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Detecting trunk motion changes due to pregnancy using pattern recognition techniques.

Wendy Gilleard, Daniel T H Lai, Pazit Levinger

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 24, 2009
    PubMed
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    This study shows support vector machine (SVM) can accurately detect gait changes in pregnant women by analyzing trunk motion. This method may help identify risks for musculoskeletal injury during pregnancy.

    Area of Science:

    • Biomechanics
    • Human Movement Analysis
    • Pregnancy Studies

    Background:

    • Pregnancy causes anatomical changes affecting trunk mass distribution and gait.
    • Limited research exists on trunk motion adaptations during pregnancy.
    • Understanding these changes is crucial for maternal health and injury prevention.

    Purpose of the Study:

    • To investigate pattern recognition techniques for detecting differences in trunk kinematics during walking between pregnant and non-pregnant women.
    • To evaluate the efficacy of Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) in this differentiation.
    • To identify key kinematic features indicative of pregnancy-related gait alterations.

    Main Methods:

    • Employed Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) pattern recognition techniques.

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  • Analyzed trunk kinematics during walking in women in late pregnancy and a control group of nulliparous women.
  • Utilized feature selection to identify the most discriminative kinematic parameters.

    • Main Results:

    • SVM achieved higher accuracy (71.43%) than LDA (28.57%) in distinguishing trunk motion between groups.
    • Feature selection improved SVM accuracy to 95.24% using pelvic sagittal displacement and thoracic lateral tilt at heel contact.
    • Non-linear analysis may be necessary for optimal detection of pregnancy-related trunk motion changes.

    Conclusions:

    • SVM effectively differentiates pregnancy-related trunk motion changes during walking.
    • Identified specific kinematic features (pelvic sagittal displacement, thoracic lateral tilt) as key indicators.
    • Findings suggest potential for altered musculoskeletal loads and increased injury risk in pregnant women.