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http://dx.doi.org/10.14372/IEMEK.2018.13.4.215

A Knowledge Based Physical Activity Evaluation Model Using Associative Classification Mining Approach  

Son, Chang-Sik (DGIST)
Choi, Rock-Hyun (DGIST)
Kang, Won-Seok (DGIST)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.13, no.4, 2018 , pp. 215-223 More about this Journal
Abstract
Recently, as interest of wearable devices has increased, commercially available smart wristbands and applications have been used as a tool for personal healthy management. However most previous studies have focused on evaluating the accuracy and reliability of the technical problems of wearable devices, especially step counts, walking distance, and energy consumption measured from the smart wristbands. In this study, we propose a physical activity evaluation model using classification rules, induced from the associative classification mining approach. These rules associated with five physical activities were generated by considering activities and walking times in target heart rate zones such as 'Out-of Zone', 'Fat Burn Zone', 'Cardio Zone', and 'Peak Zone'. In the experiment, we evaluated the prediction power of classification rules and verified its effectiveness by comparing classification accuracies between the proposed model and support vector machine.
Keywords
Physical activity evaluation model; Physical activity criteria; Classification mining; Class association rule;
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