{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T13:05:55Z","timestamp":1777381555896,"version":"3.51.4"},"reference-count":46,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2017,2,22]],"date-time":"2017-02-22T00:00:00Z","timestamp":1487721600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Ambient Intelligence and Smart Environments"],"published-print":{"date-parts":[[2017,2,22]]},"abstract":"<jats:p>\n                    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\u2019 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 (\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mi mathvariant=\"italic\">r<\/mml:mi>\n                        <mml:mo>=<\/mml:mo>\n                        <mml:mn>0.937<\/mml:mn>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    ,\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mi mathvariant=\"italic\">p<\/mml:mi>\n                        <mml:mo mathvariant=\"normal\">&lt;<\/mml:mo>\n                        <mml:mn>0.001<\/mml:mn>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    ) 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.\n                  <\/jats:p>","DOI":"10.3233\/ais-170428","type":"journal-article","created":{"date-parts":[[2017,2,21]],"date-time":"2017-02-21T11:32:51Z","timestamp":1487676771000},"page":"163-179","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":11,"title":["Exploratory analysis of older adults\u2019 sedentary behavior in the primary living area using kinect depth data"],"prefix":"10.1177","volume":"9","author":[{"given":"Tanvi","family":"Banerjee","sequence":"first","affiliation":[{"name":"Wright State University","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Yefimova","sequence":"additional","affiliation":[{"name":"University of California","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James M.","family":"Keller","sequence":"additional","affiliation":[{"name":"University of Missouri","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marjorie","family":"Skubic","sequence":"additional","affiliation":[{"name":"University of Missouri","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diana Lynn","family":"Woods","sequence":"additional","affiliation":[{"name":"Azusa Pacific University","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marilyn","family":"Rantz","sequence":"additional","affiliation":[{"name":"University of Missouri","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2017,2,22]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1093\/ije\/dys118"},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","unstructured":"S.\u00a0Baker S.\u00a0Roth D.\u00a0Scharstein M.\u00a0Black J.\u00a0Lewis and R.\u00a0Szeliski A database and evaluation methodology for optical flow in: Proc. 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