Abstract
The concept of a Smart House has received research and consumer attention as a promising solution to provide safe and independent living for an aging population. These benefits can be enriched with the incorporation of Senior Healthwatch, a home health monitoring system that collects data from the sensors installed in the house and provides critical information about the residents' health condition during their daily activity. This study introduces a neural network modeling approach combined with cluster and decision tree analyses as a means to develop the inferential logic of Senior Healthwatch. Its role is to convert the data obtained from the Smart House into more meaningful information about the residents' daily activity such as the times for meal preparation, eating, and cleaning. The results of this study can become a major feature in the Smart House technology.
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