Next generation prosthetics will rely massively on myoelectric ”Pattern Recognition” (PR) based control approaches, to improve their users’ dexterity. One major identified factor of successful functioning of these approaches lies in the training of amputees and in their understanding of how those prosthetics works. We thus propose here an intuitive pattern similarity biofeedback which can be easily used to train amputees and allow them to optimize their muscular contractions to improve their control performance. Experiments were conducted on twenty able-bodied participants and one transradial amputee. Their performance in controlling an interface through a myoelectric PR algorithm was evaluated; before and after a short automatic user training session consisting in using the proposed visual biofeedback for ten participants, and using a generic PR algorithm output feedback for the others ten. Participants who were trained with the proposed biofeedback increased their classification score for the retrained gesture (by 39.4%), without affecting the overall classification performance (which progressed by 10.2%) through over-training and increase of False Positive rate as observed in the control group. Additional analysis indicates a clear change in contraction strategy only in the group who used the proposed biofeedback. These preliminary results highlight the potential of this method which does not focus so much on over-optimizing the pattern recognition algorithm or on physically training the users, but on providing them simple and intuitive information to adapt or change their motor strategies to solve some misclassification issues.

中文翻译：

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用模式相似性生物反馈指导用户培训，以提高肌电模式识别的性能。

下一代假肢将在很大程度上依赖于基于肌电“模式识别”（PR）的控制方法，以提高其用户的灵活性。这些方法成功发挥作用的一个主要因素是对截肢者的培训以及他们对这些假肢如何工作的理解。因此，我们在这里提出一种直观的模式相似性生物反馈，该反馈可以轻松地用于训练截肢者并允许他们优化其肌肉收缩以改善其控制性能。实验针对20名身体健康的参与者和1名经trans骨截肢者进行。评估了它们通过肌电PR算法控制界面的性能；在简短的自动用户培训之前和之后，包括对十名参与者使用拟议的视觉生物反馈，并使用通用PR算法输出其他十个反馈。接受拟议的生物反馈训练的参与者通过过度训练和在对照组中观察到的假阳性率增加，在重新训练后的手势中将其分类得分提高了（39.4％），而没有影响整体分类表现（进步了10.2％）。组。进一步的分析表明，只有在使用拟议的生物反馈的人群中，收缩策略才发生明显变化。这些初步结果凸显了这种方法的潜力，该方法并没有将重点放在过度优化模式识别算法或对用户进行身体训练上，而是在向他们提供简单直观的信息以适应或更改他们的运动策略以解决一些错误分类问题。