Masamichi Shimosaka
ABSTRACT
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.
- A. Kono, I. Kai, C. Sakato, and Z. R. Laurence. Frequency of going outdoors predicts long-range functional chnage among ambulatory frail elder living at home. Archives of Gerontology and Geriatrics, 45:233--242, 2007.Google Scholar
Cross Ref
- M. Lawton and E. M. Brody. Assessment of older people; self-maintaining and instumental activiityes of daily living. Gerontologist, 9(3):179--186, 1969.Google ScholarCross Ref
- K. Okumiya, K. Matsubayashi, and T. Nakamura. The timed up and go test and manual button score are useful predictors of functional decline in basic and instrumental adl in community-dwelling older people. J Am Geriatr So, 47(4):497--498, 1999.Google ScholarCross Ref
- W. H. Organization. International classification of functioning, disability and health (ICF). 2001.Google Scholar
- M. Shimosaka, T. Ishino, H. Noguchi, T. Sato, and T. Mori. Detecting human activity profiles with dirichlet enhanced inhomogeneous poisson processes. In ICPR, pages 4384--4387, 2010. Google ScholarDigital Library
- R. Tibshirani. Regression shrinkage and selection via the lasso. J. R. Statis. Soc. B, 58:267--288, 1996.Google ScholarCross Ref
- S. Tominaga, M. Shimosaka, R. Fukui, and T. Sato. A unified framework for modeling and predicting going-out behavior. In Pervasive2012. Google ScholarDigital Library
Index Terms
- Health score prediction using low-invasive sensors
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