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    A GENERALIZED SOFTWARE SOLUTION FOR THE ESTIMATION OF JOINT MOMENTS: AN APPLICATION TO LIFTING

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    Date
    2013-08-06
    Author
    Kingston, David
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    Abstract
    Objective: To develop modular software to assess angular impulse and to determine the effect of a reduced dataset on the net angular impulse acting at the L5/S1 joint.

    Background. With the prevalence and incidence of lower back pain increasing annually, accurate assessment of physical job demands is needed. Many lab based approaches exist to measure the moments acting on the lower back, but require advanced and sensitive testing equipment. Of the methodologies currently used in industrial settings, most require significant contributions of time or money to be implemented. There is a need for cost and time effective methods to record a worker’s kinematic data over their whole shift.

    Methods: Twelve participants performed 12 consecutive lifts under five lifting conditions: SQ00 (squat 0kg); SQ04 (squat 4kg); SQ10 (squat 10kg); FP04 (fast squat 4kg); ST04 (stoop 4kg). Kinematic data of the upper limbs, head, and trunk was recorded with external load data and kinetic analysis was performed by implementing an extension of the Hof (1992) method called the lined-segment engine (LSE) to calculate the angular impulse (N•m•s) acting on the L5/S1 joint.

    Results: The LSE was sensitive to changes in load, lifting speed, and lifting posture (p < 0.05). There was no difference in dynamic, quasi-static, or static models when calculating angular impulse, but there was a difference in the L5/S1 angular impulse when the upper limbs were removed from the dynamic LSE model (p < 0.05).

    Conclusion: The LSE requires further refinement, but could be a generic approach to kinetic calculations. A scaled no-arms model for calculating the angular impulse acting on the low back could be used to assess field based lifting studies with 5.8% error.
    URI for this record
    http://hdl.handle.net/1974/8143
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    • Queen's Graduate Theses and Dissertations
    • School of Kinesiology & Health Studies Graduate Theses
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