mlsanityData to 14 Oct 2026

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

arXiv 2411.07828 ↗First posted 12 Nov 2024cs.LGcited by 3

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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.

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

18 of 33 references.

References · 18

  1. 2019Contrastive Multiview Codingcited by 1,301
  2. 2019RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, and New Methodscited by 73
  3. 2018IONet: Learning to Cure the Curse of Drift in Inertial Odometrycited by 55
  4. 2023IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbudscited by 54
  5. 2018Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Timecited by 47
  6. 2023SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environmentscited by 47
  7. 2017RIDI: Robust IMU Double Integrationcited by 41
  8. 2023FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Spacecited by 33
  9. 2023Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensorscited by 30
  10. 2022COCOA: Cross Modality Contrastive Learning for Sensor Datacited by 21
  11. 2020Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inferencecited by 16
  12. 2022Learning from the Best: Contrastive Representations Learning Across Sensor Locations for Wearable Activity Recognitioncited by 13
6 more
  1. 2023Timestamp-supervised Wearable-based Activity Segmentation and Recognition with Contrastive Learning and Order-Preserving Optimal Transportcited by 10
  2. 2020MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Modelcited by 9
  3. 2023Learning Responsibility Allocations for Safe Human-Robot Interaction with Applications to Autonomous Drivingcited by 7
  4. 2024From Orthogonality to Dependency: Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signalscited by 4
  5. 2023Robust Indoor Localization with Ranging-IMU Fusioncited by 3
  6. 2022Multi-level Contrast Network for Wearables-based Joint Activity Segmentation and Recognitioncited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Suite-IN++: A FlexiWear BodyNet Integrating Global and Local Motion Features from Apple Suite for Robust Inertial Navigationcited by 0
  2. 2025Human Motion Estimation with Everyday Wearablescited by 0
  3. 2026IMU-HOI: A Symbiotic Framework for Coherent Human-Object Interaction and Motion Capture via Contact-Conscious Inertial Fusioncited by 0