Abstract：
Objective.The use of electromyogram (EMG) signals recorded from the wrist is emerging as a desirable input modality for human-machine interaction (HMI). Although forearm-based EMG has been used for decades in prosthetics, there has been comparatively little prior work evaluating the performance of wrist-based control, especially in online, user-in-the-loop studies. Furthermore, despite different motivating use cases for wrist-based control, research has mostly adopted legacy prosthesis control evaluation frameworks.Approach.Gaining inspiration from rhythm games and the Schmidt\'s law speed-accuracy tradeoff, this work proposes a new temporally constrained evaluation environment with a linearly increasing difficulty to compare the online usability of wrist and forearm EMG. Compared to the more commonly used Fitts\' Law-style testing, the proposed environment may offer different insights for emerging use cases of EMG as it decouples the machine learning algorithm\'s performance from proportional control, is easily generalizable to different gesture sets, and enables the extraction of a wide set of usability metrics that describe a users ability to successfully accomplish a task at a certain time with different levels of induced stress.Main results.The results suggest that wrist EMG-based control is comparable to that of forearm EMG when using traditional prosthesis control gestures and can even be better when using fine finger gestures. Additionally, the results suggest that as the difficulty of the environment increased, the online metrics and their correlation to the offline metrics decreased, highlighting the importance of evaluating myoelectric control in real-time evaluations over a range of difficulties.Significance.This work provides valuable insights into the future design and evaluation of myoelectric control systems for emerging HMI applications.
摘要：
目的：从手腕记录的肌电图（EMG）信号的使用正在成为人机交互（HMI）的理想输入方式。尽管基于前臂的EMG已经在假肢中使用了数十年，评估基于手腕的控制性能的先前工作相对较少，尤其是在网上，用户在循环研究。此外，尽管基于手腕的控制有不同的激励用例，研究大多采用了传统的假肢控制评估框架。
方法：从节奏游戏和施密特定律的速度-准确性权衡中获得灵感，这项工作提出了一种新的时间约束评估环境，线性增加的难度来比较手腕和前臂EMG的在线可用性。与更常用的Fitts法律式测试相比，所提出的环境可能为EMG的新兴用例提供不同的见解，因为它将机器学习算法的性能与比例控制分离，很容易推广到不同的手势集，并且可以提取广泛的可用性指标，这些指标描述用户在特定时间以不同程度的诱导压力成功完成任务的能力。
结果：结果表明，在使用传统的假肢控制手势时，基于手腕EMG的控制与前臂EMG的控制相当，并且在使用精细的手指手势时甚至可以更好。此外,结果表明，随着环境难度的增加，在线指标及其与离线指标的相关性下降，强调在一系列困难的实时评估中评估肌电控制的重要性。
结论：这项工作为新兴的HMI应用的肌电控制系统的未来设计和评估提供了有价值的见解。
方法：从节奏游戏和施密特定律的速度-准确性权衡中获得灵感，这项工作提出了一种新的时间约束评估环境，线性增加的难度来比较手腕和前臂EMG的在线可用性。与更常用的Fitts法律式测试相比，所提出的环境可能为EMG的新兴用例提供不同的见解，因为它将机器学习算法的性能与比例控制分离，很容易推广到不同的手势集，并且可以提取广泛的可用性指标，这些指标描述用户在特定时间以不同程度的诱导压力成功完成任务的能力。
结果：结果表明，在使用传统的假肢控制手势时，基于手腕EMG的控制与前臂EMG的控制相当，并且在使用精细的手指手势时甚至可以更好。此外,结果表明，随着环境难度的增加，在线指标及其与离线指标的相关性下降，强调在一系列困难的实时评估中评估肌电控制的重要性。
结论：这项工作为新兴的HMI应用的肌电控制系统的未来设计和评估提供了有价值的见解。
