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

Article


Rana, Rajib, Austin, Daniel, Jacobs, Peter G., 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. https://doi.org/10.1109/JSEN.2016.2577708
Article Title

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

ERA Journal ID4437
Article CategoryArticle
AuthorsRana, Rajib (Author), Austin, Daniel (Author), Jacobs, Peter G. (Author), Karunanithi, Mohanraj (Author) and Kaye, Jeffrey (Author)
Journal TitleIEEE Sensors Journal
Journal Citation16 (16), pp. 6351-6358
Number of Pages8
Year2016
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN1530-437X
1558-1748
Digital Object Identifier (DOI)https://doi.org/10.1109/JSEN.2016.2577708
Web Address (URL)http://ieeexplore.ieee.org/document/7486123/?reload=true
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.

Keywordsgait velocity, passive infrared (PIR) motion sensors, transition time, support vector regression
ANZSRC Field of Research 2020461199. Machine learning not elsewhere classified
Public Notes

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Byline AffiliationsInstitute for Resilient Regions
Oregon Health and Science University, United States
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
Institution of OriginUniversity of Southern Queensland
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