Duraj Agnieszka, Duczymiński Daniel
Institute of Information Technology, Lodz University of Technology, al. Politechniki 8, 93-590 Łódź, Poland.
Entropy (Basel). 2023 Jul 26;25(8):1121. doi: 10.3390/e25081121.
The present article is devoted to outlier detection in phases of human movement. The aim was to find the most efficient machine learning method to detect abnormal segments inside physical activities in which there is a probability of origin from other activities. The problem was reduced to a classification task. The new method is proposed based on a nested binary classifier. Test experiments were then conducted using several of the most popular machine learning algorithms (linear regression, support vector machine, -nearest neighbor, decision trees). Each method was separately tested on three datasets varying in characteristics and number of records. We set out to evaluate the effectiveness of the models, basic measures of classifier evaluation, and confusion matrices. The nested binary classifier was compared with deep neural networks. Our research shows that the method of nested binary classifiers can be considered an effective way of recognizing outlier patterns for HAR systems.
本文致力于人体运动阶段的异常值检测。目的是找到最有效的机器学习方法，以检测体育活动中可能源于其他活动的异常片段。该问题被简化为一个分类任务。基于嵌套二元分类器提出了一种新方法。然后使用几种最流行的机器学习算法（线性回归、支持向量机、k近邻、决策树）进行了测试实验。每种方法分别在三个特征和记录数量不同的数据集上进行测试。我们着手评估模型的有效性、分类器评估的基本指标以及混淆矩阵。将嵌套二元分类器与深度神经网络进行了比较。我们的研究表明，嵌套二元分类器方法可被视为用于HAR系统识别异常模式的有效方法。
