TY - JOUR
T1 - Gait velocity estimation using time-interleaved between consecutive passive IR Sensor Activations
AU - Rana, Rajib
AU - Austin, Daniel
AU - Jacobs, Peter G.
AU - Karunanithi, Mohanraj
AU - Kaye, Jeffrey
N1 - Publisher Copyright:
© 2001-2012 IEEE.
PY - 2016/8
Y1 - 2016/8
N2 - 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.
AB - 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.
KW - Gait velocity
KW - passive infrared (PIR) motion sensors
KW - support vector regression
KW - transition time
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U2 - 10.1109/JSEN.2016.2577708
DO - 10.1109/JSEN.2016.2577708
M3 - Article
AN - SCOPUS:84982218158
SN - 1530-437X
VL - 16
SP - 6351
EP - 6358
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 16
M1 - 7486123
ER -