Fang Yinfeng, Zhou Dalin, Li Kairu, Liu Honghai
Intelligent Systems and Biomedical Robotics Group, School of ComputingUniversity of Portsmouth.
IEEE Trans Biomed Eng. 2017 Nov;64(11):2575-2583. doi: 10.1109/TBME.2016.2641584.
It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation.It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation.
显然，用户训练对基于模式识别的肌电假肢设备控制性能有显著影响。尽管离线数据集的分类准确率看似合理，但在线准确率通常会受到生理状况变化和电极位移的影响。通过适当的用户训练策略，可以增强用户生成一致肌电图（EMG）模式的能力，以提高在线性能。本研究提出了一种聚类反馈策略，该策略通过可视化的在线EMG信号输入以及训练样本的质心向用户提供实时反馈，训练样本的维度通过降维被降至最小数量。聚类反馈提供了一个标准，引导用户有意调整运动手势和肌肉收缩力。实验结果表明，基于聚类反馈的用户训练过程中，手部动作识别准确率稳步提高，而传统的分类器反馈方法，即标签反馈，几乎没有任何改进。结果表明，使用适当的分类器反馈可以加速用户训练过程，并为在基于模式识别的假肢设备操作方面经验有限或没有经验的截肢者预示着光明的未来。显然，用户训练对基于模式识别的肌电假肢设备控制性能有显著影响。尽管离线数据集的分类准确率看似合理，但在线准确率通常会受到生理状况变化和电极位移的影响。通过适当的用户训练策略，可以增强用户生成一致肌电图（EMG）模式的能力，以提高在线性能。本研究提出了一种聚类反馈策略，该策略通过可视化的在线EMG信号输入以及训练样本的质心向用户提供实时反馈，训练样本的维度通过降维被降至最小数量。聚类反馈提供了一个标准，引导用户有意调整运动手势和肌肉收缩力。实验结果表明，基于聚类反馈的用户训练过程中，手部动作识别准确率稳步提高，而传统的分类器反馈方法，即标签反馈，几乎没有任何改进。结果表明，使用适当的分类器反馈可以加速用户训练过程，并为在基于模式识别的假肢设备操作方面经验有限或没有经验的截肢者预示着光明的未来。
