Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council (CSIC), Madrid, Spain.
Electronic Technology Department, Rey Juan Carlos University, Madrid, Spain.
J Neuroeng Rehabil. 2023 Oct 24;20(1):141. doi: 10.1186/s12984-023-01268-8.
Electromyography (EMG) is a classical technique used to record electrical activity associated with muscle contraction and is widely applied in Biomechanics, Biomedical Engineering, Neuroscience and Rehabilitation Robotics. Determining muscle activation onset timing, which can be used to infer movement intention and trigger prostheses and robotic exoskeletons, is still a big challenge. The main goal of this paper was to perform a review of the state-of-the-art of EMG onset detection methods. Moreover, we compared the performance of the most commonly used methods on experimental EMG data.
A total of 156 papers published until March 2022 were included in the review. The papers were analyzed in terms of application domain, pre-processing method and EMG onset detection method. The three most commonly used methods [Single (ST), Double (DT) and Adaptive Threshold (AT)] were applied offline on experimental intramuscular and surface EMG signals obtained during contractions of ankle and knee joint muscles.
Threshold-based methods are still the most commonly used to detect EMG onset. Compared to ST and AT, DT required more processing time and, therefore, increased onset timing detection, when applied on experimental data. The accuracy of these three methods was high (maximum error detection rate of 7.3%), demonstrating their ability to automatically detect the onset of muscle activity. Recently, other studies have tested different methods (especially Machine Learning based) to determine muscle activation onset offline, reporting promising results.
This study organized and classified the existing EMG onset detection methods to create consensus towards a possible standardized method for EMG onset detection, which would also allow more reproducibility across studies. The three most commonly used methods (ST, DT and AT) proved to be accurate, while ST and AT were faster in terms of EMG onset detection time, especially when applied on intramuscular EMG data. These are important features towards movement intention identification, especially in real-time applications. Machine Learning methods have received increased attention as an alternative to detect muscle activation onset. However, although several methods have shown their capability offline, more research is required to address their full potential towards real-time applications, namely to infer movement intention.
肌电图（EMG）是一种用于记录与肌肉收缩相关的电活动的经典技术，广泛应用于生物力学、生物医学工程、神经科学和康复机器人领域。确定肌肉激活的起始时间，这可以用于推断运动意图并触发假肢和外骨骼机器人，仍然是一个巨大的挑战。本文的主要目的是对肌电图起始检测方法的最新技术进行综述。此外，我们还比较了最常用方法在实验肌电图数据上的性能。
共纳入截至 2022 年 3 月发表的 156 篇论文进行综述。从应用领域、预处理方法和肌电图起始检测方法三个方面对这些论文进行了分析。离线应用于踝关节和膝关节肌肉收缩过程中获得的实验性肌内和表面肌电图信号，对三种最常用的方法[单（ST）、双（DT）和自适应阈值（AT）]进行了比较。
基于阈值的方法仍然是最常用的检测肌电图起始的方法。与 ST 和 AT 相比，DT 在应用于实验数据时需要更多的处理时间，因此增加了起始时间检测。这三种方法的准确性都很高（最大错误检测率为 7.3%），表明它们有能力自动检测肌肉活动的起始。最近，其他研究已经测试了不同的方法（特别是基于机器学习的方法）来离线确定肌肉激活的起始，报告了有希望的结果。
本研究对现有的肌电图起始检测方法进行了整理和分类，旨在为肌电图起始检测创建一个可能的标准化方法，从而提高研究之间的可重复性。三种最常用的方法（ST、DT 和 AT）被证明是准确的，而 ST 和 AT 在肌电图起始检测时间方面更快，特别是应用于肌内肌电图数据时。这些是识别运动意图的重要特征，特别是在实时应用中。机器学习方法作为检测肌肉激活起始的替代方法受到了越来越多的关注。然而，尽管有几种方法已经离线证明了其能力，但仍需要更多的研究来充分发挥它们在实时应用中的潜力，即推断运动意图。
