Galambos Colleen, Skubic Marjorie, Wang Shaung, Rantz Marilyn
University of Missouri, School of Social Work.
Gerontechnology. 2013 Jan 1;11(3):457-468. doi: 10.4017/gt.2013.11.3.004.00.
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.
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 information. 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.
本研究调查基于被动红外（PIR）运动传感器的运动密度图以及密度图的平均外出时间和每小时平均密度测量值是否足够灵敏，以检测心理健康随时间的变化。
在传感器网络中，记录来自PIR运动传感器的数据，这些传感器在人们在家中走动时捕获运动事件。如果存在连续运动，传感器将以7秒的间隔生成事件。如果居民活动较少，事件生成的频率会更低。一个网络应用程序将数据显示为活动密度图，垂直轴表示小时，水平轴表示连续的天数，显示每小时的事件。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（包括同质性、局部变化和熵），并与每小时的平均运动密度和平均离家时间相结合。两个不同密度图的相似性由一个数字表示，该数字在特征空间中作为从一个图到另一个图的距离计算得出，或者是一种不相似性度量。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短健康调查问卷-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。采用个案研究方法，分析了5名已确诊心理健康问题个体的密度图。这些密度图与每天每小时的平均外出时间、在家每小时的平均密度以及心理健康评估分数一起进行审查，以确定是否存在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）。这些测量足够灵敏，得出的平均外出时间和在家每小时的平均密度表明有显著变化。有证据表明与健康评估分数一致。这项初步研究表明，密度图可作为早期疾病检测的工具。结果表明，传感器技术有可能增强传统的医疗保健评估和护理协调。
