A data mining approach for fall detection by using k-nearest neighbour algorithm on wireless sensor network data

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A data mining approach for fall detection by using k-nearest neighbour algorithm on wireless sensor network data

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Fall detection technology is critical for the elderly people. In order to avoid the need of full time care giving service, the actual trend is to encourage elderly to stay living autonomously in their homes as long as possible. Reliable fall detection methods can enhance life safety of the elderly and boost their confidence by immediately alerting fall cases to caregivers. This study presents an algorithm of fall detection, which detects fall events by using data-mining approach. The authors' proposed method performs detection in two steps. First, it collects the wireless sensor network (WSN) data in stream format from sensor devices. Second, it uses k-nearest neighbour algorithm, that is, well-known lazy learning algorithm to detect fall occurrences. It detects falls by identifying the fall patterns in the data stream. Experiments show that the proposed method has promising results on WSN data stream in detecting falls.

Inspec keywords: data mining; wireless sensor networks

Other keywords: wireless sensor network data; sensor devices; WSN data stream; fall detection methods; data mining; lazy learning algorithm; full time care giving service; k-nearest neighbour algorithm

Subjects: Wireless sensor networks; Data handling techniques; Knowledge engineering techniques

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