Automated Health Alerts Using In-Home Sensor Data for Embedded Health Assessment

Marjorie Skubic, Rainer Dane Guevara, Marilyn Rantz, Marjorie Skubic, Rainer Dane Guevara, Marilyn Rantz

Abstract

We present an example of unobtrusive, continuous monitoring in the home for the purpose of assessing early health changes. Sensors embedded in the environment capture behavior and activity patterns. Changes in patterns are detected as potential signs of changing health. We first present results of a preliminary study investigating 22 features extracted from in-home sensor data. A 1-D alert algorithm was then implemented to generate health alerts to clinicians in a senior housing facility. Clinicians analyze each alert and provide a rating on the clinical relevance. These ratings are then used as ground truth for training and testing classifiers. Here, we present the methodology for four classification approaches that fuse multisensor data. Results are shown using embedded sensor data and health alert ratings collected on 21 seniors over nine months. The best results show similar performance for two techniques, where one approach uses only domain knowledge and the second uses supervised learning for training. Finally, we propose a health change detection model based on these results and clinical expertise. The system of in-home sensors and algorithms for automated health alerts provides a method for detecting health problems very early so that early treatment is possible. This method of passive in-home sensing alleviates compliance issues.

Keywords: Behavioral bio-markers; eldercare monitoring; health alerts; in-home sensing.

Figures

Figure 1.
Figure 1.
Integrated sensor network with health alerts and ratings on clinical relevance.
Figure 2.
Figure 2.
A typical one bedroom apartment with embedded sensors, as used in this study.
Figure 3.
Figure 3.
Principal components analysis (PCA) reduction of sensor data collected on case study #2 with features extracted from the motion and bed sensor data. Each data point represents one day (599 days total; 32 abnormal days).
Figure 4.
Figure 4.
PCA reduction of 24-D feature vectors (top) and 12-D feature vectors (bottom) from the early illness alert study. Red O’are poor alert days; blue X’s are good alert days as rated by clinicians.
Figure 5.
Figure 5.
PCA reduction of 6-D feature vectors from the early illness alert study. Red O’s are poor alert days; blue X’s are good alert days as rated by clinicians.
Figure 6.
Figure 6.
ROC curves for the SVM and fuzzy pattern tree (FPT) using 6-D feature vectors.

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