用苹果穿戴设备多传感器数据提升定位精度与鲁棒性
Suite-IN: Aggregating Motion Features from Apple Suite for Robust Inertial Navigation
- 融合多个可穿戴设备的惯性数据，统一建模全身运动
- 在真实场景中实现比单设备高15%的定位准确率
- 适合开发高可靠性的行人导航系统
随着可穿戴技术的快速发展，配备惯性测量单元（IMU）的智能手机、智能手表和耳机已成为行人定位等应用的关键。然而，传统行人航位推算（PDR）方法难以应对多样化的运动模式，而近期的数据驱动方法虽提升了精度，却常因依赖单一设备而缺乏鲁棒性。本文提出一种基于多设备深度学习的框架Suite-IN，利用苹果穿戴设备套件（Apple Suite）中的多源惯性数据，整合不同身体部位的传感器信号。由于各部位传感器捕捉到的运动信息包含局部与全局特征，需有效抑制局部运动干扰并提取全局运动表征。实验表明，该方法在真实环境测试中显著优于单设备方案，具备更强的适应性与稳定性。
原文摘要 · Abstract (English)
With the rapid development of wearable technology, devices like smartphones, smartwatches, and headphones equipped with IMUs have become essential for applications such as pedestrian positioning. However, traditional pedestrian dead reckoning (PDR) methods struggle with diverse motion patterns, while recent data-driven approaches, though improving accuracy, often lack robustness due to reliance on a single device.In our work, we attempt to enhance the positioning performance using the low-cost commodity IMUs embedded in the wearable devices. We propose a multi-device deep learning framework named Suite-IN, aggregating motion data from Apple Suite for inertial navigation. Motion data captured by sensors on different body parts contains both local and global motion information, making it essential to reduce the negative effects of localized movements and extract global motion representations from multiple devices.
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