Asogbon Mojisola Grace, Samuel Oluwarotimi Williams, Nsugbe Ejay, Li Yongcheng, Kulwa Frank, Mzurikwao Deogratias, Chen Shixiong, Li Guanglin
CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China.
Nsugbe Research Labs, Swindon, United Kingdom.
Front Neurosci. 2023 Feb 22;17:1018037. doi: 10.3389/fnins.2023.1018037. eCollection 2023.
Electromyogram-based pattern recognition (EMG-PR) has been widely considered an essentially intuitive control method for multifunctional upper limb prostheses. A crucial aspect of the scheme is the EMG signal recording duration (SRD) from which requisite motor tasks are characterized per time, impacting the system's overall performance. For instance, lengthy SRD inevitably introduces fatigue (that alters the muscle contraction patterns of specific limb motions) and may incur high computational costs in building the motion intent decoder, resulting in inadequate prosthetic control and controller delay in practical usage. Conversely, relatively shorter SRD may lead to reduced data collection durations that, among other advantages, allow for more convenient prosthesis recalibration protocols. Therefore, determining the optimal SRD required to characterize limb motion intents adequately that will aid intuitive PR-based control remains an open research question.
This study systematically investigated the impact and generalizability of varying lengths of myoelectric SRD on the characterization of multiple classes of finger gestures. The investigation involved characterizing fifteen classes of finger gestures performed by eight normally limb subjects using various groups of EMG SRD including 1, 5, 10, 15, and 20 s. Two different training strategies including Between SRD and Within-SRD were implemented across three popular machine learning classifiers and three time-domain features to investigate the impact of SRD on EMG-PR motion intent decoder.
The between-SRD strategy results which is a reflection of the practical scenario showed that an SRD greater than 5 s but less than or equal to 10 s (>5 and < = 10 s) would be required to achieve decent average finger gesture decoding accuracy for all feature-classifier combinations. Notably, lengthier SRD would incur more acquisition and implementation time and vice-versa. In inclusion, the study's findings provide insight and guidance into selecting appropriate SRD that would aid inadequate characterization of multiple classes of limb motion tasks in PR-based control schemes for multifunctional prostheses.
基于肌电图的模式识别（EMG-PR）已被广泛认为是一种用于多功能上肢假肢的本质上直观的控制方法。该方案的一个关键方面是肌电信号记录持续时间（SRD），通过它可以每次表征必要的运动任务，这会影响系统的整体性能。例如，较长的SRD不可避免地会引入疲劳（这会改变特定肢体运动的肌肉收缩模式），并且在构建运动意图解码器时可能会产生高昂的计算成本，从而导致在实际使用中假肢控制不足和控制器延迟。相反，相对较短的SRD可能会导致数据采集持续时间缩短，这除了其他优点外，还允许更方便的假肢重新校准协议。因此，确定足以充分表征肢体运动意图以辅助基于直观PR的控制所需的最佳SRD仍然是一个开放的研究问题。
本研究系统地研究了不同长度的肌电SRD对多类手指手势表征的影响和普遍性。该研究涉及使用包括1、5、十、15和20秒在内的各种肌电SRD组，对八名正常肢体受试者执行的十五类手指手势进行表征。在三种流行的机器学习分类器和三种时域特征上实施了两种不同的训练策略，包括SRD之间和SRD之内，以研究SRD对EMG-PR运动意图解码器的影响。
反映实际情况的SRD之间策略结果表明，对于所有特征-分类器组合而言，如果要实现不错的平均手指手势解码准确率，则需要SRD大于5秒但小于或等于10秒（>5且<=10秒）。值得注意的是，更长的SRD会带来更多的采集和实施时间，反之亦然。此外，该研究的结果为选择合适的SRD提供了见解和指导，这将有助于在基于PR的多功能假肢控制方案中对多类肢体运动任务进行充分表征。
