Continuous Gait Velocity Estimation using Houseohld Motion Detectors

TL;DR

This study introduces a passive, cost-effective method using household motion detectors and support vector regression to estimate gait velocity in older adults, enabling frequent, in-home health monitoring.

Contribution

It presents a novel approach to predict gait velocity from transition times between rooms, with high accuracy, using passive infrared sensors in home environments.

Findings

Average prediction error less than 2.5 cm/sec.

Collected data over 5 years from 74 older adults.

Provides 20 to 100 times more measurements daily.

Abstract

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.