 This study examined how visual feedback during training affects the accuracy of a myoelectric classification-based control system. The researchers found that properly designed feedback mechanisms and training tasks can improve the quality of the training data and increase the accuracy of predictions about usability. Additionally, the researchers discovered that screen-guided training, which is commonly used in labs, may not accurately reflect real-world use. Therefore, it is important to design training protocols that better mirror the testing environment to ensure that both the algorithms and users are prepared for real-time control. This article was authored by Jenna Elnafel, Kevin B. Engelhart, and Eric J. Scheme.