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Efficient Classification Techniques of Human Activities from Smartphone Sensor Data using Machine Learning Algorithms


Affiliations
1 Department of Computer Science & Engineering, Islamic University, Kushtia, Bangladesh
     

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Increasing use of accelerometer and protractor sensors in recent years has created a field of study for the definition of human activities. This issue is tried to be solved by using machine learning methods. For this, it is solved by extracting different properties from the obtained signals, obtaining the characteristics specific to the activity and classifying these properties. In this study, the time and frequency domain properties of 4 different human activities were extracted, then a pre-treatment step was applied in accordance with the obtained feature set, and then the size was reduced with PCA and Fisher ‘LDA methods. The k-NN classifier and perceptron classifiers were designed for the obtained feature set and the classification process was performed. In this study, the classification success of these methods using different parameters has been examined and the results are shown.

Keywords

Feature Extraction, Fisher’s LDA, Gradient Descent Method, Human Activity Identification, Kessler’s Reconstruction, k-NN Classifier, PCA
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  • Efficient Classification Techniques of Human Activities from Smartphone Sensor Data using Machine Learning Algorithms

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Authors

Diponkor Bala
Department of Computer Science & Engineering, Islamic University, Kushtia, Bangladesh
G. M. Waliullah
Department of Computer Science & Engineering, Islamic University, Kushtia, Bangladesh

Abstract


Increasing use of accelerometer and protractor sensors in recent years has created a field of study for the definition of human activities. This issue is tried to be solved by using machine learning methods. For this, it is solved by extracting different properties from the obtained signals, obtaining the characteristics specific to the activity and classifying these properties. In this study, the time and frequency domain properties of 4 different human activities were extracted, then a pre-treatment step was applied in accordance with the obtained feature set, and then the size was reduced with PCA and Fisher ‘LDA methods. The k-NN classifier and perceptron classifiers were designed for the obtained feature set and the classification process was performed. In this study, the classification success of these methods using different parameters has been examined and the results are shown.

Keywords


Feature Extraction, Fisher’s LDA, Gradient Descent Method, Human Activity Identification, Kessler’s Reconstruction, k-NN Classifier, PCA

References