European Journal of Scientific Research - EuroJournals
European Journal of Scientific Research - EuroJournals
European Journal of Scientific Research - EuroJournals
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432 Nooritawati Md Tahir, Aini Hussain, Salina Abdul Samad,<br />
Hafizah Husain and Mohd Yus<strong>of</strong> Jamaluddin<br />
interesting and remarkable performance is as in experiment 1 that contributed the best classification<br />
rate in each category. As a result, the combination <strong>of</strong> E1, E2, E3 is chosen as the optimized<br />
eigenpostures.<br />
Conclusion<br />
In conclusion, a task <strong>of</strong> classifying four main human postures namely standing, sitting, bending and<br />
lying position based on eigenvectors analysis is presented. As can be seen from the experimental<br />
results, eigenspace technique based on statistical analysis prior to classification can be employed for<br />
human posture classification with high degree <strong>of</strong> accuracy. The initial thirty five feature vectors<br />
suggested by the rule <strong>of</strong> thumbs <strong>of</strong> PCA namely the KG rule, Scree Test and Cumulative Variance are<br />
trimmed down to a new subset <strong>of</strong> twenty feature vectors via the ANOVA. Further, the MCP and<br />
homogeneous subset tests have lessen the feature selection to only four eigenpostures and confirmed<br />
that the unseen postures have been correctly classified by using only three combination <strong>of</strong><br />
eigenpostures as inputs to the neural network classifier. E1, E2, E3 are selected as the optimized<br />
combination. This suggests that the eigenspace technique along with statistical data analysis can be put<br />
into practice for posture recognition, which can lead to a wide variety <strong>of</strong> applications such as security<br />
systems, intruder’s alertness, gait analysis, action recognition, human computer interaction, action<br />
recognition for surveillance applications and tracking techniques for video coding, and image displays.<br />
The rules <strong>of</strong> thumb <strong>of</strong> PCA and statistical analysis have facilitated us to achieve the selection <strong>of</strong><br />
eigenpostures for classification <strong>of</strong> human postures efficiently.<br />
Acknowledgement<br />
This work was supported by MOSTI under the IRPA Grant No: 03-02-02-0017-SR0003/07-03. The<br />
authors also acknowledge Pr<strong>of</strong> Dr Burhanuddin Yeop Majlis as the Program Head and UiTM for the<br />
UiTM-JPA SLAB scholarship awards.