Neuromusculoskeletal Biomechanics and Chiropractic (NeuBaC)

Mission:

Improve the human condition through movement, motor control, injury prevention, and improved rehabilitation.

Our Mission Through Neuromechanics

Neuromechanics research explores how the brain, nervous system, muscles, and joints work together to produce and control human movement. By examining the dynamic interactions between neural and biomechanical systems, our research contributes to a deeper understanding of movement and informs innovative approaches to health, performance, and rehabilitation. Specifically, our work helps to:

  • Understand/Advance understanding of movement disorders by identifying the neural and mechanical factors that contribute to impaired motor function.
  • Prevent injury through improved knowledge of movement patterns, biomechanics, and risk factors.
  • Improve rehabilitation strategies by developing evidence-based interventions that support recovery and restore function.
  • Enhance sport performance by uncovering factors that influence movement efficiency, coordination, and athletic achievement.
  • Drive innovation in Prosthetics and Exoskeletons, including prosthetics, orthotics, and wearable exoskeletons.
  • Inform robotics and human-machine systems by applying principles of human movement and motor control.

Through this research, we aim to advance scientific understanding of human movement while supporting the development of technologies and interventions that improve health, independence, and quality of life across diverse populations.

Learn more about our research

Nathan Schilaty

DC, PhD

Director and Associate Professor

Meet the Team

Latest News

Featured Publications

  • Tran M, Holler J, Moran B, Schilaty ND, Templeton JM. Predicting time to clearance of sport-related concussions using machine learning.. Digital health. 2026;12:20552076261450858. PMCID: PMC13191159
    Publisher's Version
    Publisher's Version

    OBJECTIVE: To evaluate whether integrating longitudinal clinical data improves machine learning (ML)-based prediction of time to medical clearance following sport-related concussion (SRC) and to identify clinical features most strongly associated with classification of either 'prolonged' recovery (≥ 30 days) or 'normal' recovery (< 30 days).

    METHODS: A retrospective cohort of 217 athletes (mean age 26.94 years) from the USF Concussion Center (2021-2025) was analyzed. Six ML classifiers were trained on Visit 1 features (n = 48) and combined Visit 1 + Visit 2 features (n = 95). Internal validation was performed using Leave-One-Out Cross-Validation (LOOCV).

    RESULTS: Prolonged recovery occurred in 81.1% of the cohort. Adding Visit 2 features improved accuracy in 66% of models, with XGBoost achieving the highest accuracy (0.84, +5% gain over Visit 1). Specificity remained low (0.00-0.34) due to class imbalance. VOR Vertical Headache and its change score were the most frequent predictors of prolonged recovery, present in 81% and 100% of models, respectively. Treatment presence between visits emerged as the strongest predictor of normal recovery.

    CONCLUSIONS: Longitudinal clinical data modestly improves ML-based SRC recovery predictions. Vestibulo-oculomotor symptoms - particularly headache provoked during vertical VOR testing - are robust prognostic indicators. These findings support the utility of granular VOMS subscores for early risk stratification and targeted rehabilitation. External validation is required before clinical deployment. Code: https://github.com/MeganTran6023/Sport-Related-Concussions_Machine-Lear…. IRB: USF STUDY003514.

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