Gait analysis for patients with Alzheimer'S disease using a triaxial accelerometer

Abstract

This paper presents an inertial-sensor-based wearable device and its associated stride detection algorithm to analyze gait information for patients with Alzheimer's disease (AD). The wearable gait analysis device is composed of a triaxial accelerometer, a microcontroller, and an RF wireless transmission module. To validate the effectiveness of the proposed device and algorithm, nine AD patients and three healthy controls were recruited to participate a gait analysis experiment. They were asked to mount the device on their foot and walk along a straight line of 40 meters at normal speed. The stride detection algorithm, consisting of procedures of data collection, signal preprocessing, and stride detection, has been developed for acquiring gait feature information from acceleration signals. The advantages of this wearable gait analysis device include the following: 1) It can be used anywhere without any external device, and 2) the stride detection algorithm can acquire gait feature information from acceleration signals automatically and effectively. Experimental results show that the AD patients exhibited a significantly shorter mean stride length and slower mean gait speed than those of the healthy controls. No significant differences in mean stride frequency and mean cadence were observed in the two groups. The variability in the percentage of the stance phase of the AD patients was slightly greater than that of the healthy controls. Based on the above results and discussions with physicians, we conclude that the proposed wearable gait analysis device is a promising tool for automatically analyzing gait information which can serve as indicators for early diagnosis of AD.