  Gait velocity has been consistently shown to be an important indicator and
predictor of health status, especially in older adults. Gait velocity is often
assessed clinically, but the assessments occur infrequently and thus do not
allow 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 passive
infrared motion detectors installed in a patient's own home. Using a support
vector regression approach to model the relationship between transition times
and gait velocities, we show that velocity can be predicted with an average
error less than 2.5 cm/sec. This is demonstrated with data collected over a 5
year period from 74 older adults monitored in their own homes. This method is
simple and cost effective, and has advantages over competing approaches such
as: obtaining 20 to100x more gait velocity measurements per day, and offering
the fusion of location specific information with time stamped gait estimates.
These advantages allow stable estimates of gait parameters (maximum or average
speed, variability) at shorter time scales than current approaches. This also
provides a pervasive in home method for context aware gait velocity sensing
that allows for monitoring of gait trajectories in space and time.
