Abstract
Activity recognition in smart homes has attracted increasing attention from researchers due to its potential to recognize the occupant’s activities of daily living such as showering, putting away laundry, grooming, etc. Recognizing the activities of daily living can help to support and assist the older adults, and enable them to continue living independently within their own homes. In order to support the occupants, activity recognition algorithms need to learn from a series of observations obtained from sensors. The central question that this paper aims to address is which sensors are informative for activity recognition. In this paper, the sensor selection problem is addressed using minimum-Redundancy Maximum-Relevance (mRMR) method.
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