TAILIEUCHUNG - Multi-label classification for physical activity recognition from various accelerometer sensor positions

This study proposed the multilabel classification technique with the Label Combination (LC) approach in order to tackle this issue. The result was compared with several state-of-the-art traditional multi-class classification approaches. The multi-label classification result significantly outperformed the traditional multi-class classification methods as well as minimized the model build time. | Journal of ICT, 17, No. 2 (April) 2018, pp: 209–231 How to cite this paper: Mohamed, R., Zainudin, M. N. S., Sulaiman, M. N., Perumal, T., & Mustapha, N. (2018). Multi-label classification for physical activity recognition from various accelerometer sensor positions. Journal of Information and Communication Technology, 17 (2), 209–231. MULTI-LABEL CLASSIFICATION FOR PHYSICAL ACTIVITY RECOGNITION FROM VARIOUS ACCELEROMETER SENSOR POSITIONS Raihani Mohamed, 1,2Mohammad Noorazlan Shah Zainudin, Md Nasir Sulaiman, 1Thinagaran Perumal & 1Norwati Mustapha 1 Faculty of Computer Science and Information Technology Universiti Putra Malaysia, Selangor, Malaysia 2 Faculty of Electronics and Computer Engineering Universiti Teknikal Malaysia Melaka, Malaysia 1 1 raihanim@; noorazlan@; nasir@; thinagaran@; norwati@ ABSTRACT In recent years, the use of accelerometers embedded in smartphones for Human Activity Recognition (HAR) has been well considered. Nevertheless, the role of the sensor placement is yet to be explored and needs to be further investigated. In this study, we investigated the role of sensor placements for recognizing various types of physical activities using the accelerometer sensor embedded in the smartphone. In fact, most of the reported work in HAR utilized traditional multi-class classification approaches to determine the types of activities. Hence, this study was to recognize the activity based on the best sensor placements that are appropriate to the activity performed. The traditional multi-class classification approach required more manual work and was time consuming to run the experiment separately. Thus, this study proposed the multilabel classification technique with the Label Combination (LC) approach in order to tackle this issue. The result was compared Received: 8 August 2017 Accepted: 20 February 2018 209 Journal of ICT, 17, No. 2 (April) 2018, pp: 209–231 with several state-of-the-art .

• Điện - Điện tử
• Multi label classification for physical activity recognition
• Various accelerometer sensor positions
• The Label Combination
• Multi class classification approaches
• Several state of the art traditional multi class classification approache