  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 (PIR) motion
sensors installed in the consecutively visited room pair carry rich latent
information about a person's gait velocity. We name this time gap transition
time and show that despite a six second refractory period of the PIR sensors,
transition time can be used to obtain an accurate representation of gait
velocity.
  Using a Support Vector Regression (SVR) approach to model the relationship
between transition time and gait velocity, we show that gait velocity can be
estimated 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 to 100x 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.
