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

• URL: http://arxiv.org/abs/2105.02960v1
• Date: Wed, 28 Apr 2021 17:01:41 GMT
• Title: A Deep Transfer Learning-based Edge Computing Method for Home Health Monitoring
• Authors: Abu Sufian, Changsheng You and Mianxiong Dong
• Abstract summary: Home health monitoring is a non-intrusive sub-area of health services at home. A pre-trained convolutional neural network-based model can leverage edge devices with a small amount of ground-labeled data. On-site computing of visual data captured by RGB, depth, or thermal sensor could be possible in an affordable way.
• Score: 20.17535790986831
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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