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Human Activity Recognition through Smartphone’s Tri-Axial Accelerometer using Time Domain Wave Analysis and Machine Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 127 - Number 18 |
| Year of Publication: 2015 |
| Authors: Sarthak Gupta, Ajeet Kumar |
10.5120/ijca2015906733
|
Sarthak Gupta, Ajeet Kumar . Human Activity Recognition through Smartphone’s Tri-Axial Accelerometer using Time Domain Wave Analysis and Machine Learning. International Journal of Computer Applications. 127, 18 ( October 2015), 22-26. DOI=10.5120/ijca2015906733
Abstract
Human activity recognition aims at capturing current state of human in the immediate environment. This article poses a novel approach to predict the human activity by exploiting the data collected from smartphone’s triaxial accelerometer sensor. This approach employs a time domain wave analysis on the collected data and extracts relevant features that intuitively distinguish various activities such as walking, standing, running, jogging and sitting. The method then classifies the extracted features using various Machine learning algorithms comprising of SVM, J48 and AdaBoost and Random Forest. This work attains a steadfast accuracy of 98.8283%. HAR has numerous uses, ranging from healthcare, HCI, ubiquitous networks, to entertainment. The need for ever-increasing accuracy levels is inevitable and this work serves the purpose.
References
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