Abstract
This chapter presents a generalized and versatile connected health solution for patient monitoring. It consists of a mobile system that can be used at home, an ambulance and a hospital. The system uses the Shimmer sensor device to collect three axes (x, y and z) accelerometer data as well as electrocardiogram signals. The accelerometer data is used to implement a fall detection system using the k-Nearest Neighbors classifier. The classification algorithm is implemented on various platforms including a PC and the Zynq system on chip platform where both programmable logic and processing system of the Zynq are explored. In addition, the electrocardiogram signals are used to extract vital information, the signals are also encrypted using the Advanced Encryption Standard and sent wirelessly using Wi-Fi for further processing. Implementation results have shown that the best overall accuracy reaches 90% for the fall detection while meeting real-time performances when implemented on the Zynq and while using only 48% of Look-up Tables and 22% of Flip-Flops available on chip.
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Acknowledgements
This paper was made possible by National Priorities Research Program (NPRP) grant No. 5-080-2-028 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
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Ganem Abunahia, D., Raafat Abou Al Ola, H., Ahmad Ismail, T., Amira, A., Ait Si Ali, A., Bensaali, F. (2018). Generalised and Versatile Connected Health Solution on the Zynq SoC. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2016. Studies in Computational Intelligence, vol 751. Springer, Cham. https://doi.org/10.1007/978-3-319-69266-1_22
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