Gait velocity has been repeatedly shown to be an important predictor and indicator of both cognitive and physical function. Gait velocity has been successful at predicting dementia [1
], cognitive decline [3
], future disability [4
], and future risk of hospitalization [5
], especially in aging populations. Other studies have demonstrated a link between gait velocity and both executive function [6
] and cognition [8
Despite the abundant evidence supporting gait velocity as an important measure of an individuals' well being and health status, in practice gait velocity is typically assessed infrequently – often a year or more passes between assessments - and only in a clinical setting. This clinical based gait assessment methodology suffers from several shortcomings including the inability to differentiate between abrupt changes in function and slower changes occurring over time. Additionally, several visits are generally required before variability (which may also be an important indicator of function) in gait velocity can be accurately assessed. One approach to overcome these limitations using passive infrared motion sensors to measure gait velocity unobtrusively in the home setting was recently proposed and validated [10
This in-home monitoring technology was developed in the context of the Intelligent Systems for Assessing Aging Changes (ISAAC) study described in detail elsewhere [11
]. Briefly, the ISAAC study seeks to use home-based unobtrusive sensor technology in wireless networks to monitor activity patterns such as gait velocity, general activity, and time-out-of-home to detect changes in cognitive, physical, and behavioral domains. This in-home sensor technology has been installed in over 200 homes in the Portland, OR (USA) metropolitan area most of which are currently being monitored. As a result, as part of the ISAAC study we have unobtrusively collected millions of gait velocity measurements from over 200 hundred subjects in their own residence during normal daily activities.
In this paper, we discuss a method for longitudinal analysis of these in-home collected gait velocities. We proceed with a brief description of the gait velocity measurement system and data collection followed by a description of a model for the probability density function of these gait velocities including the assumptions underlying this model. We then describe an algorithm for estimating this density function and its evolution over time and provide values for the algorithm parameters that perform well both for abrupt changes and slower changes over time, based on empirical evidence. We follow by demonstrating the proposed method of analysis on two subjects; one who suffered an acute medical event during the monitoring period and one who suffered a slow decline in function over time. Finally, we conclude by discussing extensions of this methodology and future work.