Detecting Human Activity Profiles with Dirichlet Enhanced Inhomogeneous Poisson Processes
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A unified framework for modeling and predicting going-out behavior
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Hi-index | 0.00 |
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.