A Smartphone -Based Model for Human Activity Recognition

Ali Al-Taei
2017 Ibn AL- Haitham Journal For Pure and Applied Science  
Activity recognition (AR) is a new interesting and challenging research area with many applications (e.g. healthcare, security, and event detection). Basically, activity recognition (e.g. identifying user's physical activity) is more likely to be considered as a classification problem. In this paper, a combination of 7 classification methods is employed and experimented on accelerometer data collected via smartphones, and compared for best performance. The dataset is collected from 59
more » ... s who performed 6 different activities (i.e. walk, jog, sit, stand, upstairs, and downstairs). The total number of dataset instances is 5418 with 46 labeled features. The results show that the proposed method of ensemble boost-based classifier overperforms other classifiers that were examined in this research paper.
doi:10.30526/30.3.1628 fatcat:mam7u4ro2vb2hizzmazdjyn6cu