Wearable Recognition System for Physical Activities

Abstract

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 basic physical activities like lying, sitting, walking, standing, cycling, running, ascending and descending stairs 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 8 different activities collected from 12 different subjects. Our results indicate that the system has a good accuracy rate.