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

arXiv: https://arxiv.org/abs/2411.07828 · HTML: https://mlsanity.com/paper/2411.07828/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 12 Nov 2024.

## Builds on (most cited first, 18 of 18)

- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, and New Methods](https://mlsanity.com/paper/1905.12853/) (2019)
- [IONet: Learning to Cure the Curse of Drift in Inertial Odometry](https://mlsanity.com/paper/1802.02209/) (2018)
- [IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds](https://mlsanity.com/paper/2304.12518/) (2023)
- [Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time](https://mlsanity.com/paper/1810.04703/) (2018)
- [SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environments](https://mlsanity.com/paper/2303.09095/) (2023)
- [RIDI: Robust IMU Double Integration](https://mlsanity.com/paper/1712.09004/) (2017)
- [FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space](https://mlsanity.com/paper/2310.20071/) (2023)
- [Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors](https://mlsanity.com/paper/2312.02196/) (2023)
- [COCOA: Cross Modality Contrastive Learning for Sensor Data](https://mlsanity.com/paper/2208.00467/) (2022)
- [Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference](https://mlsanity.com/paper/2001.04061/) (2020)
- [Learning from the Best: Contrastive Representations Learning Across Sensor Locations for Wearable Activity Recognition](https://mlsanity.com/paper/2210.01459/) (2022)
- [Timestamp-supervised Wearable-based Activity Segmentation and Recognition with Contrastive Learning and Order-Preserving Optimal Transport](https://mlsanity.com/paper/2310.09114/) (2023)
- [MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model](https://mlsanity.com/paper/2009.09404/) (2020)
- [Learning Responsibility Allocations for Safe Human-Robot Interaction with Applications to Autonomous Driving](https://mlsanity.com/paper/2303.03504/) (2023)
- [From Orthogonality to Dependency: Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals](https://mlsanity.com/paper/2405.16083/) (2024)
- [Robust Indoor Localization with Ranging-IMU Fusion](https://mlsanity.com/paper/2309.08803/) (2023)
- [Multi-level Contrast Network for Wearables-based Joint Activity Segmentation and Recognition](https://mlsanity.com/paper/2208.07547/) (2022)

## Built on by (most cited first, 3 of 3)

- [Suite-IN++: A FlexiWear BodyNet Integrating Global and Local Motion Features from Apple Suite for Robust Inertial Navigation](https://mlsanity.com/paper/2504.00438/) (2025)
- [Human Motion Estimation with Everyday Wearables](https://mlsanity.com/paper/2512.21209/) (2025)
- [IMU-HOI: A Symbiotic Framework for Coherent Human-Object Interaction and Motion Capture via Contact-Conscious Inertial Fusion](https://mlsanity.com/paper/2606.28604/) (2026)

## Abstract (arXiv)

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.