Passive In-home Estimation of Gait Velocity From Motion Sensors.
Year: 2013
Type: preprint
Abstract: Gait velocity has been consistently shown to be an important indicator and predictor of health status, especially in elderly populations. Gait velocity is often assessed clinically, but clinical assessments occur infrequently and thus do not allow optimal detection of key health changes when they occur. In this paper, we propose using the time it takes a person to move between rooms in their home - referred to as 'transition times' - estimated from passive infrared motion sensors installed in a patients own home to predict gait velocity. By using a support vector regression approach to model the relationship between transition times and observed gait velocities, we show that we can predict unobserved velocities accurately. In particular, we demonstrate that the proposed approach has an average error of 4 cm/sec using data collected over a 5 year period from 76 study participants monitored both in their own homes and within a clinical setting. The proposed method is simple and cost effective, and has advantages over competing approaches such as not requiring patients to wear a device or needing dedicated sensors to measure gait. Furthermore, this method provides substantially more frequent estimates of gait velocity than are provided by other approaches.
Source: arXiv (Cornell University)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2226178156