BioE PhD Proposal Presentation- Felicia Davenport
Time and Date: 2:00 PM, Thursday, May 18th, 2023
Location: J. Erskine Love Building – Room 210
Zoom: https://gatech.zoom.us/j/98109368835?pwd=Tmtybmg2WDRrN2JTYU9TREVjQUM0dz09
Meeting ID: 981 0936 8835 | Passcode: 460155
Advisor:
Gregory Sawicki, Ph.D. (Georgia Institute of Technology)
Thesis Committee:
Aaron Young Ph.D. (Georgia Institute of Technology)
Omer Inan, Ph.D. (Georgia Institute of Technology)
Karl Zelik, Ph.D. (Vanderbilt University)
Ajit Chaudhari, Ph.D. (Ohio State University)
Joint Loading in Industrial Lifts: Informing Mitigation Strategies through Joint-Level Biomechanics
Work-related injuries due to overexertion remain a leading cause of health problems in manual occupations. Manual labor personnel often perform variations of repetitive lifting and twisting under loads throughout their workday. Prolonged exposure to mechanical loading can lead to strain in soft tissues and degradation in bones that can lead to prominent chronic ailments such as low back pain and osteoarthritis which continue to plague the workforce, with about 50% of reported injuries stemming from the back or knee. Chronic bone injuries are tricky to identify in the early stages as it’s difficult to measure in vivo and a considerable amount of deterioration is needed to register the pain. Joint contact forces capture the internal force felt by the bone and can be estimated through computational neuromusculoskeletal modeling methods. Thus, providing insight on internal joint loading. Therefore, there is a critical gap in understanding and mitigating injuries from chronic joint loading in the back and knee.
Despite initiatives implemented in the workplace to combat chronic overexertion injuries such as methodology training and commercial braces, the problems seem to prevail without a known resolution. In the first aim of the proposed project, I seek to develop a framework which characterizes joint contact forces across work-specific lifting tasks in the back and the knee and identifies tasks in need of assistance. Modern technology has shown promising results to addressing deficits in human capabilities. Exoskeletons have shown reductions in energy expenditure, muscle activity, and joint loading. Since muscle forces are known to be the dominate factor in contributing to joint contact forces, exoskeletons may be a suitable intervention to harmful joint loading. The second aim of my proposed work intends to investigate the effects of exoskeletons on joint contact forces. I hypothesize that prescribing a back and knee exoskeleton can reduce joint contact forces by lowering muscle activity in work-specific tasks. Advances in machine learning have also proven effective at estimating and predicting biological metrics such as kinematics and kinetics from wearable sensors. The objective of the third aim is to create a system to estimate joint contact forces using machine learning technologies and determine the minimal amount of input data required for reliable performance.
Electromyography (EMG) will be used to measure muscle activity and computational software (OpenSim and the Calibrated EMG-Informed Neuromusculoskeletal Modeling Toolbox (CEINMS)) will calculate joint kinematics, kinetics, and contact forces with (1) no exoskeleton, (2) an active knee exoskeleton, and (3) a passive back exoskeleton. Later, a wearable sensor-driven machine learning algorithm will be used to estimate joint contact forces. Successful completion of the aims will prove beneficial to ergonomists, clinicians, and applied engineers alike by informing rehabilitation strategies, exoskeleton design and controllers, and real-time biofeedback systems.