Abstract
A cloud based health care system is proposed in this paper for the elderly by providing abnormal gait behavior detection, classification, online diagnosis, and remote aid service. Intelligent mobile terminals with triaxial acceleration sensor embedded are used to capture the movement and ambulation information of elderly. The collected signals are first enhanced by a Kalman filter. And the magnitude of signal vector features is then extracted and decomposed into a linear combination of enhanced Gabor atoms. The Wigner-Ville analysis method is introduced and the problem is studied by joint time-frequency analysis. In order to solve the large-scale abnormal behavior data lacking problem in training process, a cloud based incremental SVM (CI-SVM) learning method is proposed. The original abnormal behavior data are first used to get the initial SVM classifier. And the larger abnormal behavior data of elderly collected by mobile devices are then gathered in cloud platform to conduct incremental training and get the new SVM classifier. By the CI-SVM learning method, the knowledge of SVM classifier could be accumulated due to the dynamic incremental learning. Experimental results demonstrate that the proposed method is feasible and can be applied to aged care, emergency aid, and related fields.
Highlights
In most developed countries, the aging problem has existed for many decades, while, in developing countries, population aging has taken place relatively recently due to the demographic changes and strong gains in life expectancy
2400 imitated sample data were collected from young people and they were divided into two groups
The I1 set with 300 incremental data samples of 45-year-olds was used to conduct first time cloud based incremental Support vector machine (SVM) learning; the SVM classifier is updated and denoted by Ω1o
Summary
The aging problem has existed for many decades, while, in developing countries, population aging has taken place relatively recently due to the demographic changes and strong gains in life expectancy. The share of the working-age population will continue to fall during the four decades, to about 51 per cent in 2050 [1]. In contrast the old-age dependency ratio will grow rapidly, as a result a society’s capacity for taking care of its elderly members could be overwhelmed. People who live alone with illness, health problems or action barrier, are especially in need of online supervision in case of emergency situations, such as sudden sign of illness, accidental fall, or shock. The fall as one of “four giants of geriatrics” in old age, becomes a major public health problem, which could lead to serious injury even death. To prevent dangerous situations arising when elderly lives alone in their homes and to enhance the quality of their lives, more health care in general and more specialized services are required
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