An Activity Recognition System For Mobile Phones

We present a novel system that recognizes and records the motional activities of a person using a mobile phone. Wireless sensors measuring the intensity of motions are attached to body parts of the user. Sensory data is collected by a mobile application that recognizes prelearnt activities in real-time. For efficient motion pattern recognition of gestures and postures, feed-forward backpropagation neural networks are adopted. The design and implementation of the system are presented along with the records of our experiences. Results show high recognition rates for distinguishing among six different motion patterns. The recognized activity can be used as an additional retrieval key in an extensive mobile memory recording and sharing project. Power consumption measurements of the wireless communication and the recognition algorithm are provided to characterize the resource requirements of the system.