Co-Adaptive Velocity and Position Control of 3-DoFs Prosthesis via Incremental Learning.

Upper-limb prosthesis control remains challenging in achieving natural and intuitive movements, especially for devices with multiple actuated degrees of freedom (DoFs), often demanding high cognitive effort. Machine learning aids in mapping phantom limb muscle patterns to prosthetic movements, but is limited by the instability of electromyographic signals over time. This study investigates two simultaneous and proportional myocontrol strategies, based on position and velocity, using incremental learning for a 3-DoFs prosthesis, allowing co-adaptation between the system and the user. Six able-bodied and five limb-difference participants performed Target Achievement Control tests over four sessions per control strategy, assessing performance, usability, workload, simultaneity, and proportionality. Results indicate that velocity control consistently outperforms position control in both populations, yielding lower errors, higher success rates and path efficiency, and lower workload. Notably, both control strategies showed significant improvement over time in the able-bodied group, while only position control improved significantly in the limb-difference group. Interestingly, no significant difference in usability was observed between the two strategies in either group. Position control promoted greater simultaneous actuation of multi-DoFs. However, the overall findings support the use of velocity-based control as a means to improve prosthetic performance and user satisfaction.