Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units
Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units
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DOI：
10.1093/comjnl/bxt075
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发表时间：
2014-11-01
期刊：
影响因子：
1.4
通讯作者：
Yuksek, Murat Cihan
中科院分区：
文献类型：
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作者：
Barshan, Billur;Yuksek, Murat Cihan
这项研究提供了一个比较评估的不同技术进行分类的人类活动，而穿着惯性和磁传感器单元的胸部，手臂和腿部。每个单元中的陀螺仪、加速度计和磁力计都是三轴的。朴素贝叶斯分类器，人工神经网络（ANN），基于差异的分类器，三种类型的决策树，高斯混合模型（GARCH）和支持向量机（SVM）被认为是。使用主成分分析从原始传感器数据中提取的特征集用于分类。采用三种不同的交叉验证技术来验证分类器。在正确区分率、混淆矩阵和计算成本方面，提供了分类器的性能比较。ANN（99.2%），SVM（99.2%）和GMM（99.1%）的正确区分率最高。甘精胰岛素可能是首选，因为它们的计算要求较低。关于传感器单元在身体上的位置，那些戴在腿上的传感器单元是最有用的。比较不同的传感器模式表明，如果只使用一种传感器类型，最高的分类率实现与磁力计，其次是加速度计和陀螺仪。该研究还比较了两种常用的开源机器学习环境（WEKA和PRTools）在功能、可扩展性、分类器性能和执行时间方面的差异。
This study provides a comparative assessment on the different techniques of classifying human activities performed while wearing inertial and magnetic sensor units on the chest, arms and legs. The gyroscope, accelerometer and the magnetometer in each unit are tri-axial. Naive Bayesian classifier, artificial neural networks (ANNs), dissimilarity-based classifier, three types of decision trees, Gaussian mixture models (GMMs) and support vector machines (SVMs) are considered. A feature set extracted from the raw sensor data using principal component analysis is used for classification. Three different cross-validation techniques are employed to validate the classifiers. A performance comparison of the classifiers is provided in terms of their correct differentiation rates, confusion matrices and computational cost. The highest correct differentiation rates are achieved with ANNs (99.2%), SVMs (99.2%) and a GMM (99.1%). GMMs may be preferable because of their lower computational requirements. Regarding the position of sensor units on the body, those worn on the legs are the most informative. Comparing the different sensor modalities indicates that if only a single sensor type is used, the highest classification rates are achieved with magnetometers, followed by accelerometers and gyroscopes. The study also provides a comparison between two commonly used open source machine learning environments (WEKA and PRTools) in terms of their functionality, manageability, classifier performance and execution times.