This paper introduces the first generalization and adaptation benchmark using machine learning for evaluating out-of-distribution performance of electromyography (EMG) classification algorithms. The ability of an EMG classifier to handle inputs drawn from a different distribution than the training distribution is critical for real-world deployment as a control interface. By predicting the user's intended gesture using EMG signals, we can create a wearable solution to control assistive technologies, such as computers, prosthetics, and mobile manipulator robots. This new out-of-distribution benchmark consists of two major tasks that have utility for building robust and adaptable control interfaces: 1) intersubject classification and 2) adaptation using train-test splits for time-series. This benchmark spans nine datasets--the largest collection of EMG datasets in a benchmark. Among these, a new dataset is introduced, featuring a novel, easy-to-wear high-density EMG wearable for data collection. The lack of open-source benchmarks has made comparing accuracy results between papers challenging for the EMG research community. This new benchmark provides researchers with a valuable resource for analyzing practical measures of out-of-distribution performance for EMG datasets. Our code and data from our new dataset can be found at emgbench.github.io.

翻译：本文首次提出了一个基于机器学习的泛化与适应基准测试，用于评估肌电信号分类算法在分布外数据上的性能。肌电信号分类器处理与训练分布不同的输入数据的能力，对于其作为控制接口在实际场景中的部署至关重要。通过利用肌电信号预测用户的意图手势，我们可以创建一种可穿戴解决方案，用于控制辅助技术，例如计算机、假肢和移动操作机器人。这一新的分布外基准测试包含两个对构建鲁棒且适应性强的控制接口具有实用价值的主要任务：1）跨被试分类和2）利用时间序列的训练-测试分割进行适应。该基准测试涵盖了九个数据集，是基准测试中肌电数据集的最大集合。其中，引入了一个新的数据集，其特点是采用了一种新颖、易于佩戴的高密度肌电可穿戴设备进行数据采集。由于缺乏开源基准测试，肌电研究社区在比较不同论文的准确率结果时一直面临挑战。这一新基准为研究人员提供了一个宝贵的资源，用于分析肌电数据集分布外性能的实际度量指标。我们的代码及新数据集的数据可在 emgbench.github.io 找到。