Neurorehabilitation Systems, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
J Neuroeng Rehabil. 2022 Jul 21;19(1):78. doi: 10.1186/s12984-022-01056-w.
Myoelectric control based on hand gesture classification can be used for effective, contactless human-machine interfacing in general applications (e.g., consumer market) as well as in the clinical context. However, the accuracy of hand gesture classification can be impacted by several factors including changing wrist position. The present study aimed at investigating how channel configuration (number and placement of electrode pads) affects performance in hand gesture recognition across wrist positions, with the overall goal of reducing the number of channels without the loss of performance with respect to the benchmark (all channels).
Matrix electrodes (256 channels) were used to record high-density EMG from the forearm of 13 healthy subjects performing a set of 8 gestures in 3 wrist positions and 2 force levels (low and moderate). A reduced set of channels was chosen by applying sequential forward selection (SFS) and simple circumferential placement (CIRC) and used for gesture classification with linear discriminant analysis. The classification success rate and task completion rate were the main outcome measures for offline analysis across the different number of channels and online control using 8 selected channels, respectively.
The offline analysis demonstrated that good accuracy (> 90%) can be achieved with only a few channels. However, using data from all wrist positions required more channels to reach the same performance. Despite the targeted placement (SFS) performing similarly to CIRC in the offline analysis, the task completion rate [median (lower-upper quartile)] in the online control was significantly higher for SFS [71.4% (64.8-76.2%)] compared to CIRC [57.1% (51.8-64.8%), p < 0.01], especially for low contraction levels [76.2% (66.7-84.5%) for SFS vs. 57.1% (47.6-60.7%) for CIRC, p < 0.01]. For the reduced number of electrodes, the performance with SFS was comparable to that obtained when using the full matrix, while the selected electrodes were highly subject-specific.
The present study demonstrated that the number of channels required for gesture classification with changing wrist positions could be decreased substantially without loss of performance, if those channels are placed strategically along the forearm and individually for each subject. The results also emphasize the importance of online assessment and motivate the development of configurable matrix electrodes with integrated channel selection.
基于手势分类的肌电控制可用于一般应用（例如消费市场）以及临床环境中的有效、非接触式人机界面。然而，手势分类的准确性可能会受到多种因素的影响，包括手腕位置的变化。本研究旨在探讨通道配置（电极垫的数量和位置）如何影响不同手腕位置的手势识别性能，总体目标是在不损失性能的情况下减少通道数量，以达到基准（所有通道）。
使用矩阵电极（256 个通道）记录 13 名健康受试者在前臂上进行的一组 8 个手势的高密度肌电图，在 3 个手腕位置和 2 个力水平（低和中）下进行。通过应用顺序前向选择（SFS）和简单圆周放置（CIRC）选择一组减少的通道，并使用线性判别分析进行手势分类。离线分析的主要结果测量指标是不同通道数量下的分类成功率和任务完成率，在线控制分别使用 8 个选定通道。
离线分析表明，仅使用少数几个通道即可实现高精度（>90%）。然而，使用所有手腕位置的数据需要更多的通道才能达到相同的性能。尽管目标放置（SFS）在离线分析中表现与 CIRC 相似，但在线控制中的任务完成率[中位数（下四分位数-上四分位数）]，SFS[71.4%（64.8-76.2%）]明显高于 CIRC[57.1%（51.8-64.8%），p<0.01]，尤其是低收缩水平时[SFS 为 76.2%（66.7-84.5%），CIRC 为 57.1%（47.6-60.7%），p<0.01]。对于减少的电极数量，如果将电极沿前臂有策略地放置并为每个个体单独放置，SFS 的性能可与使用全矩阵获得的性能相媲美，而选定的电极则具有高度的个体特异性。
本研究表明，如果在手腕位置变化时的手势分类所需的通道数量可以大大减少，而不会损失性能，则可以沿着前臂和每个个体有策略地放置这些通道。结果还强调了在线评估的重要性，并激发了具有集成通道选择的可配置矩阵电极的发展。
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