A Deep Transfer Learning-based Edge Computing Method for Home Health Monitoring

TL;DR

This paper presents a transfer learning-based edge computing approach for home health monitoring that enables real-time, privacy-preserving analysis of visual sensor data using pre-trained CNN models on edge devices.

Contribution

It introduces a novel method combining transfer learning and edge computing for efficient, private, and real-time home health monitoring using visual sensors.

Findings

Enables on-site processing of visual data with minimal labeled data.

Reduces privacy and bandwidth concerns by avoiding data transmission.

Supports real-time health monitoring in an affordable manner.

Abstract

The health-care gets huge stress in a pandemic or epidemic situation. Some diseases such as COVID-19 that causes a pandemic is highly spreadable from an infected person to others. Therefore, providing health services at home for non-critical infected patients with isolation shall assist to mitigate this kind of stress. In addition, this practice is also very useful for monitoring the health-related activities of elders who live at home. The home health monitoring, a continuous monitoring of a patient or elder at home using visual sensors is one such non-intrusive sub-area of health services at home. In this article, we propose a transfer learning-based edge computing method for home health monitoring. Specifically, a pre-trained convolutional neural network-based model can leverage edge devices with a small amount of ground-labeled data and fine-tuning method to train the model. Therefore, on-site computing of visual data captured by RGB, depth, or thermal sensor could be possible in an affordable way. As a result, raw data captured by these types of sensors is not required to be sent outside from home. Therefore, privacy, security, and bandwidth scarcity shall not be issues. Moreover, real-time computing for the above-mentioned purposes shall be possible in an economical way.