Improving real-time physiological signs estimation using plethysmography wave and heterogeneous embedded system

Our work presents a real-time embedded implementation of a proposed approach for physiological signs monitoring, such as heart and breathing rates, using a Photoplethysmography signal (PPG) retrieved from digital RGB cameras. The proposed algorithm was implemented in an embedded architecture to assess both the processing time and algorithmic complexity. The proposed method is based on image processing techniques to extract the noisy PPG signal and signal processing, filtering, and decomposition algorithm to estimate the instantaneous vitals indicators. On the embedded implementation side, the common criteria that must be studied are the accuracy of the result estimation, processing time optimisation, and hardware-software adoption. The latter standard is met by the hardware-software co-design concept which will lead to adopting the algorithm's layers with an embedded platform architecture. On our side, we will principally use the High-Level Synthesis (HLS) as a parallel programming language and the computing homogeneous/heterogeneous devices (CPU/GPU). Our proposed optimised algorithm's implementation offers a gain of x5.05, x24.96, and x36.68 compared with the native version using MATLAB and the optimised version using C/C++, OpenMP, and OpenCL tool, respectively, in some functional blocks.