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Classifying Human Activities with Temporal Extension of Random Forest

  • Shih Yin OoiEmail author
  • Shing Chiang Tan
  • Wooi Ping Cheah
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9950)

Abstract

Sensor-Based Human Activity Recognition (HAR) is a study of recognizing the human’s activities by using the data captured from wearable sensors. Avail the temporal information from the sensors, a modified version of random forest is proposed to preserve the temporal information, and harness them in classifying the human activities. The proposed algorithm is tested on 7 public HAR datasets. Promising results are reported, with an average classification accuracy of ~98 %.

Keywords

Human activity Classification Random forest Temporal sequences Machine learning 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Shih Yin Ooi
    • 1
    Email author
  • Shing Chiang Tan
    • 1
  • Wooi Ping Cheah
    • 1
  1. 1.Faculty of Information Science and TechnologyMultimedia UniversityMelakaMalaysia

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