a resource optimized physical movement monitoring scheme for environmental and on-body sensor networks
Perhaps the most significant challenge in design of on-body sensors is the wearability concern. This concern requires that the size of the nodes (sensors, processing units and batteries) is minimized. Therefore, the computation and communication executed in on-body nodes must be moderated significantly. In this paper, we propose a collaborative signal processing scheme for physical movement monitoring that utilizes on-body and environmental sensors. The environmental sensor nodes perform the bulk of the signal processing and provide feedback to the on-body sensor nodes. This is due to the fact that the environmental sensor nodes have access to more powerful processing units and an unlimited energy supply. The feedback simplifies the signal processing on the on-body nodes significantly. We achieve this by performing a hierarchical classification and introducing a probabilistic measure on likelihood of possible classes for the final level of classification on on-body sensor nodes. The experimental results show the effectiveness of our method. On average the classification accuracy is reduced by 3\% while the computational complexity can be scaled down by one order of magnitude compared to a global and comprehensive classification scheme.
physical movement monitoring sensor networks wearable computing health-care