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Gait velocity has been consistently shown to be an important indicator andpredictor of health status, especially in older adults. Gait velocity is oftenassessed clinically, but the assessments occur infrequently and thus do notallow optimal detection of key health changes when they occur. In this paper,we show the time it takes a person to move between rooms in their home denoted'transition times' can predict gait velocity when estimated from passiveinfrared motion detectors installed in a patient's own home. Using a supportvector regression approach to model the relationship between transition timesand gait velocities, we show that velocity can be predicted with an averageerror less than 2.5 cm/sec. This is demonstrated with data collected over a 5year period from 74 older adults monitored in their own homes. This method issimple and cost effective, and has advantages over competing approaches suchas: obtaining 20 to100x more gait velocity measurements per day, and offeringthe fusion of location specific information with time stamped gait estimates.These advantages allow stable estimates of gait parameters (maximum or averagespeed, variability) at shorter time scales than current approaches. This alsoprovides a pervasive in home method for context aware gait velocity sensingthat allows for monitoring of gait trajectories in space and time.

Continuous Gait Velocity Estimation using Houseohld Motion Detectors