Resource-efficient and reliable long term wireless monitoring of the photoplethysmographic signal
Proceedings of the 2nd Conference on Wireless Health - WH '11
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
... ficient 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: MIMIC database and data collected using commercial wearable sensors. Results show significant bandwidth and energy savings due to data transmission reduction. The average reduction in data size for wearable sensor-based data is 300:1, while maintaining a diagnostic accuracy above 94%.