Health score prediction using low-invasive sensors

Scores of health state for elderly people are regarded as important in nursing or medical fields. On the other hand, gaining the scores needs nurses to execute questionnaires. Owing to this, the execution rate for the health assessment is still low in ordinary homes. To solve this problem, we propose a method to predict the health score by using low-invasive sensors. We adopt regression as the prediction method and construct features to absorb the individual difference. As a part of feasibility study of social participation for elderly people, we execute the survey of health state using questionnaires by a nurse and install low-invasive sensors in real life simultaneously. Experimental result in the feasibility study shows a promise of the score prediction from sensor data. In addition, the result suggests that the extraction of features related to living behaviors improves the accuracy compared to using raw sensor data.