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.

翻译：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、智能手表和耳机等设备已成为行人定位等应用的关键组成部分。然而，传统的行人航位推算（PDR）方法难以应对多样的运动模式，而近期基于数据驱动的方法虽提升了精度，却常因依赖单一设备而缺乏鲁棒性。在本研究中，我们尝试利用嵌入可穿戴设备的低成本商用IMU来提升定位性能。我们提出了一个名为Suite-IN的多设备深度学习框架，通过聚合苹果设备套件的运动数据进行惯性导航。由身体不同部位传感器捕获的运动数据同时包含局部与全局运动信息，因此关键在于减少局部运动的负面影响，并从多个设备中提取全局运动表征。