Management of Dementia and Depression Utilizing In- Home Passive Sensor Data.
OBJECTIVE
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.


METHODS
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.


CONCLUSIONS
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.
University of Missouri, School of Social Work.
Colleen
C
Galambos
Marjorie
M
Skubic
Shaung
S
Wang
Marilyn
M
Rantz
early illness detection; motion density mapping; passive sensors; technology and mental illness