Zhang Xu, Li Xiangxin, Samuel Oluwarotimi Williams, Huang Zhen, Fang Peng, Li Guanglin
CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China.
Front Neurorobot. 2017 Sep 27;11:51. doi: 10.3389/fnbot.2017.00051. eCollection 2017.
Electromyogram (EMG) contains rich information for motion decoding. As one of its major applications, EMG-pattern recognition (PR)-based control of prostheses has been proposed and investigated in the field of rehabilitation robotics for decades. These prostheses can offer a higher level of dexterity compared to the commercially available ones. However, limited progress has been made toward clinical application of EMG-PR-based prostheses, due to their unsatisfactory robustness against various interferences during daily use. These interferences may lead to misclassifications of motion intentions, which damage the control performance of EMG-PR-based prostheses. A number of studies have applied methods that undergo a postprocessing stage to determine the current motion outputs, based on previous outputs or other information, which have proved effective in reducing erroneous outputs. In this study, we proposed a postprocessing strategy that locks the outputs during the constant contraction to block out occasional misclassifications, upon detecting the motion onset using a threshold. The strategy was investigated using three different motion onset detectors, namely mean absolute value, Teager-Kaiser energy operator, or mechanomyogram (MMG). Our results indicate that the proposed strategy could suppress erroneous outputs, during rest and constant contractions in particular. In addition, with MMG as the motion onset detector, the strategy was found to produce the most significant improvement in the performance, reducing the total errors up to around 50% (from 22.9 to 11.5%) in comparison to the original classification output in the online test, and it is the most robust against threshold value changes. We speculate that motion onset detectors that are both smooth and responsive would further enhance the efficacy of the proposed postprocessing strategy, which would facilitate the clinical application of EMG-PR-based prosthetic control.
肌电图（EMG）包含用于运动解码的丰富信息。作为其主要应用之一，基于肌电图模式识别（PR）的假肢控制在康复机器人领域已被提出并研究了数十年。与市售假肢相比，这些假肢可以提供更高的灵活性。然而，基于肌电图 - PR的假肢在临床应用方面进展有限，因为它们在日常使用中对各种干扰的鲁棒性不令人满意。这些干扰可能导致运动意图的错误分类，从而损害基于肌电图 - PR的假肢的控制性能。许多研究应用了在后期处理阶段基于先前输出或其他信息来确定当前运动输出的方法，这些方法已被证明在减少错误输出方面是有效的。在本研究中，我们提出了一种后期处理策略，该策略在使用阈值检测到运动开始时，在持续收缩期间锁定输出以阻止偶尔的错误分类。使用三种不同的运动开始检测器，即平均绝对值、Teager - Kaiser能量算子或肌动图（MMG）对该策略进行了研究。我们的结果表明，所提出的策略可以抑制错误输出，特别是在休息和持续收缩期间。此外，以MMG作为运动开始检测器时，发现该策略在性能上有最显著的改善，与在线测试中的原始分类输出相比，总误差降低了约50%（从22.9%降至11.5%），并且对阈值变化最具鲁棒性。我们推测，既平滑又灵敏的运动开始检测器将进一步提高所提出的后期处理策略的功效，这将促进基于肌电图 - PR的假肢控制的临床应用。
