PERFORMANCE EVALUATION OF CLASSIFIERS ON ACTIVITY RECOGNITION FOR DISASTERS MITIGATION USING SMARTPHONE SENSING

Authors

  • Sadiq, Fatai Idowu Faculty of Computing, University Teknologi, Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Ali Selamat UTM-IRDA Digital Media Center of Excellence, University Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Roliana Ibrahim UTM-IRDA Digital Media Center of Excellence, University Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

DOI:

https://doi.org/10.11113/jt.v77.6320

Keywords:

SmartPhone sensing, multitask activity recognition, disaster mitigation, classification, and performance evaluation

Abstract

Activity recognition (ARs) is a classification problem that cuts across many domains. The introduction of ARs accuracy which may be significantly low with decision tree algorithm and the use of smartphone sensing in previous studies has proven its relevance for effective disaster mitigation in our society. Smartphone sensing is an approach found to be useful for activity recognition to monitor people in large gatherings due to the power of embedded sensors on the handheld devices. In this paper, a multitask activity recognition architecture is proposed  for proper monitoring of people in large gatherings to control disaster occurrences in crowd, flood, road and fire accidents using related activity scenario in time of danger. We implement the proposed architecture to determine the outcome of activity recognized with K-nearest neighbour (KNN) for k= 3 and 4 to  compare performance to that of weka using accelerometer and digital compass (dc) sensors on the same dataset. The results of ARs accuracy of 100% and 99% in weka, 85% and 89% with KNN shows an improved performance in both tools. The performance of Multilayer Perceptron (MLP), Support Vector Machine (SVM), Naives baye (NB), Decision tree (DT), against KNN were investigated using precision, recall and f-measure in weka as well. The results show significant improvement with performance parameters on accelerometer and dc against the use of accelerometer sensor only with KNN and DT having low number of classified activity recognized on training and testing data. 

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Published

2015-11-17

Issue

Vol. 77 No. 13: Technology-Driven Sustainable Development in Sciences & Engineering Vol. 2

Section

Science and Engineering

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Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.

How to Cite

PERFORMANCE EVALUATION OF CLASSIFIERS ON ACTIVITY RECOGNITION FOR DISASTERS MITIGATION USING SMARTPHONE SENSING. (2015). Jurnal Teknologi (Sciences & Engineering), 77(13). https://doi.org/10.11113/jt.v77.6320