Our multidisciplinary research program comprises computational and experimental studies using non-invasive electromyography, motion capture, transcranial magnetic stimulation, and neuromuscular electrical stimulation used during motion with complex dynamics defined by virtual reality. This setup enables the shaping of human behavior in a way that minimizes noise across individuals while revealing meaningful biomechanical and neurological differences. The analysis of the obtained rich dataset using dynamical musculoskeletal modeling helps derive the functional mechanisms of the neural control of movement and quantify motor deficits caused by stroke and other conditions.
Talkington, W. J., Pollard, B. S., Olesh, E. V., and Gritsenko, V. (2015) Multifunctional setup for studying human motor control using transcranial magnetic stimulation, electromyography, motion capture, and virtual reality. JoVE (103), e52906, PMCID: PMC4692582
Taitano, R. I., Yough, M. T., Hanna, K., Korol, A. S., and Gritsenko, V. (2024) Setup for the Quantitative Assessment of Motion and Muscle Activity During Virtual Box and Block Test. JoVE: JoVE65736. DOI: 10.3791/65736
More videos of what we do are here!
NASA: BioAISense: Bioinspired Artificial Intelligence for autonomous assessment of Sensorimotor function
The goal is to derive solutions for describing quantitatively, autonomously, and in real-time the individual neuromechanical transformations using AI algorithms and tractable neuromuscular dynamic models.
AFOSR: Multimodal Framework for Sensation to Action Transformation
Major Goals: We propose to test a hypothesis that subdividing the overall complexity of musculoskeletal transformations into sequential dynamic transformations will enable biomimetic simulations of distributed neural processing along the neuroaxis.