Gait velocity has been consistently shown to be an important indicator and predictor of health status, especially in older adults. It is often assessed clinically, but the assessments occur infrequently and do not allow optimal detection of key health changes when they occur. In this paper, we show that the time gap between activations of a pair of passive infrared motion sensors in the consecutively visited room-pair carry rich latent information about a person's gait velocity. We name this time gap transition time and modeling the relationship between transition time and gait velocity, and using a support vector regression approach, we show that gait velocity can be estimated with an average error of <2.5 cm/s. Our method is simple and cost effective and has advantages over competing approaches such as: obtaining 20-100 times more gait velocity measurements per day. It also provides a pervasive in-home method for context-aware gait velocity sensing that allows for monitoring of gait trajectories in space and time.
- Gait velocity
- passive infrared (PIR) motion sensors
- support vector regression
- transition time
ASJC Scopus subject areas
- Electrical and Electronic Engineering