Vsens: Incorporating XR into the Process of Collecting Virtual IMU Data
Fengzhou Liang, Tian Min, Chengshuo Xia, Yuta Sugiura
Abstract
Virtual Inertial Measurement Units (IMUs) offer a promising approach to generating synthetic motion data for training and evaluating human activity recognition (HAR) systems. However, existing virtual IMU workflows remain fragmented and technically demanding, requiring users to switch between 3D editors, scripting tools, and offline signal processing pipelines. These limitations hinder usability and iterative refinement for researchers and developers who design HAR systems. We present Vsens , an XR-based system that unifies virtual IMU configuration, visualization, and data synthesis within an immersive workspace. Vsens allows developers to directly manipulate sensor placements on digital avatars and observe synthesized IMU signals in real time. To understand how developers with different expertise levels benefit from such an environment, we conducted a user study with 20 HAR developers covering a broad range of experience levels. Results show that Vsens improves configuration efficiency and fosters deeper spatial understanding of sensor behavior across the participants. Beyond usability, our findings reveal how embodied interaction and real-time feedback inform the design of practical virtual data collection systems that foster more effective and human-centered workflows.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Related papers
- IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity RecognitionHyeokHyen Kwon, Catherine Tong, Harish Haresamudram, Yan Gao et al.UbiComp 2020 · 153 citations
- AvatAR: An Immersive Analysis Environment for Human Motion Data Combining Interactive 3D Avatars and TrajectoriesPatrick Reipschläger, Frederik Brudy, Raimund Dachselt, Justin Matejka et al.CHI 2022 · 41 citations
- SpatIO: Spatial Physical Computing Toolkit Based on Extended RealitySeung Hyeon Han, Yeeun Han, Kyeongho Park, Sangjun Lee et al.CHI 2025 · 6 citations
- Approaching the Real-World: Supporting Activity Recognition Training with Virtual IMU DataHyeokHyen Kwon, Bingyao Wang, Gregory D. Abowd, Thomas PlötzUbiComp 2021 · 48 citations
- Use Case Matters: Comparing the User Experience and Task Performance Across Tasks for Embodied Interaction in VRJonathan Tschanter, Christian Merz, Marie Luisa Fiedler, Carolin Wienrich et al.IEEE VR 2026
