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首页> 外文会议>IEEE International Conference on Signal Processing, Communications and Computing >Classification of Steering Wheel Contacts from Electrocardiogram Signals Using Machine Learning
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Classification of Steering Wheel Contacts from Electrocardiogram Signals Using Machine Learning

机译：使用机器学习从心电图信号分类方向盘触点

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With the current day advancements in both computational power and machine learning (ML) techniques, there is a fundamental shift toward the application of new and smarter technologies. Worldwide incidents of motor vehicle crashes cause financial and emotional distress, along with physical injury, and even death, often stemming from driver fatigue. Nowadays, advanced ML techniques can be combined with electrocardiogram signals recorded from hand-contact with the motor vehicle steering wheel, to accurately detect the onset of driver fatigue. However, the signal recorded is only viable for fatigue analysis when two hands are in contact with the wheel. This work aims to carry out a comparative evaluation on a selected set of ML algorithms, when considering their ability to determine the number of contacts on a steering wheel. The ML classifiers considered in this study include the unsupervised methods K-means clustering, and Gaussian Mixture Model, and the supervised methods Support Vector Machine (SVM), Linear Discriminant Analysis, and Convolutional Neural Network (CNN). The evaluation is carried out based on both standard ML evaluation metrics including accuracy, precision, specificity, and computational cost. The experimental results show that the CNN produced the highest-level accuracy (>99%), but also had the highest computational cost. The SVM method presented the most balanced performance with a low computational cost and the second highest-level accuracy (94%). This paper assesses the viability of ML algorithms to eliminate the non-viable segments within ECGs that are used to determine driver fatigue. This is done by evaluating the techniques ability to consistently detect the number of contacts on a steering wheel, and its ability to be implemented in real-time, through the analysis of computational cost.
机译：随着计算能力和机器学习（ML）技术的日新月异，从根本上转移了向新的和更智能的技术的应用。全球范围内发生的汽车撞车事故通常会由于驾驶员疲劳而导致财务和情感困扰，以及人身伤害甚至死亡。如今，先进的ML技术可以与从与汽车方向盘的手接触中记录的心电图信号结合使用，以准确检测驾驶员疲劳的发作。但是，仅当两只手接触车轮时，记录的信号才可用于疲劳分析。这项工作旨在在考虑选择的一组ML算法确定方向盘上的触点数量的能力时进行比较评估。本研究中考虑的ML分类器包括无监督方法K均值聚类和高斯混合模型，以及受监督方法支持向量机（SVM），线性判别分析和卷积神经网络（CNN）。评估是基于两种标准的ML评估指标进行的，包括准确性，准确性，特异性和计算成本。实验结果表明，CNN产生了最高级别的准确性（> 99％），但同时具有最高的计算成本。支持向量机方法以最低的计算成本和最高的第二精度（94％）提供了最平衡的性能。本文评估了ML算法的可行性，以消除ECG中用于确定驾驶员疲劳的不可行部分。这是通过评估计算成本来评估能够一致地检测方向盘上的触点数量的技术能力及其实时实现的能力来实现的。

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