Generative model for the creation of musical emotion, meaning, and form
ETP '03 Proceedings of the 2003 ACM SIGMM workshop on Experiential telepresence
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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
IEEE Transactions on Wireless Communications
Challenges of implementing cyber-physical security solutions in body area networks
BodyNets '09 Proceedings of the Fourth International Conference on Body Area Networks
Body-posture-based dynamic link power control in wearable sensor networks
IEEE Communications Magazine
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
GeM-REM: Generative Model-Driven Resource Efficient ECG Monitoring in Body Sensor Networks
BSN '11 Proceedings of the 2011 International Conference on Body Sensor Networks
Energy-efficient long term physiological monitoring
Proceedings of the 2nd Conference on Wireless Health
Power constrained sensor sample selection for improved form factor and lifetime in localized BANs
Proceedings of the conference on Wireless Health
PEES: physiology-based end-to-end security for mHealth
Proceedings of the 4th Conference on Wireless Health
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Wearable photoplethysmogram (PPG) sensors are extensively used for remote monitoring of blood oxygen level and flow rate in numerous pervasive healthcare applications with diverse quality of service requirements. These sensors operate under severe resource constraints and communicate over an adverse wireless channel with human body-induced path loss and mobility-caused fading. In this paper, we take a generative model-based data collection approach towards achieving energy-efficient and reliable PPG monitoring. We develop two models that can generate synthetic PPG signals given a set of input parameters. These generative models are then used to design and implement a resource-efficient, reliable data reporting method for wireless PPG sensors. We investigate the performance of our method under realistic wireless channel error models and provide methods to improve accuracy at a marginal energy cost. We implement the proposed technique using existing sensor platforms and evaluate its performance on two datasets: the MIMIC database and data collected using commercial wearable sensors. Results for wearable sensor-based data show bandwidth and communication energy savings of 300:1, while maintaining a diagnostic accuracy above 94%.