Making neurorehabilitation fun: Multiplayer training via damping forces balancing differences in skill levels

Area of Science:

• Robotics
• Neurorehabilitation
• Human-Computer Interaction

Background:

• Multiplayer environments may increase training intensity in robot-aided rehabilitation for stroke patients.
• The ARMin rehabilitation robot is utilized for upper limb motor recovery post-stroke.

Purpose of the Study:

• To investigate the dynamics of two-player training using a haptic-based environment with the ARMin robot.
• To develop and test a challenge level adaptation algorithm for regulating training difficulty in robot-aided therapy.

Main Methods:

• Implemented a haptic-based, two-player environment for time-constrained reaching movements.
• Developed an algorithm to control virtual damping, adapting challenge based on a desired success rate.
• Tested the algorithm in simulations, with unimpaired participants, and with a stroke patient.

Main Results:

• The algorithm effectively adjusted damping to achieve low (50%), moderate (70%), and high (90%) success rates in single and multiplayer modes.
• Challenge level adaptation was found to be more critical for engagement than multiplayer settings alone.
• Multiplayer settings demonstrated motivational and encouraging effects on participants.

Conclusions:

• Adaptive challenge adjustment is crucial for engaging stroke patients in robot-aided therapy.
• Multiplayer settings can enhance motivation and encouragement during rehabilitation.
• Further development of multiplayer platforms is warranted for stroke therapy optimization.