IMUCoCo: Enabling Flexible On-Body IMU Placement for Human Pose Estimation and Activity Recognition
Haozhe Zhou, Riku Arakawa, Yuvraj Agarwal, Mayank Goel

TL;DR
IMUCoCo introduces a flexible framework that allows IMUs to be placed anywhere on the body for accurate human pose estimation and activity recognition, enhancing user experience and adaptability.
Contribution
The paper presents IMUCoCo, a novel method that maps IMU signals from arbitrary body locations into a unified feature space, enabling flexible sensor placement for motion analysis.
Findings
Supports accurate pose estimation across diverse sensor placements.
Enables user-defined and context-dependent IMU positioning.
Improves user experience by reducing placement constraints.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
