Increased interest in body–machine interfaces necessitates understanding how to train users to use nontraditional inputs. In this study, a control task driven by subject-activated surface electromyography was developed as a testbed to observe the effects of automated training methodologies on the development of performance, workload, and trust. Forty-eight subjects learned to use a surface-electromyography-based command system to perform a Fitts’s-law-style cursor-to-target task with 120 training trials and 40 evaluation trials. Subjects were divided into four groups: control, concurrent feedback, terminal feedback, and adaptive threshold. The control group trained and learned through repetition using the visual feedback of the cursor position. The concurrent feedback group received additional concurrent visual feedback during command input, and the terminal feedback group had supplementary visual feedback after command input. The adaptive threshold group did not have any additional feedback, but experienced changes in the cursor control designed to induce motor learning adaptation. The results indicate that 1) additional visual feedback improves task performance, workload, and trust during training, and 2) the groups converged in their command proficiency by the end of training.

对人体与机器​​界面的兴趣日益增加，因此有必要了解如何训练用户使用非传统输入。在这项研究中，由受试者激活的表面肌电图驱动的控制任务被开发为测试平台，以观察自动训练方法对性能，工作量和信任度发展的影响。四十八名受试者学会了使用基于表面肌电图的命令系统执行120项训练试验和40项评估试验，以执行费茨法则式的光标到目标任务。将受试者分为四组：控制，并发反馈，终端反馈和适应性阈值。对照组使用光标位置的视觉反馈通过重复训练和学习。并发反馈组在命令输入期间收到了其他并发视觉反馈，输入命令后，终端反馈组具有补充的视觉反馈。自适应阈值组没有任何其他反馈，但是在光标控件中经历了变化，旨在引起运动学习适应。结果表明，1）额外的视觉反馈可改善训练过程中的任务性能，工作量和信任度，以及2）训练结束时各小组的命令能力已趋于一致。