TY - CHAP
T1 - Generalised and Versatile Connected Health Solution on the Zynq SoC
AU - Ganem Abunahia, Dina
AU - Raafat Abou Al Ola, Hala
AU - Ahmad Ismail, Tasnim
AU - Amira, Abbes
AU - Ait Si Ali, Amine
AU - Bensaali, Faycal
PY - 2017/12/31
Y1 - 2017/12/31
N2 - 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 platform 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.
AB - 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 platform 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.
U2 - 10.1007/978-3-319-69266-1_22
DO - 10.1007/978-3-319-69266-1_22
M3 - Chapter
SN - 9783319692654
T3 - Intelligent Systems and Applications
SP - 454
EP - 474
BT - Intelligent Systems and Applications
A2 - Bi, Yaxin
A2 - Kapoor, Supriya
A2 - Bhatia, Rahul
PB - Springer
ER -