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A force levels and gestures integrated multi-task strategy for neural decoding
Complex & Intelligent Systems ( IF 5.8 ) Pub Date : 2020-05-06 , DOI: 10.1007/s40747-020-00140-9
Shaoyang Hua , Congqing Wang , Zuoshu Xie , Xuewei Wu

This paper discusses the problem of decoding gestures represented by surface electromyography (sEMG) signals in the presence of variable force levels. It is an attempt that multi-task learning (MTL) is proposed to recognize gestures and force levels synchronously. First, methods of gesture recognition with different force levels are investigated. Then, MTL framework is presented to improve the gesture recognition performance and give information about force levels. Last but not least, to solve the problem that using the greedy principle in MTL, a modified pseudo-task augmentation (PTA) trajectory is introduced. Experiments conducted on two representative datasets demonstrate that compared with other methods, frequency domain information with convolutional neural network (CNN) is more suitable for gesture recognition with variable force levels. Besides, the feasibility of extracting features that are closely related to both gestures and force levels is verified via MTL. By influencing learning dynamics, the proposed PTA method can improve the results of all tasks, and make it applicable to the case where the main tasks and auxiliary tasks are clear.



中文翻译:

力级别和手势集成的多任务策略用于神经解码

本文讨论了在力水平可变的情况下对由表面肌电图(sEMG)信号表示的手势进行解码的问题。尝试提出多任务学习(MTL)来同步识别手势和力量水平。首先,研究了不同力水平下的手势识别方法。然后,提出了MTL框架以提高手势识别性能并提供有关力级别的信息。最后但并非最不重要的一点是,为了解决在MTL中使用贪婪原理的问题,引入了一种改进的伪任务增强(PTA)轨迹。在两个代表性数据集上进行的实验表明,与其他方法相比,带卷积神经网络(CNN)的频域信息更适合于可变力水平的手势识别。此外,通过MTL验证了提取与手势和力度水平密切相关的特征的可行性。通过影响学习动态,提出的PTA方法可以改善所有任务的结果,使其适用于主要任务和辅助任务清晰的情况。

更新日期:2020-05-06
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