Multi-label classification using label combination to recognize human activity based on various sensor positions - Zainudin, Muhammad Noorazlan Shah; Mohamed, Raihani; Sulaiman, Md. Nasir; Perumal, Thinagaran; Mustapha, Norwati; Ahmad Nazri, Azree Shahrel (School of Computing, UUM College of Arts and Sciences, 2017)
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.