Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units
Author
Barshan, B.
Yüksek, M. C.
Date
2014-11Source Title
The Computer Journal
Print ISSN
0010-4620
Publisher
Oxford University Press
Volume
57
Issue
11
Pages
1649 - 1667
Language
English
Type
ArticleItem Usage Stats
122
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400
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Abstract
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. © 2013 © The British Computer Society 2013. All rights reserved.
Keywords
AccelerometerBody sensor networks
Classifiers
Cross validation
Gyroscope
Human activity classification
Machine learning environments
Magnetometer
PRTools
Wearable sensors
WEKA
Accelerometers
Bayesian networks
Decision trees
Neural networks
Principal component analysis
Support vector machines
Human activities
Inertial sensor
Learning environments
Computer aided instruction