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Examples of virtual environments with different levels of richness: Normal virtual environment (A); Empty virtual environment (B); Texture virtual environment (C); Dynamic virtual environment (D); Outside virtual environment (E); and Incongruent virtual environment (F) .
Immersive Virtual Reality (IVR) in robotic rehabilitation can offer users more naturalistic and engaging Virtual Environments (VEs).
In this project, we investigate the impact of embodiment and VE richness on skill acquisition and cognitive load of people after stroke to ensure that training conditions remain engaging without becoming overwhelming.
We employ a human-centered design approach, with the immersive virtual training environments co-created with end-users (e.g., physio- and occupational therapists, patients) to meet their experiences, clinical needs, recovery goals, and capabilities.
Using this, we aim to create immersive virtual training environments for IVR-based neurorehabilitation that maximize skill acquisition and enhance neuroplasticity.
Funding
Funding
VIDI
Publication(s)
Publication(s)
Cucinella, S.L., de Winter, J.C.F., Grauwmeijer, E. et al. Towards personalized immersive virtual reality neurorehabilitation: a human-centered design. J NeuroEngineering Rehabil 22, 7 (2025). https://doi.org/10.1186/s12984-024-01489-5
Cucinella, S.L., de Winter, J.C.F., Grauwmeijer, E. et al. Towards personalized immersive virtual reality neurorehabilitation: a human-centered design. J NeuroEngineering Rehabil 22, 7 (2025). https://doi.org/10.1186/s12984-024-01489-5
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