User adaptation in long-term, open-loop myoelectric training: implications for EMG pattern recognition in prosthesis control
User adaptation in long-term, open-loop myoelectric training: implications for EMG pattern recognition in prosthesis control
复制标题
长期开环肌电训练中的用户适应：对假肢控制中肌电图模式识别的影响
DOI：
10.1088/1741-2560/12/4/046005
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发表时间：
2015-08-01
影响因子：
4
通讯作者：
Zhu, Xiangyang
中科院分区：
文献类型：
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作者：
He, Jiayuan;Zhang, Dingguo;Zhu, Xiangyang
Objective.最近的研究报告，分类性能的肌电信号（EMG）随着时间的推移而下降，没有适当的分类再训练。这个问题是相关的肌电信号模式识别的应用程序中的主动假肢控制。Approach.在这项研究中，我们调查了连续11天的变化，在8个健全的主体和两个截肢者的EMG分类性能。主要结果。据观察，当分类器在第一天的数据上进行训练并在第二天的数据上进行测试时，分类误差呈指数下降，但对于身体健全的受试者，在四天后达到稳定，对于截肢者，在六到九天后达到稳定。日间表现逐渐接近相应的日内表现。意义这些结果表明，当受试者执行预定义运动的天数增加时，EMG信号特征随时间的相对变化逐渐变小。因此，运动任务的表现随着时间的推移更加一致，从而产生更多可重复的EMG模式，即使受试者对其表现没有任何外部反馈。身体健全的受试者和肢体缺陷的受试者的学习曲线都可以建模为指数函数。这些结果提供了重要的见解，在实际的长期肌电控制应用的用户适应特性，与自适应模式识别系统的设计的影响。
Objective. Recent studies have reported that the classification performance of electromyographic (EMG) signals degrades over time without proper classification retraining. This problem is relevant for the applications of EMG pattern recognition in the control of active prostheses. Approach. In this study we investigated the changes in EMG classification performance over 11 consecutive days in eight able-bodied subjects and two amputees. Main results. It was observed that, when the classifier was trained on data from one day and tested on data from the following day, the classification error decreased exponentially but plateaued after four days for able-bodied subjects and six to nine days for amputees. The between-day performance became gradually closer to the corresponding within-day performance. Significance. These results indicate that the relative changes in EMG signal features over time become progressively smaller when the number of days during which the subjects perform the pre-defined motions are increased. The performance of the motor tasks is thus more consistent over time, resulting in more repeatable EMG patterns, even if the subjects do not have any external feedback on their performance. The learning curves for both able-bodied subjects and subjects with limb deficiencies could be modeled as an exponential function. These results provide important insights into the user adaptation characteristics during practical long-term myoelectric control applications, with implications for the design of an adaptive pattern recognition system.