Exploratory analysis of older adults' sedentary behavior in the primary living area using kinect depth data

Abstract

We describe case studies of clinically significant changes in sedentary behavior of older adults captured with a novel computer vision algorithm for depth data. An unobtrusive Microsoft Kinect sensor continuously recorded older adults' activity in the primary living spaces of TigerPlace apartments. Using the depth data from a period of ten months, we develop a context aware algorithm to detect person-specific postural changes (sit-to-stand and stand-to-sit events) that define sedentary behavior. The robustness of our algorithm was validated over 33,120 minutes of data for 5 residents against manual analysis of raw depth data as the ground truth, with a strong correlation (r = 0.937, p < 0.001) and mean error of 17 minutes/day. Our findings are highlighted in two case studies of sedentary activity and its relationship to clinical assessments of functional decline. Our findings show strong potential for future research towards a generalizable platform to automatically study sedentary behavior patterns with an in-home activity monitoring system.