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Fall Detection Application on an ARM and FPGA Heterogeneous Computing Platform

Hong Thi Khanh Nguyen, Cecile Belleudy and Pham Van Tuan

Heterogeneous computing platform, Zynq- 7000 all programmable system-on-chip, not only accomplishes high efficiency solution in emerging the power consumption, execution time for implementing the Fall Detection application but also takes the advantage of Open source Computer Vision (OpenCV) libraries. The main goal of this work is to design and implement the Fall Detection Application on ARM Cortex A9 processor of Zynq Platform. Besides, real power consumption, estimated execution time and calculated energy are extracted from the implementation. Based on the observed measurements, pre-processing module based on morphology filter which occupies most execution time will be replaced by Sobel Filter. Then Sobel Filter will be implemented on hardware (FPGAs) part of the platform. The result analysis leads to a potential low power exploration of HW/SW co-design flow for performance improvement of the fall detection application