Physical activity recognizer based on multimodal sensors in smartphone for ubiquitous-lifecare services
Source of Publication
2017 IEEE AFRICON: Science, Technology and Innovation for Africa, AFRICON 2017
© 2017 IEEE. Smartphone-based activity recognition is an emerging field of research that enables a large number of human-centric applications in the u-lifecare domain. Currently, major challenges include the development of real-time position independent and lightweight classifier models to recognize the physical activities inside the smartphone environment. In this paper, we propose a real-time position independent physical activity recognizer that utilizes the embedded accelerometer, ambient light and proximity sensors of smartphone to recognize the physical activities. To validate our model, we implement it in an open source Android platform to recognize six physical activities and performed extensive experiments over 10 subjects. We obtained 88% of class-accuracy and 91.55% F-measures. It is expected that our model would be a practical and realistic solution for physical activity recognition due to its unobtrusive nature and real-time classification of activities.
Fahim, Muhammad; Khattak, Asad Masood; Aleem, Saiqa; and Katheeri, Haseena Al, "Physical activity recognizer based on multimodal sensors in smartphone for ubiquitous-lifecare services" (2017). Scopus Indexed Articles. 1245.