期刊
IEEE ACCESS
卷 7, 期 -, 页码 33834-33850出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2904122
关键词
Activity monitoring; Apriori; body sensors; FP growth; productive periodic frequent patterns; smart data; temporal database
资金
- Deanship of Scientific Research at King Saud University [RGP-281]
Extracting indicative characteristics from the sensor data provide diverse avenues for improving the well-being of the elderly people living alone in their homes through understanding and identifying their behavioral patterns while considering any environmental changes. In this paper, we present a new model to explore the challenges associated with mining patterns from the body sensor data and their potential use in discovering regular human routines through mining periodic patterns from a non-uniform temporal database. The non-uniform nature of the temporal database adds more challenges to the mining of periodic patterns as the items may have different periodicity and frequency occurrences. Another challenge is how to discover the correlation between the discovered patterns. In addition, we examine the context-enriched periodic patterns which provide more insights about residents' health. A new algorithm for the contextualized-correlated periodic pattern mining from a non-uniform temporal database is presented along with an extensive evaluation of its performance using a real-life dataset.
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