N. Petrellis
ABSTRACT
Current work evaluates the precision of low-cost medical sensors, which are incorporated in an e-health platform presented recently by the authors. The sensors' accuracy is an important issue that is investigated in this paper in order to highlight the medical cases where the low-cost developed e-health platform can be used in a fairly reliable way. Specifically, the sensor values obtained from the e-health platform were filtered using the methods of moving average window (MAW), Principal component analysis (PCA) and simplified Kalman filter. It is shown that although moving average window achieves a significant error reduction, the produced output introduces a latency penalty in the original sensor signal. Kalman filter exhibits worse performance from both the MAW and the PCA methods. Finally, it is demonstrated that the PCA method sustains advanced compression of about 30% while in the same time reduces the error of the primary signal measurement, thus improving the sensor accuracy.
- Zhang, Y., Sun, L., Song, H. and Cao X. (2014), Ubiquitous WSN for Healthcare: Recent Advances and Future Prospects, IEEE Internet of Things Journal, 1(4) p. 311--318.Google Scholar
- Patel, S., Park, H., Bonato, P., Chan, L., and Rodgers, M. (2012) A review of wearable sensors and systems with application in rehabilitation. Journal of Neuro Engineering and Rehabilitation p. 9--21.Google Scholar
- Gay, V., and Leijdekkers, P. (2007) A Health Monitoring System Using Smart Phones and Wearable Sensors, Int. Journal of ARM, 8(2), p. 29--35.Google Scholar
- Chan, V., Ray, P. and Parameswaran, N., (2008), Mobile e-Health monitoring: an agent-based approach, Communications, IET, Telemedicine and E-Health Communication Systems, 2(2), p. 223--230.Google ScholarCross Ref
- Mukherjee, S., Dolui, K. and Kanti Datta, S.(2014), Patient Health Management System using e-Health Monitoring Architecture, Advanced Computing Conference (IACC' 14), IEEE International, 21-22 Feb. 2014, p. 400--405.Google Scholar
- Khelil, A., Shaikh, F.K., Sheikh, A.A., Felemban, E., and Bojan, H. (2014), DigiAID: A Wearable Health Platform for Automated Self-tagging in Emergency Cases. 4th MobiHealth, Nov 14, Athens, Greece.Google ScholarCross Ref
- Cooking-Hacks (2013), {Online} Available at http://www.cooking-hacks.com/documentation/tutorials/ehealth-biometric-sensor-platform-arduino-raspberry-pi-medicalGoogle Scholar
- Antonopoulos, C., Panagiotakopoulos, T., Panagiotou, C., Touliatos, G., Koubias, S., Kameas, A. and Voros, N. (2015), On Developing a Novel Versatile Framework for Heterogeneous Home Monitoring WSN networks, EAI Endorsed Transactions on Pervasive Health and Technology, 1(1).Google ScholarCross Ref
- The Shimmer Platform (2008), {Online}. Available at http://www.shimmersensing.com/Google Scholar
- Petrellis, N., Birbas, M. and Gioulekas, F. (2015), The Front End Design of a Health Monitoring System, Proceedings of the 7th HAICTA 2015 conference, Kavala, Sep. 17--20, 2015, p. 426--436.Google Scholar
- Xu, H., Caramanis, C., and Sanghavi S. (2012), Robust pca via outlier pursuit, IEEE Tran. on Information Theory, vol. 58, no. 5. Google ScholarDigital Library
- McCoy, M., and Tropp, J.A. (2011), Two proposals for robust PCA using semide finite programming, Electronic Journal of Statistics, vol. 5, pp. 1123--1160.Google ScholarCross Ref
- Carmi, P. Gurfil, and Kanevsky, D. (2010), Methods for Sparse Signal Recovery Using Kalman Filtering With Embedded Pseudo-Measurement Norms and Quasi-Norms," IEEE Transactions on Signal Processing, vol. 58, no. 4, pp. 2405--2409. Google ScholarDigital Library
- Carmi, P. Gurfil, and D. Kanevsky, (2010) Methods for Sparse Signal Recovery Using Kalman Filtering With Embedded Pseudo-Measurement Norms and Quasi-Norms, IEEE Transactions on Signal Processing, vol. 58, no. 4, pp. 2405--2409. Google ScholarDigital Library
- Kanevsky, D., Carmi, A., Horesh, L., Gurfil, P., Ramabhadran, B., Sainath, T. N. (2010), Kalman Filtering for Compressed Sensing, 2010 Conference on Information Fusion.Google ScholarCross Ref
- Chou, Y.-L (1975)., Statistical Analysis, Holt International, ISBN 0-03-089422-0Google Scholar
Index Terms
- Compressing and Filtering Medical Data in a Low Cost Health Monitoring System
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