Pervasive healthcare and wireless health monitoring
Mobile Networks and Applications
BSN '09 Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks
An Ultra Low Power Pulse Oximeter Sensor Based on Compressed Sensing
BSN '09 Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks
Minimizing 802.11 interference on ZigBee medical sensors
BodyNets '09 Proceedings of the Fourth International Conference on Body Area Networks
IEEE Transactions on Information Theory
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In this work, we present signal processing approaches to offload complexity from resource constrained sensor nodes to gateway/receiver nodes with better power, memory and CPU budgets. We consider the resources in commercially available cell phone platforms to fill the role of both gateway and receiver nodes in emerging Body Sensor Networks, with applications in healthcare. We leverage Compressed Sensing (CS), wherein signals can be reconstructed fairly accurately with high probability from significantly fewer measurements than that suggested by the Nyquist-Shannon sampling rate, albeit with additional complexity at the receiver. This enables receiver nodes with better resource budgets to leverage computationally intensive signal processing algorithms in lieu of on-board processing at the sensor node. We show that aliasing can be avoided at the sensor by trading analog domain complexity for a modest increase in digital domain complexity with synthetic examples and real-time pulse oximeter implementation. We describe ways to leverage receiver resources for mitigating packet losses and sensing artifacts and present experimental results with ECG. Finally, we motivate multi-sensor fusion at the receiver and show that CS paradigm can be used to reduce sensor complexity with sloppy clock management schemes.