A Smartphone Location Independent Activity Recognition Method Based on the Angle Feature

The smartphone-based human activity recognition method is helpful in the context awareness, health monitoring and inertial positioning. Comparing with the traditional wearable computing which fixes accelerometers on the specific positions of a user body, the activity recognition method based on a smartphone faces the problem of varying sensor locations. In this paper, we lay emphasis on the study of a feature extraction algorithm which is independent of the phone locations. First, the angle motion model is presented to illustrate the human activities. The model describes the difference among walking, going upstairs and going downstairs. Then, an angle feature extraction algorithm is proposed according to the angle motion model. Our analysis shows that different activities have significantly different angle features. Finally, our experiments are introduced. The experiments include data collecting, analysis of experiments results. The experiments results show that the recognition accuracy improved by 2% through adding the angle feature to original features.