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Multi-label classification using label combination to recognize human activity based on various sensor positions


Zainudin, Muhammad Noorazlan Shah and Mohamed, Raihani and Sulaiman, Md. Nasir and Perumal, Thinagaran and Mustapha, Norwati and Ahmad Nazri, Azree Shahrel (2017) Multi-label classification using label combination to recognize human activity based on various sensor positions. In: 6th International Conference on Computing and Informatics (ICOCI 2017), 25-27 Apr. 2017, Kuala Lumpur, Malaysia. (pp. 669-674).


Activity recognition using accelerometer sensor shows the good and positive impact in current health style perseverance. The sensor already built in the various smartphones, belts, and tapes for easy usage and applications in order to detect person’s activity in real time response. Ram-pant research has been done to measure the effectiveness of accelerometer location detection of the activity. Hence, a separate method has been used to tackle the issues. This paper proposed multi-label classification (MLC) to look the effectiveness of the sensor location with a correlation of types of activity at the same time the similar activity could be discriminated accurately. The traditional single label works using Random Forest (RF) was used and the accuracy of the model will be compared with MLC using LC (Label Combination) with RF as a base classifier. As a result, MLC outperform the traditional single classifier approach to distinguish the accuracy for both stairs activities such as downstairs and upstairs is highly accurate using proposed method. At the same time, the pocket position successfully considered as the best sensor position to recognize various types of activities.

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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Computer Science and Information Technology
Publisher: School of Computing, UUM College of Arts and Sciences
Keywords: Activity recognition; Smartphones; Accelerometer; Multi-label classification; Label combination; Random forest
Depositing User: Nabilah Mustapa
Date Deposited: 05 Jul 2018 09:35
Last Modified: 05 Jul 2018 09:35
URI: http://psasir.upm.edu.my/id/eprint/64452
Statistic Details: View Download Statistic

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