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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.21696 |
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| _version_ | 1866915267658907648 |
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| author | Rossi, Cristina Varghese, Rini Bastian, Amy J |
| author_facet | Rossi, Cristina Varghese, Rini Bastian, Amy J |
| contents | Virtual reality (VR) is increasingly used to enhance the ecological validity of motor control and learning studies by providing immersive, interactive environments with precise motion tracking. However, designing realistic VR-based motor tasks remains complex, requiring advanced programming skills and limiting accessibility in research and clinical settings. MovementVR is an open-source platform designed to address these challenges by enabling the creation of customizable, naturalistic reaching tasks in VR without coding expertise. It integrates physics-based hand-object interactions, real-time hand tracking, and flexible experimental paradigms, including motor adaptation and reinforcement learning. The intuitive graphical user interface (GUI) allows researchers to customize task parameters and paradigm structure. Unlike existing platforms, MovementVR eliminates the need for scripting while supporting extensive customization and preserving ecological validity and realism. In addition to reducing technical barriers, MovementVR lowers financial constraints by being compatible with consumer-grade VR headsets. It is freely available with comprehensive documentation, facilitating broader adoption in movement research and rehabilitation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_21696 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MovementVR: An open-source tool for the study of motor control and learning in virtual reality Rossi, Cristina Varghese, Rini Bastian, Amy J Quantitative Methods Virtual reality (VR) is increasingly used to enhance the ecological validity of motor control and learning studies by providing immersive, interactive environments with precise motion tracking. However, designing realistic VR-based motor tasks remains complex, requiring advanced programming skills and limiting accessibility in research and clinical settings. MovementVR is an open-source platform designed to address these challenges by enabling the creation of customizable, naturalistic reaching tasks in VR without coding expertise. It integrates physics-based hand-object interactions, real-time hand tracking, and flexible experimental paradigms, including motor adaptation and reinforcement learning. The intuitive graphical user interface (GUI) allows researchers to customize task parameters and paradigm structure. Unlike existing platforms, MovementVR eliminates the need for scripting while supporting extensive customization and preserving ecological validity and realism. In addition to reducing technical barriers, MovementVR lowers financial constraints by being compatible with consumer-grade VR headsets. It is freely available with comprehensive documentation, facilitating broader adoption in movement research and rehabilitation. |
| title | MovementVR: An open-source tool for the study of motor control and learning in virtual reality |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2504.21696 |