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Browsing by Author "Ojelade, Aanuoluwapo Ezekiel"

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    Evaluation of Markerless Motion Capture to Assess Physical Exposures During Material Handling Tasks
    Ojelade, Aanuoluwapo Ezekiel (Virginia Tech, 2024-03-12)
    Manual material handling (MMH) tasks are associated with the development of work-related musculoskeletal disorders (WMSDs). Minimizing the frequency and intensity of handling objects is an ideal solution, yet MMH remains an integral part of many industry sectors, including manufacturing, construction, warehousing, and distribution. Physical exposure assessment can help identify high-risk tasks, guide the development and evaluation of ergonomic interventions, and contribute to understanding exposure-risk relationships. Physical exposure can be evaluated using self-assessment, observational methods, and direct measurements. Nevertheless, implementing these methods in situ can be challenging, time consuming, expensive, and infeasible or inaccurate in many cases. Thus, there is a critical need to improve physical exposure assessments to protect workers and save costs. This dissertation assessed the accuracy of a markerless motion capture system (MMC) to quantify physical exposures during MMH tasks using three studies. Specifically, the first study investigated the performance of an MMC system, together with machine learning algorithms, for classifying diverse MMH tasks during a simulated complex job. In the second study, the feasibility of predicting dynamic hand forces was determined, using alternative measures, such as kinematics from MMC and/or in-sole pressure systems, coupled with a machine learning algorithm. Finally, in the third study, we systematically evaluated MMC for assessing biomechanical demands, by comparing outputs from a full-body musculoskeletal model driven by kinematic and kinetics from gold standard input and estimates derived from the MMC and in-sole pressure measurement system. Overall, the findings of these studies demonstrated the potential of using MMC to classify several common occupational tasks and to estimate the associated biomechanical demands for a given worker (automatically and with minimal physical contact). Additionally, the methods developed here can help stakeholders rapidly assess an individual worker's exposure to physical demands during diverse tasks.