In this paper, we propose a novel framework for the smart healthcare system, where we employ the compressed sensing (CS) and the combination of the state-of-the-art machine learning based denoiser as well as the alternating direction of method of multipliers (ADMM) structure. This integration significantly simplifies the software implementation for the lowcomplexity encoder, thanks to the modular structure of ADMM. Furthermore, we focus on detecting fall down actions from image streams. Thus, teh primary purpose of thus study is to reconstruct the image as visibly clear as possible and hence it helps the detection step at the trained classifier. For this efficient smart health monitoring framework, we employ the trained binary convolutional neural network (CNN) classifier for the fall-action classifier, because this scheme is a part of surveillance scenario. In this scenario, we deal with the fallimages, thus, we compress, transmit and reconstruct the fallimages. Experimental results demonstrate the impacts of network parameters and the significant performance gain of the proposal compared to traditional methods.

中文翻译：

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基于机器学习的智能医疗监控框架

在本文中，我们为智能医疗保健系统提出了一个新的框架，我们采用压缩感知 (CS) 和最先进的基于机器学习的降噪器以及乘法器方法的交替方向的组合(ADMM) 结构。由于 ADMM 的模块化结构，这种集成显着简化了低复杂度编码器的软件实现。此外，我们专注于检测图像流中的跌倒动作。因此，这种研究的主要目的是尽可能清晰地重建图像，因此它有助于训练分类器的检测步骤。对于这个高效的智能健康监测框架，我们将训练有素的二元卷积神经网络 (CNN) 分类器用于跌倒动作分类器，因为这个方案是监视场景的一部分。在这种情况下，我们处理fallimages，因此，我们压缩、传输和重建fallimages。实验结果证明了网络参数的影响以及与传统方法相比该提议的显着性能增益。