Pairwise Classification using Combination of Statistical Descriptors with Spectral Analysis Features for Recognizing Walking Activities

Authors

  • M. N. Shah Zainudin Faculty of Computer Science and Information Technology, UPM, 43400 Serdang, Selangor, Malaysia. Faculty of Electronic and Computer Engineering, UTeM, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia.
  • Md. Nasir Sulaiman Faculty of Computer Science and Information Technology, UPM, 43400 Serdang, Selangor, Malaysia
  • Norwati Mustapha Faculty of Computer Science and Information Technology, UPM, 43400 Serdang, Selangor, Malaysia
  • Thinagaran Perumal Faculty of Computer Science and Information Technology, UPM, 43400 Serdang, Selangor, Malaysia

Keywords:

HAR, Accelerometer, Inter-Class Similarities, Pairwise Classification, Random Forest,

Abstract

The advancement of sensor technology has provided valuable information for evaluating functional abilities in various application domains. Human activity recognition (HAR) has gained high demand from the researchers to undergo their exploration in activity recognition system by utilizing Micro-machine Electromechanical (MEMs) sensor technology. Tri-axial accelerometer sensor is utilized to record various kinds of activities signal placed at selected areas of the human bodies. The presence of high inter-class similarities between two or more different activities is considered as a recent challenge in HAR. The nt of incorrectly classified instances involving various types of walking activities could degrade the average accuracy performance. Hence, pairwise classification learning methods are proposed to tackle the problem of differentiating between very similar activities. Several machine learning classifier models are applied using hold out validation approach to evaluate the proposed method.

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Published

2017-09-15

How to Cite

Zainudin, M. N. S., Sulaiman, M. N., Mustapha, N., & Perumal, T. (2017). Pairwise Classification using Combination of Statistical Descriptors with Spectral Analysis Features for Recognizing Walking Activities. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-11), 55–60. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2738

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