# Transfer Learning of RSSI to Improve Indoor Localisation Performance

arXiv: https://arxiv.org/abs/2412.09292 · HTML: https://mlsanity.com/paper/2412.09292/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 31 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 2 of 2)

- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Detecting Signatures of Early-stage Dementia with Behavioural Models Derived from Sensor Data](https://mlsanity.com/paper/2007.03615/) (2020)

## Abstract (arXiv)

With the growing demand for health monitoring systems, in-home localisation is essential for tracking patient conditions. The unique spatial characteristics of each house required annotated data for Bluetooth Low Energy (BLE) Received Signal Strength Indicator (RSSI)-based monitoring system. However, collecting annotated training data is time-consuming, particularly for patients with limited health conditions. To address this, we propose Conditional Generative Adversarial Networks (ConGAN)-based augmentation, combined with our transfer learning framework (T-ConGAN), to enable the transfer of generic RSSI information between different homes, even when data is collected using different experimental protocols. This enhances the performance and scalability of such intelligent systems by reducing the need for annotation in each home. We are the first to demonstrate that BLE RSSI data can be shared across different homes, and that shared information can improve the indoor localisation performance. Our 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. This state-of-the-art RSSI augmentation model significantly enhances the robustness of in-home health monitoring systems.