Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference (FICC), Volume 2
Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference (FICC), Volume 2
复制标题
信息和通信的进展 - 2021 年信息和通信未来会议 (FICC) 论文集，第 2 卷
DOI：
10.1007/978-3-030-73103-8_65
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Dolopikos C
中科院分区：
文献类型：
--
作者：
Dolopikos C
在这项工作中，我们实现了高达92%的五个手势之间的肌电数据伪实时分类准确率。大多数目前最先进的肌电信号处理方法都不能在学习后的环境中对实时数据进行分类，即在模型训练和结果分析之后。本文的工作表明，一个模型校正过程能够将模型的实时分类准确率从67.87%提高到91.93%，提高了24.06%。我们还表明，经典机器学习模型的集成可以比深度神经网络的性能更好。使用Myo臂章测量前臂肌肉活动，从15名受试者收集4个手势(张开手指、挥出、挥入、紧握拳头)的原始肌电数据集。在每个受试者的手势执行之间清理数据集，并使用滑动时间窗算法来执行EMG信号的统计分析，并提取有意义的数学特征作为学习范例的输入。本文使用的分类器包括随机森林、支持向量机、多层感知器和深度神经网络。这三个经典的分类器通过集成投票系统被组合成一个模型，得分为91.93%，而深度神经网络在对一个主题进行校准和进行实时分类后的性能都达到了88.68%(两者的校准前得分分别为67.87%和74.27%)。
In this work, we achieve up to 92% classification accuracy of electromyographic data between five gestures in pseudo-real-time. Most current state-of-the-art methods in electromyographical signal processing are unable to classify real-time data in a post-learning environment, that is, after the model is trained and results are analysed. In this work we show that a process of model calibration is able to lead models from 67.87% real-time classification accuracy to 91.93%, an increase of 24.06%. We also show that an ensemble of classical machine learning models can outperform a Deep Neural Network. An original dataset of EMG data is collected from 15 subjects for 4 gestures (Open-Fingers, Wave-Out, Wave-in, Close-fist) using a Myo Armband for measurement of forearm muscle activity. The dataset is cleaned between gesture performances on a per-subject basis and a sliding temporal window algorithm is used to perform statistical analysis of EMG signals and extract meaningful mathematical features as input to the learning paradigms. The classifiers used in this paper include a Random Forest, a Support Vector Machine, a Multilayer Perceptron, and a Deep Neural Network. The three classical classifiers are combined into a single model through an ensemble voting system which scores 91.93% compared to the Deep Neural Network which achieves a performance of 88.68%, both after calibrating to a subject and performing real-time classification (pre-calibration scores for the two being 67.87% and 74.27%, respectively).