MyoInteract: A Framework for Fast Prototyping of Biomechanical HCI Tasks using Reinforcement Learning
arXiv:2602.15245v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL)-based biomechanical simulations have the potential to revolutionise HCI research and interaction design, but currently lack usability and interpretability. Using the Human Action Cycle as a design lens, we identify key limitations of biomechanical RL frameworks and develop MyoInteract, a novel framework for fast prototyping of biomechanical HCI tasks. MyoInteract allows designers to setup tasks, user models, and training parameters from an easy-to-use GUI within minutes. It trains and evaluates muscle-actuate
The increasing sophistication of reinforcement learning and biomechanical simulation is enabling more accessible tools for human-computer interaction design.
This framework offers a significant leap in prototyping and designing interfaces that directly leverage human physiology, potentially revolutionizing fields from prosthetics to gaming.
HCI design can now integrate complex biomechanical models and reinforcement learning more rapidly, moving beyond abstract interaction paradigms to embodied ones.
- · HCI Researchers
- · Interaction Designers
- · Prosthetics Developers
- · Gaming Industry
- · Traditional HCI prototyping tools
- · Purely abstract UI/UX methodologies
Faster development and iteration of biomechanically-aware human-computer interfaces.
Emergence of new interaction modalities that leverage detailed user physiological feedback and control.
Potential for hyper-personalized human augmentation devices and interfaces that adapt to individual biomechanics over time.
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Read at arXiv cs.AI