Gait velocity estimation using time-interleaved between consecutive passive IR sensor activations

Rana, Rajib and Austin, Daniel and Jacobs, Peter G. and Karunanithi, Mohanraj and Kaye, Jeffrey (2016) Gait velocity estimation using time-interleaved between consecutive passive IR sensor activations. IEEE Sensors Journal, 16 (16). pp. 6351-6358. ISSN 1530-437X

Abstract

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 contextaware gait velocity sensing that allows for monitoring of gait trajectories in space and time.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published version cannot be displayed due to copyright restrictions.
Faculty / Department / School: Current - Institute for Resilient Regions
Date Deposited: 16 Feb 2017 00:22
Last Modified: 16 Feb 2017 00:22
Uncontrolled Keywords: gait velocity, passive infrared (PIR) motion sensors, transition time, support vector regression
Fields of Research : 08 Information and Computing Sciences > 0805 Distributed Computing > 080599 Distributed Computing not elsewhere classified
Socio-Economic Objective: C Society > 92 Health > 9299 Other Health > 929999 Health not elsewhere classified
Identification Number or DOI: 10.1109/JSEN.2016.2577708
URI: http://eprints.usq.edu.au/id/eprint/30053

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