Gait variability analysis using continuous RF data streams of human activity

Abstract

The detection and rapid response to critical health events, such as falls, as well as monitoring of balance and gait for fall risk assessment are important to ensuring positive outcomes, longevity, and quality-of-life, especially in older adults. Mobility limitations affect more than one-third of adults and older adults with a history of falls have exhibited increased gait variability relative to younger adults. However, quantitative gait analysis in laboratories equipped with gold standard sensors, are expensive and may not be easily accessible for aging and rural communities. Thus, radio frequency (RF) sensing is an emerging technology for in-home health monitoring because they are non-contact devices that are effective in the dark and do not acquire private visual recordings. This work proposes a technique for the estimation of step-time variability from continuous streams of RF data acquired while a person does daily activities. The proposed approach is first validated through comparison with a Vicon motion capture system in a lab environment, after which the proposed approaches are used to analyze a continuous stream of daily activity data acquired from 5 different participants. Our results show that RF sensing is a promising, effective tool for gait variability assessment in natural, in-home environments. • Step-time estimation from continuous RF data streams of sequential activity. • Segmentation, human activity classification, and gait variability assessment. • Enables gait analysis and fall risk assessment in home environments. • Radar enables non-intrusive, remote assessment of natural gait.