Suite-IN: Aggregating Motion Features from Apple Suite for Robust Inertial Navigation
This work addresses the need for more accurate and robust inertial navigation for users of wearable technology, representing an incremental improvement over existing data-driven methods.
The paper tackles the problem of robust pedestrian positioning using inertial measurement units (IMUs) in wearable devices by proposing Suite-IN, a multi-device deep learning framework that aggregates motion data from Apple Suite, resulting in enhanced positioning performance.
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.