subjected to a walking test on level ground for a distance of 6.5 m with the wearable activity monitoring system (W-AMS). Foot pressure and movement images were simultaneously recorded as references. The ICs and ECs detected by the proposed algorithm were compared with the manually marked events in the foot pressure signals. In healthy subjects, all the ICs and ECs were correctly detected. In the PD group, the detection accuracy was 97.6% for the ICs and 99.4% for the ECs. Based on these results, this novel method holds promise for use in monitoring temporal gait parameters continuously in PD patients, which will subsequently allow for the evaluation of motor fluctuations in PD patients.