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摘要翻译：
表面肌电（s-EMG）传感器是控制上肢假肢的一种很有前途的方法。然而，训练课程是必要的，以建立控制器，将使s-EMG为基础的运动成为可能。机器学习算法使用训练会话期间记录的所有数据来进行姿态分类，这将允许控制器区分每个姿态。这项研究的目的是调查是否有可能做出一个在一段时间内仍然有效的姿势分类。下一步将研究它是如何根据训练期间提交给它的信息量而变化的，考虑到上肢假体的实际生活日常使用。
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英文标题：
《Towards life-long learning of posture control for s-EMG prostheses》
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作者：
Marco Lampacrescia
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最新提交年份：
2015
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分类信息：
一级分类：Quantitative Biology 数量生物学
二级分类：Other Quantitative Biology 其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
Surface electromyography (s-EMG) sensors are a promising way to control upper-limb prostheses. However a training session is necessary in order to set up the controller that will make s-EMG based movement possible. All data recorded during the training session are used by a machine learning algorithm to make a posture classification, that will allow the controller to distinguish each posture. The aim of this study is to investigate if it's possible to make a posture classification which can remain valid over time. The next step will be the study of how it varies depending on the amount of information submitted to it during the training session in view of real life everyday use of the upper-limb prosthesis.
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PDF链接：
https://arxiv.org/pdf/1511.07785
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