Journal
COMPUTER JOURNAL
Volume 57, Issue 11, Pages 1649-1667Publisher
OXFORD UNIV PRESS
DOI: 10.1093/comjnl/bxt075
Keywords
inertial sensors; accelerometer; gyroscope; magnetometer; wearable sensors; body sensor networks; human activity classification; classifiers; cross validation; machine learning environments; WEKA; PRTools
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Funding
- Scientific and Technological Research Council of Turkey (TUBITAK) [EEEAG-109E059]
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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.
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