Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Banerjee, Tanvia; * | Yefimova, Mariab | Keller, James M.c | Skubic, Marjoriec | Woods, Diana Lynnd | Rantz, Marilyne
Affiliations: [a] Department of Computer Science and Engineering, Wright State University, Dayton, OH 45435, USA. E-mail: [email protected] | [b] School of Nursing, University of California, Los Angeles, CA, USA. E-mail: [email protected] | [c] School of Electrical Engineering, University of Missouri, Columbia, MO 65211, USA. E-mails: [email protected], [email protected] | [d] School of Nursing, Azusa Pacific University, Azusa, CA, USA. E-mail: [email protected] | [e] School of Nursing, University of Missouri, Columbia, Columbia, MO, USA. E-mail: [email protected]
Correspondence: [*] Corresponding author. E-mail: [email protected].
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.
Keywords: Activity recognition, depth images, Gerontechnology, Kinect sensor, sit-to-stand analysis
DOI: 10.3233/AIS-170428
Journal: Journal of Ambient Intelligence and Smart Environments, vol. 9, no. 2, pp. 163-179, 2017
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]