Sidharth Nabar
ABSTRACT
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%.
- http://www.physionet.org/.Google Scholar
- http://math.nist.gov/mcsd/savg/papers/15-08-0780-09-0006-tg6-channel-model.pdf.Google Scholar
- J. Allen. Photoplethysmography and its application in clinical physiological measurement. Physiological measurement, 28:R1, 2007.Google ScholarCross Ref
- A. Alomainy et al. Statistical analysis and performance evaluation for on-body radio propagation with microstrip patch antennas. Antennas and Propagation, IEEE Transactions on, 55(1):245--248, 2007.Google ScholarCross Ref
- H. Asada et al. Mobile monitoring with wearable photoplethysmographic biosensors. Engineering in Medicine and Biology Magazine, IEEE, 22(3):28--40, 2003.Google ScholarCross Ref
- P. Baheti and H. Garudadri. An ultra low power pulse oximeter sensor based on compressed sensing. 2009 Body Sensor Networks, pages 144--148, 2009. Google ScholarDigital Library
- A. Banerjee et al. Challenges of implementing cyber-physical security solutions in body area networks. In Proceedings of the Fourth International Conference on Body Area Networks, page 18. ICST, 2009. Google ScholarDigital Library
- D. Birchfield. Generative model for the creation of musical emotion, meaning, and form. In Proceedings of the 2003 ACM SIGMM workshop on Experiential telepresence, pages 99--104. ACM, 2003. Google ScholarDigital Library
- S. Cotton and W. Scanlon. An experimental investigation into influence of user state and environment on fading characteristics in wireless body area networks at 2.45 GHz. Wireless Communications, IEEE Transactions on, 2009. Google ScholarDigital Library
- V. Crabtree and P. Smith. Physiological models of the human vasculature and photoplethysmography. Electronic Systems and Control Division Research, Department of Electronic and Electrical Engineering, Loughborough University, pages 60--63, 2003.Google Scholar
- A. Deshpande et al. Model-driven data acquisition in sensor networks. In Proceedings of the Thirtieth international conference on Very large data bases-Volume 30, page 599. VLDB Endowment, 2004. Google ScholarDigital Library
- R. Fletcher et al. iCalm: Wearable sensor and network architecture for wirelessly communicating and logging autonomic activity. Information Technology in Biomedicine, IEEE Transactions on, 14(2):215--223, 2010. Google ScholarDigital Library
- B. Gyselinckx et al. Human++: autonomous wireless sensors for body area networks. In Custom Integrated Circuits Conference, 2005. Proceedings of the IEEE 2005, pages 13--19. IEEE, 2005.Google ScholarCross Ref
- P. McSharry et al. A dynamical model for generating synthetic electrocardiogram signals. Biomedical Engineering, IEEE Transactions on, 50(3):289--294, 2003.Google Scholar
- Y. Mendelson, R. Duckworth, and G. Comtois. A wearable reflectance pulse oximeter for remote physiological monitoring. In Engineering in Medicine and Biology Society, 2006. EMBS'06. 28th Annual International Conference of the IEEE, pages 912--915. IEEE, 2006.Google ScholarCross Ref
- S. Millasseau et al. Contour analysis of the photoplethysmographic pulse measured at the finger. Journal of hypertension, 24(8):1449, 2006.Google ScholarCross Ref
- S. Nabar et al. GeM-REM: Generative Model-driven Resource efficient ECG Monitoring in Body Sensor Networks. In Proceedings of International Conference on Body Sensor Networks (BSN). IEEE, 2011 http://ee.washington.edu/research/nsl/papers/GeMREM.pdf. Google ScholarDigital Library
- C. Pimentel and I. Blake. Modeling burst channels using partitioned Fritchman's Markov models. Vehicular Technology, IEEE Transactions on, 47(3):885--899, 1998.Google Scholar
- M. Quwaider et al. Body-posture-based dynamic link power control in wearable sensor networks. Communications Magazine, IEEE, 48(7):134--142, 2010. Google ScholarDigital Library
- K. Reddy et al. Use of fourier series analysis for motion artifact reduction and data compression of PPG signals. Instrumentation and Measurement, IEEE Transactions on, 58(5):1706--1711, 2009.Google Scholar
- A. Reisner et al. Utility of the photoplethysmogram in circulatory monitoring. Anesthesiology, 108(5):950, 2008.Google ScholarCross Ref
- N. Selvaraj et al. Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography. Journal of Medical Engineering & Technology, 32(6):479--484, 2008.Google ScholarCross Ref
- S. Shirouzu et al. Circulatory parameter extraction from digital plethysmogram. i. waveform analysis of digital plethysmogram. In Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE, volume 6, pages 3087--3089, 1998.Google ScholarCross Ref
- B. Zhen et al. Characterization and Modeling of Dynamic On-Body Propagation at 4.5 GHz. Antennas and Wireless Propagation Letters, IEEE, 8:1263--1267, 2009.Google ScholarCross Ref
Index Terms
- Resource-efficient and reliable long term wireless monitoring of the photoplethysmographic signal
Recommendations
Energy-Efficient Long-term Continuous Personal Health Monitoring
Continuous health monitoring using wireless body area networks of implantable and wearable medical devices (IWMDs) is envisioned as a transformative approach to healthcare. Rapid advances in biomedical sensors, low-power electronics, and wireless ...
Towards a Novel In-community Healthcare Monitoring System over Wireless Sensor Networks
ICICSE '08: Proceedings of the 2008 International Conference on Internet Computing in Science and EngineeringRecently, new technological advances in biosensors, Micro Electro Mechanical Systems (MEMS) and wireless communications have enabled the design of low-cost, miniature, lightweight and intelligent physiological sensor nodes. These nodes, capable of ...
Wireless body sensor design for intra-vaginal temperature monitoring
BodyNets '10: Proceedings of the Fifth International Conference on Body Area NetworksSensor nodes are small devices able to collect and retrieve sensorial data. The use of these sensors for medical purposes offers valuable contributions to improve patients' healthcare, both for diagnosis and therapeutics monitoring. An important and ...
Comments