Performance Evaluation of Person Classification Techniques Based on Human Activities Using Smartphones' Sensors

Kim young in

doi:10.34163/jkits.2018.13.6.018

To distinguish a smartphone user, some studies have been researched to analyze various human activities of natural daily life and classify users without using an additional step, such as using fingerprints or iris. However, it is not sufficient to analyze the diversity of human activities, the analysis per subject and the experiments using techniques that were better in previous related studies. In this paper, we propose a method that classifies users with various human activities by experimenting with a public data set of six activities data collected from thirty subjects using acceleration and gyroscope sensor in smartphone and with seven classification techniques that are better to existing research results. As a result of the comparison experiment with seven classification techniques, AdaBoost showed good results in the experiment in which the user classifies all of the six activities. Next, Adaboost showed the good results in laying, sitting and standing according to each activity by using the top four techniques which were better performance in classification of all activities. In walking, walking downstairs and walking upstairs, we could find out higher accuracy than the previous three activities, and SVM showed relatively excellent accuracy. Especially, the accuracy of user classification using walking upstairs is the highest. The results of this experiment are expected to become an important basis for the development of technologies that naturally and continuously classify users in everyday life.

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REFERENCES (16)
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* 2022년 이후 발행 논문의 참고문헌은 현재 구축 중입니다.

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