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

2025-08-03

AI Summary

Traditional IMU-based pose estimation and activity recognition rely on fixed sensor placements, severely limiting practicality and deployment flexibility. To address this, we propose IMUCoCo, the first framework to model the human body space using continuous coordinates—enabling anatomically grounded spatial representation of IMU signals from arbitrary positions and quantities. By mapping heterogeneous IMU configurations onto a unified, anatomy-aware feature space, IMUCoCo eliminates dependence on predefined mounting locations and supports dynamic re-deployment as well as scene-adaptive optimal placement recommendations. Integrated with a spatially aware deep network, it achieves high accuracy in both canonical and non-canonical sensor configurations. Experiments demonstrate that IMUCoCo significantly enhances deployment flexibility and cross-configuration generalization of IMU systems in real-world settings, establishing a novel, location-agnostic modeling paradigm for wearable sensing.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Enabling flexible IMU placement for pose estimation
• Mapping signals from any body location to features
• Supporting adaptable sensor use for motion sensing

Innovation

• Flexible IMU placement on any body part
• Unified feature space based on spatial coordinates
• Adaptable for various downstream motion tasks