Abstract
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.
Highlights
Hands are important parts of the human body, which are used to perform various dexterous daily actions in an intuitive manner
The results of this study have shown that the proposed postprocessing strategy could help stabilize EMG classification outputs and potentially improve the robustness of myoelectrically controlled prostheses
The performance of the strategy using each of the three different motion onset detectors namely mean absolute value (MAV), Teager–Kaiser energy operator (TKE), and MMG, was evaluated, respectively
Summary
Hands are important parts of the human body, which are used to perform various dexterous daily actions in an intuitive manner. An early proposed prosthetic control methodology, based on the threshold detection of surface EMG measured from residual arms, has been commercially applied for a long time It determines the motion outputs upon detection of the activity from the corresponding muscles. The most critical issue might be the unsatisfactory robustness and reliability of EMGPR-based movement identifications, which are caused by some inevasible interferences in the practical uses such as electrode shifts (Hargrove et al, 2008; Young et al, 2011), muscle fatigue (Wan et al, 2010), and change in limb positions (Scheme et al, 2010; Fougner et al, 2011; Geng et al, 2012) These interferences may lead to misclassifications that constantly exist and vary during all muscle contraction phases. This study proposed a postprocessing strategy that stabilizes motion outputs during the constant contraction upon a threshold-based motion onset detection process, to improve the robustness of myoelectric control especially during the rest phase and constant contractions. This study provided a comparison among different motion onset detectors, and proposed the characteristics of motion onset detectors that are key to further enhancing the efficacy of the proposed strategy
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