基于分解肌电信号的瞬态运动激活多类检测与跟踪

Martyna Stachaczyk, S. F. Atashzar, S. Dupan, I. Vujaklija, D. Farina
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引用次数: 7

摘要

神经假体装置的性能和功效主要取决于以高时间分辨率检测用户运动意图的能力。延迟和不正确的反应显著降低了假体系统的可用性、可控性和直观性。为了检测运动意图的稳态阶段,已经进行了大量的努力。然而,检测、分类和跟踪一个完全肌肉收缩的瞬态阶段仍然是不可能的。临床建立的控制系统主要依赖于静止、稳态收缩的表面肌电图(sEMG)信号,这些信号的时间分辨率有限。在动态、短暂性收缩的不同阶段对神经活动的表征将允许开发具有高时间分辨率的临床可行的肌电系统,可以显著提高假肢装置的直观性和可用性水平。这样可以增加响应带宽,实现自然灵巧的控制,同时避免过度的补偿动作。为此,在本文中,我们探索使用运动单元动作电位序列(MUAPTs)来设计一种运动意图检测技术。目标是对肌肉激活的短暂阶段进行分类和跟踪。从三个受试者收集的数据,在屈曲任务与四个单独的数字,被用来评估算法。将其性能与基于表面肌电信号的标准方法进行了比较。结果表明,基于muapt的相位检测算法比传统的基于表面肌电信号的相位检测算法有很大的优势。研究证实,使用基于muapt的方法对动态瞬态收缩的所有阶段进行解码、分类和跟踪是可行的,这是传统基于表面肌电信号算法的鲁棒性和有效性替代方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiclass Detection and Tracking of Transient Motor Activation based on Decomposed Myoelectric Signals
Performance and efficacy of neuroprosthetic devices depend critically on the ability to detect the users motor intent with high temporal resolution. Delayed and incorrect responses significantly reduce usability, controllability and intuitiveness of prosthetic systems. Substantial efforts have been conducted to detect the steady-state phase of motor intention. However, detection, classification, and tracking of transient phases for one complete muscle contraction is still not possible. Clinically-established control systems rely mainly on surface electromyography (sEMG) signals in stationary, steady-state contractions, that have limited temporal resolution. Characterization of neural activities during different stages of a dynamic, transient contraction would allow for the development of a clinically-viable myoelectric system with a high temporal resolution that can significantly enhance the level of intuitiveness and usability of prosthetic devices. This could increase the response bandwidth and realize natural and dexterous control while avoiding exaggerated compensatory movements. For this purpose, in this paper, we explore the use of motor unit action potential trains (MUAPTs) for designing a movement intention detection technique. The goal is to classify and track the transient phases of muscle activation. Data collected from three subjects, during flexion tasks with four individual digits, is used to evaluate the algorithm. The performance is compared with that of the standard sEMG-based approach. Results showed a substantial advantage of the MUAPT-based phase detection algorithm over the conventional sEMG-based technique. It is confirmed that decoding, classification, and tracking of all stages of a dynamic, transient contraction is feasible using the proposed MUAPT-based approach, as a robust and efficient alternative for conventional sEMG-based algorithms.
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