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.
Banerjee, T., Yefimova, M., Keller, J., Skubic, M., Woods, D.L., & Rantz, M. (2017). Exploratory analysis of older adults’ sedentary behavior in the primary living area using Kinect depth data. Journal of Ambient Intelligence and Smart Environments, 9(2), 163-179.View the PDF