Human identification in health care systems using mobile edge computing

Abstract

Mobile edge computing (MEC) is a new technology that provides an IT service environment and cloud-computing resource at the edge of the mobile network, aiming to improve the user experience by reducing latency and delivering highly efficient services. A mobile health care system is a typical case of MEC. Recent advances in mobile health technology enable us to automatically record the states and behaviors of patients in hospitals by identifying their faces. Instead of using a centralized server or cloud to process these heavy loading analyses, MEC analyzes the patients' behavior locally in their rooms to enable quick response to their accidents and reduce the storage size of large-scale patient information. By using MEC, only one frontal face for each patient is available in the gallery to reduce the local computational power. Although a patient's face can be captured from any other pose, doing so challenges the face-identification algorithm. Several studies have been published to solve this problem, but most of them rely on pose estimation and landmark detection. In this paper, we propose a deeper convolutional neural network to extract the pose-invariant face features and synthesize the virtual poses simultaneously, aiming to develop a landmark-free and pose-estimation-free frontal-face synthesis system. The deeper network was divided into multiple overlapped local networks, each one trained to synthesize a small pose change, such as one larger than 45 • to30 •. The local networks were jointly trained to synthesize the frontal face from a nonfrontal pose in a progressive manner. By stacking multiple local networks, we were able to extract more robust pose-invariant features, to generate multiple virtual poses before the frontal face was synthesized. The pose-invariant features and virtual poses were incorporated to identify the face across poses. The proposed method was evaluated on the CAS-PEAL dataset with 16 layers and fine-tuned on the FERET dataset with 12 layers. The experimental results showed our network achieved impressive performance for pose-unconstrained face recognition, which can be applied for human identification in healthcare systems using MEC.

Bibliometric Analysis of Health Technology Research: 1990~2020

“…Cloud computing is also widely used in technology integration to support the development of new algorithms or technologies. Yu proposed a new algorithm of human identification for healthcare applications using mobile edge computing (MEC), and the algorithm realizes efficient patient identification by running cloud servers at the edge of mobile networks [ 93 ]. Lakshmi proposed a method to realize remote patient monitoring through cloud-based IoT medical sensors, collect biomedical data through wearable sensors, and directly transmit patient data to cloud intensive care systems to remotely monitor health status [ 94 ].…”

Learning‐based deep neural network inference task offloading in multi‐device and multi‐server collaborative edge computing

“…The continuous integration of DL and edge computing has created an emerging interdiscipline, namely edge intelligence, which is receiving tremendous amounts of interest recently. [9][10][11][12] For example, Yu et al 9 studied mobile edge computing based human identification for health care. Wang et al 10 studied the application of edge intelligence in emergency communication systems.…”

“…Using edge computing to boost the performance of DL services has become a trend. The continuous integration of DL and edge computing has created an emerging interdiscipline, namely edge intelligence, which is receiving tremendous amounts of interest recently 9‐12 . For example, Yu et al 9 studied mobile edge computing based human identification for health care.…”