Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning
Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning
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DOI：
10.1109/tnsre.2019.2907200
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发表时间：
2019-05-01
影响因子：
4.9
通讯作者：
Aszmann, Oskar
中科院分区：
文献类型：
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作者：
Prahm, Cosima;Schulz, Alexander;Aszmann, Oskar
用于上肢假肢控制的机器学习方法的研究已经取得了令人瞩目的进展。然而，将实验室的这些结果转化为患者的日常生活仍然是一个挑战，因为先进的控制方案往往会在日常干扰（例如电极移位）下崩溃。最近，有人建议应用自适应迁移学习来抵消电极移位，使用尽可能少的新记录的训练数据。在本文中，我们提出了一种新颖、简单的迁移学习版本，并提供了第一个用户研究，证明了迁移学习抵消电极移位的有效性。为此，我们引入了新颖的 Box 和 Beans 测试来评估假肢熟练程度，并将用户性能与初始简单模式识别系统、电极移位下的系统以及迁移学习后的系统进行比较。我们的结果表明，迁移学习可以显着减轻 Box 和 Beans 测试中电极移位对用户性能的影响。
Research on machine learning approaches for upper-limb prosthesis control has shown impressive progress. However, translating these results from the lab to patient's everyday lives remains a challenge because advanced control schemes tend to break down under everyday disturbances, such as electrode shifts. Recently, it has been suggested to apply adaptive transfer learning to counteract electrode shifts using as little newly recorded training data as possible. In this paper, we present a novel, simple version of transfer learning and provide the first user study demonstrating the effectiveness of transfer learning to counteract electrode shifts. For this purpose, we introduce the novel Box and Beans test to evaluate prosthesis proficiency and compare user performance with an initial simple pattern recognition system, the system under electrode shifts, and the system after transfer learning. Our results show that transfer learning could significantly alleviate the impact of electrode shifts on user performance in the Box and Beans test.