title: Detecting trunk motion changes due to pregnancy using pattern recognition techniques.
abstract: There are anatomical changes during pregnancy due to the increased and altered mass distribution in the trunk that could lead to changes in gait. There is little research, however, regarding adaptations in trunk motion with pregnancy. In this paper, we investigated the application of two pattern recognition techniques: support vector machine (SVM) and linear discriminant analysis (LDA) to detect differences in trunk kinematics, when walking, between women in late pregnancy and nulliparous (control) women. Test results indicate that the SVM can identify the trunk motion of pregnant women from their counterparts with a better accuracy compared to the LDA (71.43% vs 28.57% respectively). Furthermore, with a feature selection technique applied, the accuracy improved to 95.24% % using only 2 features namely the pelvic sagittal plane displacement and thoracic lateral tilt displacement at heel contact. The results suggest that for better detection of trunk motion changes in pregnant women, non-linear analysis may be required. The SVM was able to effectively differentiate pregnancy related trunk motion changes during a walking task which may indicate altered musculoskeletal loads with potential for injury or pain.
authors.forename: Wendy
authors.initials: W
authors.lastname: Gilleard
authors.forename: Daniel T H
authors.initials: DT
authors.lastname: Lai
authors.forename: Pazit
authors.initials: P
authors.lastname: Levinger
authors.forename: Rezaul K
authors.initials: RK
authors.lastname: Begg
mesh_terms: D000465:Algorithms; D001696:Biomechanical Phenomena; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D015233:Models, Statistical; D009038:Motion; D016011:Normal Distribution; D010363:Pattern Recognition, Automated; D011187:Posture; D011247:Pregnancy; D012372:ROC Curve; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted