Consistent control information driven musculoskeletal model for multiday myoelectric control.

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL Journal of neural engineering Pub Date : 2023-09-15 DOI:10.1088/1741-2552/acef93
Jiamin Zhao, Yang Yu, Xinjun Sheng, Xiangyang Zhu
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引用次数: 1

Abstract

Objective.Musculoskeletal model (MM)-based myoelectric interface has aroused great interest in human-machine interaction. However, the performance of electromyography (EMG)-driven MM in long-term use would be degraded owing to the inherent non-stationary characteristics of EMG signals. Here, to improve the estimation performance without retraining, we proposed a consistent muscle excitation extraction approach based on an improved non-negative matrix factorization (NMF) algorithm for MM when applied to simultaneous hand and wrist movement prediction.Approach.We added constraints andL2-norm regularization terms to the objective function of classic NMF regarding muscle weighting matrix and time-varying profiles, through which stable muscle synergies across days were identified. The resultant profiles of these synergies were then used to drive the MM. Both offline and online experiments were conducted to evaluate the performance of the proposed method in inter-day scenarios.Main results.The results demonstrated significantly better and more robust performance over several competitive methods in inter-day experiments, including machine learning methods, EMG envelope-driven MM, and classic NMF-based MM. Furthermore, the analysis of control information on different days revealed the effectiveness of the proposed method in obtaining consistent muscle excitations.Significance.The outcomes potentially provide a novel and promising pathway for the robust and zero-retraining control of myoelectric interfaces.

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用于多天肌电控制的一致控制信息驱动的肌肉骨骼模型。
目的:基于肌肉骨骼模型的肌电界面在人机交互中引起了极大的兴趣。然而,由于肌电信号固有的非平稳特性,肌电图驱动的MM在长期使用中的性能会降低。在这里,为了在不进行再训练的情况下提高估计性能,我们提出了一种基于改进的非负矩阵分解(NMF)算法的一致性肌肉兴奋提取方法,用于手和手腕运动的同时预测。方法。我们将约束和L2范数正则化项添加到关于肌肉权重矩阵和时变轮廓的经典NMF的目标函数中,通过这些项可以确定跨天的稳定肌肉协同作用。然后,这些协同效应的结果被用于驱动MM。进行了离线和在线实验,以评估所提出的方法在日间场景中的性能。主要结果。在日间实验中,与几种竞争性方法相比,包括机器学习方法、EMG包络驱动的MM和经典的基于NMF的MM,结果显示出明显更好、更稳健的性能。此外,对不同天数的控制信息的分析表明,所提方法在获得一致的肌肉激励方面是有效的。意义。这些结果可能为肌电界面的稳健和零再训练控制提供一条新的、有前景的途径。
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来源期刊
Journal of neural engineering
Journal of neural engineering 工程技术-工程:生物医学
CiteScore
7.80
自引率
12.50%
发文量
319
审稿时长
4.2 months
期刊介绍: The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels. The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.
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