IMUCoCo: Enabling Flexible On-Body IMU Placement for Human Pose Estimation and Activity Recognition

• URL: http://arxiv.org/abs/2508.01894v1
• Date: Sun, 03 Aug 2025 19:09:20 GMT
• Title: IMUCoCo: Enabling Flexible On-Body IMU Placement for Human Pose Estimation and Activity Recognition
• Authors: Haozhe Zhou, Riku Arakawa, Yuvraj Agarwal, Mayank Goel,
• Abstract summary: We introduce IMU over Continuous Coordinates (IMUCoCo), a novel framework that maps signals from a variable number of IMUs placed on the body surface into a unified feature space.<n>Our evaluations demonstrate that IMUCoCo supports accurate pose estimation in a wide range of typical and atypical sensor placements.
• Score: 23.514920388531184
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: IMUs are regularly used to sense human motion, recognize activities, and estimate full-body pose. Users are typically required to place sensors in predefined locations that are often dictated by common wearable form factors and the machine learning model's training process. Consequently, despite the increasing number of everyday devices equipped with IMUs, the limited adaptability has seriously constrained the user experience to only using a few well-explored device placements (e.g., wrist and ears). In this paper, we rethink IMU-based motion sensing by acknowledging that signals can be captured from any point on the human body. We introduce IMU over Continuous Coordinates (IMUCoCo), a novel framework that maps signals from a variable number of IMUs placed on the body surface into a unified feature space based on their spatial coordinates. These features can be plugged into downstream models for pose estimation and activity recognition. Our evaluations demonstrate that IMUCoCo supports accurate pose estimation in a wide range of typical and atypical sensor placements. Overall, IMUCoCo supports significantly more flexible use of IMUs for motion sensing than the state-of-the-art, allowing users to place their sensors-laden devices according to their needs and preferences. The framework also supports the ability to change device locations depending on the context and suggests placement depending on the use case.

Related papers

• Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation [65.30763239365928]
  We introduce Genie Envisioner (GE), a unified world foundation platform for robotic manipulation.<n>GE integrates policy learning, evaluation, and simulation within a single video-generative framework.
  arXiv  Detail & Related papers  (2025-08-07T17:59:44Z)
• BaroPoser: Real-time Human Motion Tracking from IMUs and Barometers in Everyday Devices [12.374794959250828]
  We present BaroPoser, the first method that combines IMU and barometric data recorded by a smartphone and a smartwatch to estimate human pose and global translation in real time.<n>By leveraging barometric readings, we estimate sensor height changes, which provide valuable cues for both improving the accuracy of human pose estimation and predicting global translation on non-flat terrain.
  arXiv  Detail & Related papers  (2025-08-05T10:46:59Z)
• CAFuser: Condition-Aware Multimodal Fusion for Robust Semantic Perception of Driving Scenes [56.52618054240197]
  We propose a novel, condition-aware multimodal fusion approach for robust semantic perception of driving scenes.<n>Our method, CAFuser, uses an RGB camera input to classify environmental conditions and generate a Condition Token.<n>Our model significantly improves robustness and accuracy, especially in adverse-condition scenarios.
  arXiv  Detail & Related papers  (2024-10-14T17:56:20Z)
• Masked Video and Body-worn IMU Autoencoder for Egocentric Action Recognition [24.217068565936117]
  We present a novel method for action recognition that integrates motion data from body-worn IMUs with egocentric video. To model the complex relation of multiple IMU devices placed across the body, we exploit the collaborative dynamics in multiple IMU devices. Experiments show our method can achieve state-of-the-art performance on multiple public datasets.
  arXiv  Detail & Related papers  (2024-07-09T07:53:16Z)
• Neural 5G Indoor Localization with IMU Supervision [63.45775390000508]
  Radio signals are well suited for user localization because they are ubiquitous, can operate in the dark and maintain privacy. Many prior works learn mappings between channel state information (CSI) and position fully-supervised. In this work, this requirement is relaxed by using pseudo-labels during deployment, which are calculated from an inertial measurement unit (IMU)
  arXiv  Detail & Related papers  (2024-02-15T13:51:21Z)
• Advancing Location-Invariant and Device-Agnostic Motion Activity Recognition on Wearable Devices [6.557453686071467]
  We conduct a comprehensive evaluation of the generalizability of motion models across sensor locations. Our analysis highlights this challenge and identifies key on-body locations for building location-invariant models. We present deployable on-device motion models reaching 91.41% frame-level F1-score from a single model irrespective of sensor placements.
  arXiv  Detail & Related papers  (2024-02-06T05:10:00Z)
• DiffusionPoser: Real-time Human Motion Reconstruction From Arbitrary Sparse Sensors Using Autoregressive Diffusion [10.439802168557513]
  Motion capture from a limited number of body-worn sensors has important applications in health, human performance, and entertainment. Recent work has focused on accurately reconstructing whole-body motion from a specific sensor configuration using six IMUs. We propose a single diffusion model, DiffusionPoser, which reconstructs human motion in real-time from an arbitrary combination of sensors.
  arXiv  Detail & Related papers  (2023-08-31T12:36:50Z)
• Multi-Visual-Inertial System: Analysis, Calibration and Estimation [26.658649118048032]
  We study state estimation of multi-visual-inertial systems (MVIS) and develop sensor fusion algorithms. We are interested in the full calibration of the associated visual-inertial sensors.
  arXiv  Detail & Related papers  (2023-08-10T02:47:36Z)
• IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds [41.8359507387665]
  We explore the feasibility of estimating body pose using IMUs already in devices that many users own. Our pipeline receives whatever subset of IMU data is available, potentially from just a single device, and produces a best-guess pose.
  arXiv  Detail & Related papers  (2023-04-25T02:13:24Z)
• Transformer Inertial Poser: Attention-based Real-time Human Motion Reconstruction from Sparse IMUs [79.72586714047199]
  We propose an attention-based deep learning method to reconstruct full-body motion from six IMU sensors in real-time. Our method achieves new state-of-the-art results both quantitatively and qualitatively, while being simple to implement and smaller in size.
  arXiv  Detail & Related papers  (2022-03-29T16:24:52Z)
• Attention-Based Sensor Fusion for Human Activity Recognition Using IMU Signals [4.558966602878624]
  We propose a novel attention-based approach to human activity recognition using multiple IMU sensors worn at different body locations. An attention-based fusion mechanism is developed to learn the importance of sensors at different body locations and to generate an attentive feature representation. The proposed approach is evaluated using five public datasets and it outperforms state-of-the-art methods on a wide variety of activity categories.
  arXiv  Detail & Related papers  (2021-12-20T17:00:27Z)
• SensiX: A Platform for Collaborative Machine Learning on the Edge [69.1412199244903]
  We present SensiX, a personal edge platform that stays between sensor data and sensing models. We demonstrate its efficacy in developing motion and audio-based multi-device sensing systems. Our evaluation shows that SensiX offers a 7-13% increase in overall accuracy and up to 30% increase across different environment dynamics at the expense of 3mW power overhead.
  arXiv  Detail & Related papers  (2020-12-04T23:06:56Z)
• Deep Soft Procrustes for Markerless Volumetric Sensor Alignment [81.13055566952221]
  In this work, we improve markerless data-driven correspondence estimation to achieve more robust multi-sensor spatial alignment. We incorporate geometric constraints in an end-to-end manner into a typical segmentation based model and bridge the intermediate dense classification task with the targeted pose estimation one. Our model is experimentally shown to achieve similar results with marker-based methods and outperform the markerless ones, while also being robust to the pose variations of the calibration structure.
  arXiv  Detail & Related papers  (2020-03-23T10:51:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.