Autonomous detection of different walking tasks using end point foot trajectory vertical displacement data

Abstract

Identifying different activities during walking is a key requirement for ubiquitous gait monitoring, particularly when engineering new falls prevention solutions. In this study, 5 healthy young individuals (aged 26 ± 2 years old) completed 6 different tasks (a) walking with preferred walking speed (PWS), (b) walking with 10 % increment in the PWS, (c) walking while holding a glass of water at self selected walking speed (PWSW), (d) walking normally without the glass of water at the same speed as in condition c (PWS W), (e) walking while wearing a pair of occlusion goggles at a different self selected speed (PWSG), and (f) walking normally, without the occlusion goggles at the same walking sp..