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A Study on the Effect of Adaptive Boosting on Performance of Classifiers for Human Activity Recognition

  • Kishor H. Walse
  • Rajiv V. Dharaskar
  • Vilas M. Thakare
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 469)

Abstract

Nowadays, all smartphones are equipped with powerful multiple built-in sensors. People are carrying these “sensors” nearly all the time from morning to night before sleep as they carry the smartphone all the time. These smartphone allow the data to be collected through built-in sensors, especially the accelerometer and gyroscope give us several obvious advantages in the human activity recognition research as it allow the data to be collected anywhere and anytime. In this paper, we make use of publicly available dataset online and try to improve the classification accuracy by choosing the proper learning algorithm. The benchmark dataset considered for this work is acquired from the UCI Machine Learning Repository which is available in public domain. Our experiment indicates that combining AdaBoost.M1 algorithm with Random Forest, J.48 and Naive Bayes contributes to discriminating several common human activities improving the performance of Classifier. We found that using Adaboost.M1 with Random Forest, J.48 and Naive Bayes improves the overall accuracy. Particularly, Naive Bayes improves overall accuracy of 90.95 % with Adaboost.M1 from 79.89 % with simple Naive Bayes.

Keywords

Human activity recognition (HAR) Smartphone Sensor Accelerometer Gyroscope 

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Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  • Kishor H. Walse
    • 1
  • Rajiv V. Dharaskar
    • 2
  • Vilas M. Thakare
    • 3
  1. 1.S.G.B. Amravati UniversityAmravatiIndia
  2. 2.DMAT-Disha Technical CampusRaipurIndia
  3. 3.P.G. Department of CSS.G.B. Amravati UniversityAmravatiIndia

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