Suite-IN: Aggregating Motion Features from Apple Suite for Robust Inertial Navigation

TL;DR

Suite-IN is a multi-device deep learning framework that combines motion data from various Apple devices to improve inertial navigation accuracy and robustness in pedestrian positioning.

Contribution

It introduces a novel multi-device aggregation framework that leverages diverse motion data from Apple Suite to enhance inertial navigation performance.

Findings

Improved positioning accuracy over single-device methods

Enhanced robustness against diverse motion patterns

Effective aggregation of multi-device motion data

Abstract

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.