Recognizing Physical Activities by Wearable Computing

One of the major scientific undertakings over the past few years has been exploring the interaction between humans and machines in mobile environments. Physical activity is a major part of a user's context for wearable computing applications. The system should be able to acquire the user's physical activities by using body worn sensors. We want to develop a personal activity recognition system that is practical, reliable, and can be used for health-care related applications. We propose to use the axivity device [1] which is a ready-made, light weight, small and easy to use device for identifying physical activities like lying, walking, running, sitting, standing, cycling, ascending stairs, descending stairs, strength training and swimming using decision tree classifier. In this paper, we present an approach to build a system that exhibits this property and provides evidence based on data for 10 different activities collected from 32 different subjects. Our results indicate that the system has a good accuracy rate.