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Here, we present pervasive computing technology to determine the wellness of the elderly living independently in their homes. The framework of the intelligent system consists of monitoring important daily activities through the observation of everyday object usage. The improved wellness indices defined here have helped in reducing false warnings related to the daily activities of elderly living. Time series data processing techniques have been applied to the improved wellness indicators, to take care of the dynamic situation in relation to aging of the elderly and seasonal weather variation. With a minimal number of binary motion sensors, the elderly were tracked in real time to get better information on their physical condition. The developed system was able to recognize 94 percent of the basic daily activities accurately, and at the same time the system was able to assess the wellness activities quantitatively in near real time. The well-being indices of the elderly can be used by the healthcare providers to take preventive measures on deterioration of activities of daily living, and thus consequently reduce cost of healthcare.
Suryadevara et al. (Thu,) studied this question.