Management of Dementia and Depression Utilizing In- Home Passive Sensor Data.
Management of Dementia and Depression Utilizing In- Home Passive Sensor Data.
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DOI：
10.4017/gt.2013.11.3.004.00
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发表时间：
2013-01-01
期刊：
影响因子：
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通讯作者：
Rantz, Marilyn
中科院分区：
文献类型：
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作者：
Galambos, Colleen;Skubic, Marjorie;Rantz, Marilyn
目得：本研究调查是否运动密度地图的基础上被动红外（PIR）运动传感器和平均超时和平均密度每小时的密度图的措施是足够敏感的，以检测随着时间的推移在心理健康的变化。方法：在传感器网络，数据记录从PIR运动传感器捕捉运动事件，人们在家里走动。如果存在连续运动，传感器将以7秒的间隔生成事件。如果居民不太活跃，事件将不太频繁地生成。Web应用程序将数据显示为活动密度图，显示每小时的事件，纵轴为小时，横轴为连续天数。颜色和强度提供了离家时间和活动水平的纹理指示。来自共生矩阵的纹理特征用于捕获活动的周期性模式（包括均匀性、局部变化和熵），并与每小时的平均运动密度和离家的平均时间相结合。两个不同密度图的相似性由在特征空间中计算的数字表示，该数字作为从一个图到另一个图的距离，或不相似性的度量。采用回顾性方法，将密度图与健康评估信息（老年抑郁量表、简易精神状态检查和简明健康调查表-12）进行比较，以确定活动模式变化与健康信息之间的一致性20。采用个案研究的方法，分析了5个有精神健康问题的人的密度图。这些密度图与每天每小时离开公寓的平均时间和在家小时的平均密度和心理健康评估分数一起进行了沿着审查，以确定是否有活动变化以及活动模式是否反映了心理健康状况的变化。运动密度图显示了客户活动的视觉变化，包括昼夜节律，离家时间和一般活动水平（久坐与闲逛）。这些措施是足够敏感的，产生的平均时间出公寓和平均密度每小时小时在家里的时间，表明显着的变化。有证据表明与健康评估分数一致。这项试点研究表明，密度图可用作早期疾病检测的工具。研究结果表明，传感器技术有可能增强传统的医疗评估和护理协调。
PURPOSE: This study investigates whether motion density maps based on passive infrared (PIR) motion sensors and the average time out and average density per hour measures of the density map are sensitive enough to detect changes in mental health over time.METHOD: Within the sensor network, data are logged from PIR motion sensors which capture motion events as people move around the home. If there is continuous motion, the sensor will generate events at 7 second intervals. If the resident is less active, events will be generated less frequently. A web application displays the data as activity density maps showing events per hour with hours on the vertical axis and progressive days on the horizontal axis. Color and intensity provide textural indications of time spent away from home and activity level. Texture features from the co-occurrence matrix are used to capture the periodicity pattern of the activity (including homogeneity, local variation, and entropy) and are combined with the average motion density per hour and the average time away from home. The similarity of two different density maps is represented by a number that is computed in feature space as the distance from one map to the other, or a measure of dis-similarity. Employing a retrospective approach, density maps were compared with health assessment information (Geriatric Depression Scale, Mini Mental State Exam, and Short Form Health Survey -12) to determine congruence between activity pattern changes and the health information20. A case by case study method, analyzed the density maps of 5 individuals with identified mental health issues. These density maps were reviewed along with the averages of time out of apartment per day per hour and average density per hour for hours at home and mental health assessment scores to determine if there were activity changes and if activity patterns reflected changes in mental health conditions.RESULTS & DISCUSSION: The motion density maps show visual changes in the client's activity, including circadian rhythm, time away from home, and general activity level (sedentary vs. puttering). The measures are sensitive enough, yielding averages of time out of apartment and average density per hour for hours at home that indicate significant change. There is evidence of congruence with health assessment scores. This pilot study demonstrates that density maps can be used as a tool for early illness detection. The results indicate that sensor technology has the potential to augment traditional health care assessments and care coordination.