Summary of Transfer Learning Of Rssi to Improve Indoor Localisation Performance, by Thanaphon Suwannaphong et al.
Transfer Learning of RSSI to Improve Indoor Localisation Performance
by Thanaphon Suwannaphong, Ryan McConville, Ian Craddock
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach for enhancing the performance and scalability of health monitoring systems using Bluetooth Low Energy (BLE) Received Signal Strength Indicator (RSSI)-based monitoring. To address the time-consuming task of collecting annotated training data, the authors develop Conditional Generative Adversarial Networks (ConGAN)-based augmentation combined with their transfer learning framework (T-ConGAN). This enables the transfer of generic RSSI information between different homes, reducing the need for annotation in each home. The proposed T-ConGAN enhances the macro F1 score of room-level indoor localisation by up to 12.2%, with a remarkable 51% improvement in challenging areas such as stairways or outside spaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In-home health monitoring systems are crucial for tracking patient conditions, and this paper introduces a way to make them more effective. The challenge is collecting enough data without overwhelming patients. To solve this, the researchers developed a new method using something called Conditional Generative Adversarial Networks (ConGAN). This allows information from one home to be used in another, even if the setup is different. The result is better location tracking and less work for patients. |
Keywords
» Artificial intelligence » F1 score » Tracking » Transfer learning
