Efficient transfer of human motion data over a wireless delay tolerant network

In this paper we describe the recent development of a wireless sensor network to monitor human motion and stream relevant events over the network. The events are locally generated based on accelerometer, magnetometer and gyroscope data, taken by a sensor node, which is worn by a person. A reduced radio transmission range enables implicit coarse-grain localization. To minimize the number of radio messages, the events indicate changes in the person's motion state. This paper describes a case study, where walking steps are detected using realtime wavelet analysis of accelerometer data, which can be used for finer grain localization purposes. To enable the reliable transfer of every event with a minimal radio packet overhead, a delay tolerant network using an implicit acknowledge protocol has been implemented and tested.