| This thesis describes the design, development, and testing of an EMG-based patient monitoring system using the Zynq device. Zynq is a system on chip device designed by Xilinx which consists of an ARM dual cortex-A9 processor as well as an FPGA integrated into one chip. This work also analyzes the performance of image-processing algorithms on this system and compares that performance to more traditional PC-based systems. Image processing algorithms, such as Sobel edge detection, dilation and erosion, could be used in conjunction with a camera for the patient monitoring purposes. These algorithms often perform sub-optimally on processors because of their high computation demand, thus they are excellent candidates for the hardware acceleration available on an FPGA. This analysis shows that the performance of these algorithms in hardware using the Zynq-based architecture perform about 1800 times faster than the MATLAB implementation and 40 times faster than the OpenCV implementation on the PC. Moreover, the power consumption of the Zynq device proved to be about six and five times less than PC-based implementation using MATLAB and OpenCV respectively. Thus, the Zynq-based patient monitoring system proved to be both higher performance and lower power than a processor-based system. Both factors, performance and power consumption, are crucial for patient monitoring because of the demand for mobility and battery-based systems. |
