Christopher J. Nichols

BioE PhD Defense Presentation

Time and Date: 11:00 am, Tuesday, August 19, 2025.

Location: TSRB 523a

Zoom Link: https://gatech.zoom.us/j/94986457896 

Advisor: Omer Inan, Ph.D. (School of Electrical and Computer Engineering, Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech)

Committee Members:

Greg Sawicki, Ph.D. (George W. Woodruff School of Mechanical Engineering, School of Biological Sciences, Georgia Tech)

Josiah Hester, Ph.D. (School of Computer Science, Georgia Tech)

David Ewart, M.D. (Department of Rheumatology, Minneapolis Veteran’s Affairs Medical Center)

Minoru Shinohara Ph.D. (Wallace H. Coulter Department of Biomedical Engineering, School of Biological Sciences, Georgia Tech)

Advancing Wearable Sensing for Musculoskeletal Health Monitoring in Dynamic Everyday Environments

Current musculoskeletal healthcare is largely reactive, not proactive. Often, injuries and disease are detected only after they have progressed to late stages when irreparable damage has already occurred. This delay largely results from current methods of evaluating musculoskeletal health being costly, immobile, and inaccessible, limiting their feasibility for regular monitoring in everyday environments where early-stage detection and intervention is possible. Emerging sensing technologies such as joint acoustic emissions (JAEs) and localized electrical bioimpedance (EBI) offer an objective, wearable alternative to traditional approaches, but limited research into their sensitivity to early-stage musculoskeletal injury and disease during movements and conditions where they would be realistically used has constrained their viability beyond laboratory environments, where their potential advantages can be fully realized. To enable broader clinical and real-world impact, advancements are required in both the sensitivity and accessibility of these sensing technologies to capture meaningful information on musculoskeletal function under the unpredictable conditions of daily living. This dissertation entails demonstrating their sensitivity to early signs of musculoskeletal pathology, deepening understanding of the physiological signals they capture during functional movement, and developing wearable hardware and algorithms capable of reliable operation outside controlled environments. By addressing these challenges, this work takes a crucial step towards more accessible, affordable, and actionable musculoskeletal health monitoring beyond traditional clinical and research settings.