Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the off line accuracy reported in the literature and the real-time usability of a classifier. This gap mainly stems from two factors: 1) The absence of a controller, making the data collected dissimilar to actual control. 2) The difficulty of including the four main dynamic factors (gesture intensity, limb position, electrode shift, and transient changes in the signal), as including their permutations drastically increases the amount of data to be recorded. Contrarily, online datasets are limited to the exact EMG-based controller used to record them, necessitating the recording of a new dataset for each control method or variant to be tested. Consequently, this paper proposes a new type of dataset to serve as an intermediate between off line and online datasets, by recording the data using a real-time experimental protocol. The protocol, performed in virtual reality, includes the four main dynamic factors and uses an EMG-independent controller to guide movements. This EMG-independent feedback ensures that the user is in-the-loop during recording, while enabling the resulting dynamic dataset to be used as an EMG-based benchmark. The dataset is comprised of 20 able-bodied participants completing three to four sessions over a period of 14 to 21 days. The ability of the dynamic dataset to serve as a benchmark is leveraged to evaluate the impact of different-recalibration techniques for long-term (across-day) gesture recognition, including a novel algorithm, named TADANN. TADANN consistently and significantly (p <; 0.05) outperforms using fine-tuning as the recalibration technique.

中文翻译：

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用于虚拟现实增强肌电图手势识别的可转移自适应域对抗神经网络


在基于肌电图 (EMG) 的手势识别领域，文献中报告的离线准确度与分类器的实时可用性之间存在差异。这种差距主要源于两个因素：1）缺乏控制者，使得收集到的数据与实际控制存在差异。 2) 包含四个主要动态因素（手势强度、肢体位置、电极移位和信号瞬态变化）的难度，因为包含它们的排列会大大增加要记录的数据量。相反，在线数据集仅限于用于记录它们的基于 EMG 的精确控制器，因此需要为要测试的每种控制方法或变体记录新的数据集。因此，本文提出了一种新型数据集，通过使用实时实验协议记录数据，作为离线和在线数据集之间的中间体。该协议在虚拟现实中执行，包括四个主要动态因素，并使用独立于肌电图的控制器来指导运动。这种独立于 EMG 的反馈可确保用户在记录过程中处于循环状态，同时使生成的动态数据集能够用作基于 EMG 的基准。该数据集由 20 名身体健全的参与者组成，他们在 14 至 21 天内完成了三到四次训练。利用动态数据集作为基准的能力来评估不同重新校准技术对长期（全天）手势识别的影响，包括一种名为 TADANN 的新颖算法。 TADANN 始终显着 (p <; 0.05) 优于使用微调作为重新校准技术。