手语的分析和识别是当前活跃的研究领域,主要集中在手语识别上。各种方法在分析方法和用于信号采集的设备方面有所不同。传统方法依赖于使用运动捕捉工具计算的视频分析或空间定位数据。与这些传统的识别和分类方法相比,肌电图(EMG)信号,测量肌肉电活动,提供检测手势的潜在技术。这些基于EMG的方法由于其优点最近受到关注。这促使我们对方法进行全面的研究,方法,以及利用EMG传感器进行手形识别的项目。在本文中,我们通过文献综述概述了手语识别领域,目的是对最重要的技术进行深入的审查。这些技术在本文中根据各自的方法进行了分类。该调查讨论了基于表面肌电图(sEMG)信号的手语识别系统的进展和挑战。这些系统已显示出希望,但面临sEMG数据可变性和传感器放置等问题。多个传感器提高可靠性和准确性。机器学习,包括深度学习,是用来应对这些挑战的。基于sEMG的手语识别中常见的分类器包括SVM,ANN,CNN,KNN,HMM,还有LSTM.当SVM和神经网络被广泛使用时,随机森林和KNN在某些情况下显示出更好的性能。多层感知器神经网络在一项研究中获得了完美的准确性。CNN,经常与LSTM配对,排名第三最受欢迎的分类器,可以实现卓越的准确性,当利用肌电图和IMU数据时,高达99.6%。LSTM在处理EMG信号中的顺序依赖性方面备受推崇,使其成为手语识别系统的重要组成部分。总之,该调查强调了SVM和ANN分类器的普遍性,但也表明了随机森林和KNN等替代分类器的有效性。在基于EMG的手语识别系统中,LSTM成为捕获顺序依赖性和改进手势识别的最合适算法。
The analysis and recognition of sign languages are currently active fields of research focused on sign recognition. Various approaches differ in terms of analysis methods and the devices used for sign acquisition. Traditional methods rely on video analysis or spatial positioning data calculated using motion capture tools. In contrast to these conventional recognition and classification approaches, electromyogram (EMG) signals, which measure muscle electrical activity, offer potential technology for detecting gestures. These EMG-based approaches have recently gained attention due to their advantages. This prompted us to conduct a comprehensive study on the methods, approaches, and projects utilizing EMG sensors for sign language handshape recognition. In this paper, we provided an overview of the sign language recognition field through a literature review, with the objective of offering an in-depth review of the most significant techniques. These techniques were categorized in this article based on their respective methodologies. The survey discussed the progress and challenges in sign language recognition systems based on surface electromyography (sEMG) signals. These systems have shown promise but face issues like sEMG data variability and sensor placement. Multiple sensors enhance reliability and accuracy. Machine learning, including deep learning, is used to address these challenges. Common classifiers in sEMG-based sign language recognition include SVM, ANN, CNN, KNN, HMM, and LSTM. While SVM and ANN are widely used, random forest and KNN have shown better performance in some cases. A multilayer perceptron neural network achieved perfect accuracy in one study. CNN, often paired with LSTM, ranks as the third most popular classifier and can achieve exceptional accuracy, reaching up to 99.6% when utilizing both EMG and IMU data. LSTM is highly regarded for handling sequential dependencies in EMG signals, making it a critical component of sign language recognition systems. In summary, the survey highlights the prevalence of SVM and ANN classifiers but also suggests the effectiveness of alternative classifiers like random forests and KNNs. LSTM emerges as the most suitable algorithm for capturing sequential dependencies and improving gesture recognition in EMG-based sign language recognition systems.