TY - GEN
T1 - A hierarchical rule-based activity recognition system with frequency pattern mining
AU - Li, Ji Yu
AU - Huang, Po Cheng
AU - Kuo, Yau Hwang
AU - Lee, Kuan Rong
PY - 2009/12/1
Y1 - 2009/12/1
N2 - In this paper, a hierarchical system is proposed for generating personalization action and activity recognition rules. Multi-level decision rule mining approach in our system not only discovers personal habit of device using, but also finds personal pattern of devices operation manner. First, it processes non-sequential procedure, which mines user's historical action database for constructing rules to recognize actions. Second, it creates action sequence recognized by using action rules generated before. Sequential mining method is adopted to find out the most frequent sequential pattern which is seemed as personal habit of device operating manner. Finally, non-sequential rules and sequential patterns are checked to recognize user's activity. The system generates rules depending on user's habit rather than being specified by designer who only creates general rules. The simulation results reveal that the activity recognition accuracies with 10% and 20% noise interference are 87.33% and 83.33% respectively.
AB - In this paper, a hierarchical system is proposed for generating personalization action and activity recognition rules. Multi-level decision rule mining approach in our system not only discovers personal habit of device using, but also finds personal pattern of devices operation manner. First, it processes non-sequential procedure, which mines user's historical action database for constructing rules to recognize actions. Second, it creates action sequence recognized by using action rules generated before. Sequential mining method is adopted to find out the most frequent sequential pattern which is seemed as personal habit of device operating manner. Finally, non-sequential rules and sequential patterns are checked to recognize user's activity. The system generates rules depending on user's habit rather than being specified by designer who only creates general rules. The simulation results reveal that the activity recognition accuracies with 10% and 20% noise interference are 87.33% and 83.33% respectively.
UR - http://www.scopus.com/inward/record.url?scp=77951492557&partnerID=8YFLogxK
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U2 - 10.1109/ICICIC.2009.19
DO - 10.1109/ICICIC.2009.19
M3 - Conference contribution
AN - SCOPUS:77951492557
SN - 9780769538730
T3 - 2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009
SP - 1554
EP - 1557
BT - 2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009
T2 - 2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009
Y2 - 7 December 2009 through 9 December 2009
ER -