A Machine Learning Based Framework for the Smart Healthcare Monitoring

TL;DR

This paper introduces a novel smart healthcare monitoring framework that combines compressed sensing, advanced denoising, and ADMM to efficiently detect falls from image streams, improving reconstruction quality and detection accuracy.

Contribution

The study presents a new integrated framework using compressed sensing, machine learning denoisers, and ADMM for efficient fall detection in smart healthcare systems.

Findings

Significant performance improvements over traditional methods.

Effective fall detection from compressed and reconstructed images.

Enhanced image reconstruction quality for better classification.

Abstract

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.