Classifying human activities with temporal extension of random forest

Shih Yin Ooi, Shing Chiang Tan, Wooi Ping Cheah

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

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%.

Original languageEnglish
Title of host publicationNeural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
EditorsKazushi Ikeda, Minho Lee, Akira Hirose, Seiichi Ozawa, Kenji Doya, Derong Liu
PublisherSpringer Verlag
Pages3-10
Number of pages8
ISBN (Print)9783319466804
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event23rd International Conference on Neural Information Processing, ICONIP 2016 - Kyoto, Japan
Duration: 16 Oct 201621 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9950 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Neural Information Processing, ICONIP 2016
Country/TerritoryJapan
CityKyoto
Period16/10/1621/10/16

Keywords

  • Classification
  • Human activity
  • Machine learning
  • Random forest
  • Temporal sequences

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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