Statistical analysis approach for posture recognition

Nooritawati Md Tahir, Aini Hussain, Salina Abdul Samad, Hafizah Husain

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The aim of this study is to determine the best eigenfeatures of four main human postures based on the rules of thumb of Principal Component Analysis namely the KG-rule, Cumulative Variance and the Scree Test followed by statistical analysis. Accordingly, all three rules of thumb suggest the retention of only 35 main principle components or eigenvalues. Next, these eigenfeatures that we named as 'eigenpostures' are statistically analyzed prior to classification. Thus, the most relevant component of the selected eigenpostures can be ascertained. The statistical significance of the eigenpostures is determined using ANOVA. Further, a Multiple Comparison Procedure (MCP) and homogeneous subsets tests are performed to determine the number of optimized eigenpostures for classification. Artificial Neural Network (ANN) and Support Vector Machine (SVM) were employed for classification. Results attained that the statistical analysis has enabled us to perform effectively the selection of eigenpostures for classification of human postures.

Original languageEnglish
Title of host publication2nd International Conference on Signal Processing and Communication Systems, ICSPCS 2008 - Proceedings
DOIs
Publication statusPublished - 2008
Event2nd International Conference on Signal Processing and Communication Systems, ICSPCS 2008 - Gold Coast, QLD
Duration: 15 Dec 200817 Dec 2008

Other

Other2nd International Conference on Signal Processing and Communication Systems, ICSPCS 2008
CityGold Coast, QLD
Period15/12/0817/12/08

Fingerprint

statistical analysis
Statistical methods
statistical significance
Analysis of variance (ANOVA)
neural network
Principal component analysis
Support vector machines
Neural networks

Keywords

  • ANOVA
  • Artificial neural network
  • Principal component analysis
  • Statistical analysis
  • Support vector machine

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Communication

Cite this

Tahir, N. M., Hussain, A., Abdul Samad, S., & Husain, H. (2008). Statistical analysis approach for posture recognition. In 2nd International Conference on Signal Processing and Communication Systems, ICSPCS 2008 - Proceedings [4813712] https://doi.org/10.1109/ICSPCS.2008.4813712

Statistical analysis approach for posture recognition. / Tahir, Nooritawati Md; Hussain, Aini; Abdul Samad, Salina; Husain, Hafizah.

2nd International Conference on Signal Processing and Communication Systems, ICSPCS 2008 - Proceedings. 2008. 4813712.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Tahir, NM, Hussain, A, Abdul Samad, S & Husain, H 2008, Statistical analysis approach for posture recognition. in 2nd International Conference on Signal Processing and Communication Systems, ICSPCS 2008 - Proceedings., 4813712, 2nd International Conference on Signal Processing and Communication Systems, ICSPCS 2008, Gold Coast, QLD, 15/12/08. https://doi.org/10.1109/ICSPCS.2008.4813712
Tahir NM, Hussain A, Abdul Samad S, Husain H. Statistical analysis approach for posture recognition. In 2nd International Conference on Signal Processing and Communication Systems, ICSPCS 2008 - Proceedings. 2008. 4813712 https://doi.org/10.1109/ICSPCS.2008.4813712
Tahir, Nooritawati Md ; Hussain, Aini ; Abdul Samad, Salina ; Husain, Hafizah. / Statistical analysis approach for posture recognition. 2nd International Conference on Signal Processing and Communication Systems, ICSPCS 2008 - Proceedings. 2008.
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