A force levels and gestures integrated multi-task strategy for neural decoding
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ORIGINAL ARTICLE
A force levels and gestures integrated multi-task strategy for neural decoding Shaoyang Hua1 · Congqing Wang1 · Zuoshu Xie1 · Xuewei Wu1 Received: 7 January 2020 / Accepted: 29 March 2020 © The Author(s) 2020
Abstract 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. Keywords Neural decoding · Multi-task learning (MTL) · Pseudo-task augmentation (PTA) · Convolutional neural network (CNN)
Introduction Neural decoding based on electromyography (EMG) signals has attracted many researchers to explore [1]. It is a technology translating bioelectrical signals in muscles into corresponding instructions [2]. Compared with other human–computer interaction modes, neural decoding is more convenient and less constrained by the surrounding environment, resulting in tremendous development potential in medical, entertainment and military fields [3, 4]. There has been a lot of literature about neural decoding of gestures. Naik et al. [5] associated independent component analysis with Icasso clustering to extract features of surface electromyography (sEMG), then classified gestures by linear discriminant analysis (LDA). Lima et al. investigated relevance vector machines and fractal dimension to identify seven gestures [6]. Besides, convolutional neural network (CNN) has benefited from the success in the com-
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Shaoyang Hua [email protected] College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
puter vision field, and is applied in neural decoding [7–11]. Wei et al. [12] combined information detected from electrodes in different methods, and input them to a multi-stream CNN framework. Hu et al. [13] considered time-series information by recurrent neural network based on this work, which improved the recognition accuracy. In addition, Allard et al. [14] and Zhai et al. [15] proposed a novel method by calculating the feature matrices from time–frequency domain informa
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