A DEEP TRANSFER LEARNING BASED EDGE COMPUTING METHOD FOR HOME HEALTH MONITORING

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
noncritical 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 nonintrusive 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, realtime computing for the above-mentioned
purposes shall be possible in an economical
way.