用迁移学习共享不同家庭的蓝牙信号数据，减少标注工作量
Transfer Learning of RSSI to Improve Indoor Localisation Performance
- 用条件生成对抗网络增强信号数据，实现跨房屋迁移
- 房间级定位宏平均F1提升12.2%，楼梯等区域提升51%
- 适合需要少标注、可扩展的居家健康监测系统
随着健康监测系统需求增长，居家定位对追踪患者状态至关重要。每栋房屋的空间特性各异，需为基于蓝牙低功耗（BLE）接收信号强度指示（RSSI）的监测系统标注数据，但收集标注数据耗时，尤其对行动受限的患者而言更难。为此，我们提出基于条件生成对抗网络（ConGAN）的数据增强方法，结合迁移学习框架（T-ConGAN），实现不同房屋间通用RSSI信息的迁移，即使实验协议不同亦可适用。该方法显著降低每户标注需求，提升系统性能与可扩展性。我们首次证明了BLE RSSI数据可在不同房屋间共享，且共享信息能改善室内定位表现。T-ConGAN使房间级定位的宏平均F1得分最高提升12.2%，在楼梯间或室外等挑战区域提升达51%。该先进RSSI增强模型大幅提升了居家健康监测系统的鲁棒性。
原文摘要 · Abstract (English)
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.
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