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Purpose -This paper aims to design a vision-based non-contact real-time accurate heart rate (HR) measurement framework for home nursing assistant.Design/methodology/approach -The study applied Second-Order Blind Signal Identification (SOBI) algorithm to extract remote HR signal and analyzed it with Fast Fourier Transform (FFT). Multiple regions of interest are chosen and analyzed to obtain a more accurate result.Findings -An accurate non-contact hear rate (HR) measurement framework is proposed and proved to be efficient.Originality/value -The contributions of this HR measurement framework are as follows: accurate measurement of HR, real-time performance, robust under various scenes such as conversation, lightweight computation which is suitable and necessary for home nursing assistance. This framework is designed to be flexibly used in various real-life scenes such as domestic health assistance and affectively intelligent agents and is proved to be robust under such scenes.

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Transposed convolution has been prevailing in convolutional neural networks (CNNs), playing an important role in multiple scenarios such as image segmentation and back-propagation process of training CNNs. This mainly benefits from the ability to up-sample the input feature maps by interpolating new information from the input feature pixels. However, the backward-stencil computation constrains its performance and hindered its wide application in diverse platforms. Moreover, in contrast to the efforts on accelerating the convolution, there is a rare investigation on the acceleration of transposed convolution that is identically compute-intensive as the former. For acceleration of transposed convolution, we propose an intermediate-centric dataflow scheme, in which we decouple the generation of the intermediate patch from its further process, aiming to efficiently perform the backward-stencil computation . The intermediate-centric dataflow breaks the transposed convolution into several phases/stages, achieving feeding the input feature maps and performing the backward-stencil computation in a pipelining manner. It also provides four-degree computation parallelism and efficient data reuse of input feature maps/weights. Furthermore, we also theoretically analyze the irregular data dependence leveraging the polyhedral model, which constrains the parallel computing of transposed convolution. Additionally, we devise an optimization problem to explore the design space and automatically generate the optimal design configurations for different transposed convolutional layers and hardware platforms. By selecting the representative transposed convolutional layers from DCGAN, FSRCNN, and FCN, we generate the corresponding accelerator arrays of intermediate-centric dataflow on the Xilinx Alveo U200 platform and reach the performance of 3.92 TOPS, 2.72 TOPS, and 4.76 TOPS, respectively.