European Journal of Scientific Research - EuroJournals

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