Adaptive pattern recognition of myoelectric signals: exploration of conceptual framework and practical algorithms.
Adaptive pattern recognition of myoelectric signals: exploration of conceptual framework and practical algorithms.
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DOI：
10.1109/tnsre.2009.2023282
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发表时间：
2009-06
期刊：
影响因子：
--
通讯作者：
Kuiken TA
中科院分区：
文献类型：
--
作者：
Sensinger JW;Lock BA;Kuiken TA
模式识别是从肌电信号中破译运动意图的有用工具。识别范例必须适应用户，以便随着时间的推移在临床上可行。大多数现有的范例是静态的，虽然两种形式的适应得到了有限的关注。监督自适应可以实现高精度，因为预期的类是已知的，但代价是重复繁琐的训练会话。无监督自适应试图在不知道预期类别的情况下实现高准确性，从而实现对用户来说不麻烦的自适应，但以降低准确性为代价。本研究报告了一种新的自适应实验，8名受试者，允许重复测量事后比较四个监督和三个无监督的适应范式。与非自适应分类器相比，所有有监督的自适应范例随着时间的推移减少了至少26%的错误。大多数无监督的适应范式提供了较小的减少错误，由于频繁的不确定性的正确类。选择高置信度样本的一种方法显示出最实用的实施，尽管其他方法值得进一步研究。监督适应应考虑纳入任何临床可行的模式识别控制器，无监督适应应重新获得关注，以提供透明的适应。
Pattern Recognition is a useful tool for deciphering movement intent from myoelectric signals. Recognition paradigms must adapt with the user in order to be clinically viable over time. Most existing paradigms are static, although two forms of adaptation have received limited attention. Supervised adaptation can achieve high accuracy since the intended class is known, but at the cost of repeated cumbersome training sessions. Unsupervised adaptation attempts to achieve high accuracy without knowledge of the intended class, thus achieving adaptation that is not cumbersome to the user, but at the cost of reduced accuracy. This study reports a novel adaptive experiment on eight subjects that allowed repeated measures post-hoc comparison of four supervised and three unsupervised adaptation paradigms. All supervised adaptation paradigms reduced error over time by at least 26% compared to the nonadapting classifier. Most unsupervised adaptation paradigms provided smaller reductions in error, due to frequent uncertainty of the correct class. One method that selected high-confidence samples showed the most practical implementation, although the other methods warrant future investigation. Supervised adaptation should be considered for incorporation into any clinically viable pattern recognition controller, and unsupervised adaptation should receive renewed interest in order to provide transparent adaptation.