Automatic detection of lift-off and touch-down of a pick-up walker using 3D kinematics

Walking aids have been associated with falls and it is believed that incorrect use limits their usefulness. Measures are therefore needed that characterize their stable use and the classification of key events in walking aid movement is the first step in their development. This study presents an automated algorithm for detection of lift-off (LO) and touch-down (TD) events of a pick-up walker. For algorithm design and initial testing, a single user performed trials for which the four individual walker feet lifted off the ground and touched down again in various sequences, and for different amounts of frame loading (Dataset_1). For further validation, ten healthy young subjects walked with the pick-up walker on flat ground (Dataset_2a) and on a narrow beam (Dataset_2b), to challenge balance. One 88-year-old walking frame user was also assessed. Kinematic data were collected with a 3D optoelectronic camera system. The algorithm detected over 93% of events (Dataset_1), and 95% and 92% in Dataset_2a and b, respectively. Of the various LO/TD sequences, those associated with natural progression resulted in up to 100% correctly identified events. For the 88-year-old walking frame user, 96% of LO events and 93% of TD events were detected, demonstrating the potential of the approach.