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

arXiv: https://arxiv.org/abs/2105.02960 · HTML: https://mlsanity.com/paper/2105.02960/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 41 references that are in the map.
- First posted on arXiv 28 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [Convergence of Edge Computing and Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/1907.08349/) (2019)
- [Privacy-Preserving Action Recognition for Smart Hospitals using Low-Resolution Depth Images](https://mlsanity.com/paper/1811.09950/) (2018)
- [Vision-Based Gait Analysis for Senior Care](https://mlsanity.com/paper/1812.00169/) (2018)

## Built on by (most cited first, 4 of 4)

- [Green Edge AI: A Contemporary Survey](https://mlsanity.com/paper/2312.00333/) (2023)
- [Vision-based Human Fall Detection Systems using Deep Learning: A Review](https://mlsanity.com/paper/2207.10952/) (2022)
- [Vision Language Models for Dynamic Human Activity Recognition in Healthcare Settings](https://mlsanity.com/paper/2510.21424/) (2025)
- [Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices](https://mlsanity.com/paper/2203.11295/) (2022)

## Abstract (arXiv)

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.