The effect of myoelectric prosthesis control strategies and feedback level on adaptation rate for a target acquisition task.

The long-term performance of myoelectric prostheses is related not only to the short-term performance of the controller, but also to the user's ability to learn and adapt to the system. Different control architectures may have inherent tradeoffs between their short-term performance and the amount of relevant feedback that informs this adaptation. In this study we focused on the ability of two common types of myoelectric control interfaces: raw control with raw feedback, such as a regression, and filtered control with filtered feedback, such as a classifier, to affect user adaptation. We evaluated trial-by-trial adaptation to self-generated errors during a multi degree-of-freedom target acquisition task by fitting a linear regression model to data collected from 24 able-bodied subjects. Subjects showed significantly higher adaptation behavior to self-generated errors when using raw control with a raw feedback strategy than when using filtered control with a filtered feedback strategy, which suggests that control strategies with more feedback allow for higher adaptation. These results support our hypothesis that feedback-rich control strategies allow users to better understand the myoelectric control system, which may enable better long-term performance.