Comparative study on classifying human activities with miniature inertial and magnetic sensors
Comparative study on classifying human activities with miniature inertial and magnetic sensors
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DOI：
10.1016/j.patcog.2010.04.019
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发表时间：
2010-10-01
影响因子：
8
通讯作者：
Tuncel, Orkun
中科院分区：
文献类型：
--
作者：
Altun, Kerem;Barshan, Billur;Tuncel, Orkun
本文对使用人体佩戴的微型惯性传感器和磁传感器进行人类活动分类的不同技术进行了比较研究。实现和比较的分类技术有：贝叶斯决策(BDM)、基于规则的算法(RBA)或决策树、最小二乘法(LSM)、k-近邻算法(k-NN)、动态时间规整(DTW)、支持向量机(SVM)和人工神经网络(ANN)。人类的活动通过佩戴在胸部、手臂和腿上的五个传感器单元进行分类。每个传感器单元包括三轴陀螺仪、三轴加速度计和三轴磁强计。在分类过程中使用使用主成分分析(PCA)从原始传感器数据中提取的特征集。从正确的区分率、混淆矩阵和计算成本，以及它们的预处理、训练和存储要求方面，提供了分类技术的性能比较。三种不同的交叉验证技术被用来验证分类器。结果表明，一般而言，BDM算法具有最高的分类正确率和较小的计算量。(C)2010爱思唯尔有限公司。保留所有权利。
This paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Human activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. A feature set extracted from the raw sensor data using principal component analysis (PCA) is used in the classification process. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost, as well as their preprocessing, training, and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that in general, BDM results in the highest correct classification rate with relatively small computational cost. (C) 2010 Elsevier Ltd. All rights reserved.