A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors

Manhyung Han, Jae Bang, CD Nugent, Sally McClean, Sungyoung Lee

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)


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 Naïve Bayes approach for the processing of activity modeling and real-time activity recognition. The proposed algorithm demonstrates higher accuracy than the Naïve 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%.
Original languageEnglish
Pages (from-to)16181
Issue number9
Publication statusPublished (in print/issue) - 2014


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