Yamanoi Yusuke, Togo Shunta, Jiang Yinlai, Yokoi Hiroshi
Faculty of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan.
Center for Neuroscience and Biomedical Engineering, The University of Electro-Communications, Tokyo, Japan.
Cyborg Bionic Syst. 2021 Dec 13;2021:9875814. doi: 10.34133/2021/9875814. eCollection 2021.
In recent years, myoelectric hands have become multi-degree-of-freedom (DOF) devices, which are controlled via machine learning methods. However, currently, learning data for myoelectric hands are gathered manually and thus tend to be of low quality. Moreover, in the case of infants, gathering accurate learning data is nearly impossible because of the difficulty of communicating with them. Therefore, a method that automatically corrects errors in the learning data is necessary. Myoelectric hands are wearable robots and thus have volumetric and weight constraints that make it infeasible to store large amounts of data or apply complex processing methods. Compared with general machine learning methods such as image processing, those for myoelectric hands have limitations on the data storage, although the amount of data to be processed is quite large. If we can use this huge amount of processing data to correct the learning data without storing the processing data, the machine learning performance is expected to improve. We then propose a method for correcting the learning data through utilisation of the signals acquired during the use of the myoelectric hand. The proposed method is inspired by "survival of the fittest." The effectiveness of the method was verified through offline analysis. The method reduced the amount of learning data and learning time by approximately a factor of 10 while maintaining classification rates. The classification rates improved for one participant but slightly deteriorated on average among all participants. To solve this problem, verifying the method via interactive learning will be necessary in the future.
近年来，肌电手已成为多自由度（DOF）设备，通过机器学习方法进行控制。然而，目前肌电手的学习数据是手动收集的，因此质量往往较低。此外，对于婴儿来说，由于与他们沟通困难，几乎不可能收集准确的学习数据。因此，需要一种自动纠正学习数据错误的方法。肌电手是可穿戴机器人，因此存在体积和重量限制，这使得存储大量数据或应用复杂的处理方法变得不可行。与图像处理等一般机器学习方法相比，肌电手的机器学习方法在数据存储方面存在局限性，尽管要处理的数据量相当大。如果我们能够在不存储处理数据的情况下利用这些大量的处理数据来纠正学习数据，预计机器学习性能将会提高。然后，我们提出了一种通过利用肌电手使用过程中获取的信号来纠正学习数据的方法。所提出的方法受到“适者生存”的启发。该方法的有效性通过离线分析得到了验证。该方法在保持分类率的同时，将学习数据量和学习时间减少了约10倍。对于一名参与者，分类率有所提高，但在所有参与者中平均略有下降。为了解决这个问题，未来有必要通过交互式学习来验证该方法。
