Exploratory analysis of older adults' sedentary behavior in the primary living area using kinect depth data
Exploratory analysis of older adults' sedentary behavior in the primary living area using kinect depth data
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DOI：
10.3233/ais-170428
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发表时间：
2017-01-01
影响因子：
1.7
通讯作者：
Rantz, Marilyn
中科院分区：
文献类型：
--
作者：
Banerjee, Tanvi;Yefimova, Maria;Rantz, Marilyn
我们描述了用一种新颖的深度数据计算机视觉算法捕获的老年人久坐行为临床显著变化的案例研究。一个不显眼的微软Kinect传感器不断记录老年人在TigerPlace公寓主要生活空间的活动。利用10个月的深度数据，我们开发了一种情境感知算法来检测定义久坐行为的个人特定姿势变化（从坐到站和从站到坐事件）。我们的算法在5个居民的33,120分钟数据中与人工分析原始深度数据作为地面真实数据进行了鲁棒性验证，具有很强的相关性（r = 0.937, p < 0.001），平均误差为17分钟/天。我们的发现在两个关于久坐活动及其与功能衰退临床评估关系的案例研究中得到了强调。我们的研究结果显示，未来的研究有很大的潜力，可以建立一个通用的平台，通过家庭活动监测系统自动研究久坐行为模式。
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.