Joint Loading in Industrial Lifts: Informing Mitigation Strategies through Joint-level Biomechanics
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Davenport, Felicia R.
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Abstract
Manual labor professions require workers to perform tasks which comprise their musculoskeletal integrity and expose them to heightened risks of injury from overexertion. in professions It is known that repetitive load carriage, especially during asymmetric walking, lifting, and twisting among other locomotion modes can lead to the development of tissue and joint injuries from overexertion. While it is well-documented that joint kinematics, joint kinetics, and muscle activity can be indicators of injury predisposition, we believe that internal joint loading, joint contact forces (JCFs), may provide an alternative, under-the-skin, perspective on potential biomechanical biomarkers indicative of informing about the risks of joint injuries. There is a critical gap in understanding the internal joint loading experienced by the joint capsule from nearby tissues (i.e. muscles, ligaments, etc.) and external factors during manual labor tasks, as well as how injury mitigation solutions such as wearable technology influence JCFs and predisposition to injury.
This dissertation explores the relationship between joint-level and muscle-level mechanics and effort and how these influences resolve into the internal joint state of JCFs across a myriad of manual labor-inspired movements and tasks. The overarching objectives were to investigate whether assistive and informative intervention strategies can help alleviate high, and potentially hazardous JCFs. In Chapter 2, we develop a framework that maps JCFs to industry-relevant lifting tasks. With the use of computational neuromusculoskeletal solvers to optimize estimates of muscle forces and JCFs, we identified a subset of work-specific movements that expose manual laborers to higher risks of injury at their lower back and knee joints. Following this work, in Chapter 3 we investigated how the use of exoskeletons affects JCFs in the subset of injurious manual lifting tasks identified in Chapter 2. More specifically, we sought to uncover how motorized assistance from a knee exoskeleton and passive assistance from a soft, back exoskeleton influence internal lower back and knee joint loads. In Chapter 4, we leverage analyses performed in Chapters 2 and 3 to measure how wearable sensor outputs from muscles, segments, and ground reaction forces can inform users of internal joint forces during various movements as a final mitigation strategy. Using a deep learning model equipped to self-identify relevant features which map to estimates of JCFs, key findings demonstrated that muscle activations were imperative to reliable normal JCF estimation. The wearable sensors utilized in this work were not adequate inputs for shear JCF estimation; however, we did fin d that IMUs were a primary contributor to the better performing shear JCF estimates. All in all, this dissertation provides an informative lens on JCFs, a contributing factor to joint injuries, and potential effects of wearable technology on internal joint loading.
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2024-07-27
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Dissertation