Interaction in Assistive Robotics: A Radical Constructivist Design Framework.

Area of Science:

• Biomedical Engineering
• Neuroscience
• Human-Computer Interaction

Background:

• Muscle-based control (myocontrol) for assistive devices, such as upper-limb prostheses, remains unreliable despite advancements.
• Current myocontrol systems struggle to provide sufficient functionality, limiting user adoption and device utility.

Purpose of the Study:

• To propose a novel myocontrol framework based on Radical Constructivism, moving beyond simple movement prediction.
• To enable myocontrol systems to acquire and forget knowledge on demand, enhancing adaptability and user control.

Main Methods:

• Developing a unifying framework grounded in Radical Constructivism principles.
• Designing a user interface and interaction strategy that supports on-demand knowledge acquisition and forgetting.

Main Results:

• The proposed framework facilitates the creation of new circular reactions, enabling adaptive learning in myocontrol systems.
• The system's ability to acquire and forget knowledge on demand allows for personalized and evolving user-device interaction.

Conclusions:

• Shifting the myocontrol paradigm to adaptive learning through circular reactions offers a promising avenue for improving assistive device functionality.
• The Radical Constructivism framework provides a robust foundation for developing more intuitive and effective myocontrol systems.