期刊
SENSORS
卷 14, 期 9, 页码 16181-16195出版社
MDPI
DOI: 10.3390/s140916181
关键词
activity recognition; smartphone; multimodal sensors; naive Bayes; life-log
资金
- MSIP (Ministry of Science, ICT & Future Planning), Korea, under the ITRC (Information Technology Research Center) [NIPA-2013-(H0301-13-2001]
- Industrial Strategic Technology Development Program [10035348]
- Ministry of Knowledge Economy (MKE, Korea)
- Korea Evaluation Institute of Industrial Technology (KEIT) [R0101-14-0001] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- Ministry of Public Safety & Security (MPSS), Republic of Korea [H0301-13-2001, H0301-14-1003] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Activity recognition for the purposes of recognizing a user's intentions using multimodal sensors is becoming a widely researched topic largely based on the prevalence of the smartphone. Previous studies have reported the difficulty in recognizing life-logs by only using a smartphone due to the challenges with activity modeling and real-time recognition. In addition, recognizing life-logs is difficult due to the absence of an established framework which enables the use of different sources of sensor data. In this paper, we propose a smartphone-based Hierarchical Activity Recognition Framework which extends the Naive Bayes approach for the processing of activity modeling and real-time activity recognition. The proposed algorithm demonstrates higher accuracy than the Naive Bayes approach and also enables the recognition of a user's activities within a mobile environment. The proposed algorithm has the ability to classify fifteen activities with an average classification accuracy of 92.96%.
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