| Project Detail |
The motor unit (MU) represents the final common pathway consisting of the motor neuron and all the muscle fibres it innervates. Force is controlled by recruiting MUs or modulating their discharge rate. Traditional MU behaviour investigations use intramuscular electrodes and signal processing which identifies only a few MUs close to the electrode. Recently, High-Density surface electrodes(HDsEMG) allowed for MU identification non-invasively, but it has limitations. Decomposition algorithms (DA) can with validity and reliability identify MUs, but this is primarily in healthy young males. Greater adipose tissue thickness increases signal filtering, leading to a lower amplitude MU action potential which is more difficult to detect with the current DAs. This challenge has left a knowledge gap in the study of MU behaviour in underrepresented populations (URP) (clinical, female, obese). This fellowship aims to optimize the state-of-the-art technology of HDsEMG and DA for a more diverse population. I aim to (1) identify limitations of HD-sEMG and DA in URPs; (2) adapt electrode configurations for URPs, and (3) optimize DA for individuals across URPs. This project is pertinent, as a large sector of the population is not studied, hindering MU behaviour research progression. I will focus on URPs where MU identification is challenging. I will fill this knowledge gap to allow the study of MU behaviour and assessment of all populations. Adapting this non-invasive technology is an important innovation which may be used as a model for diagnosis in clinical populations.
The quality and success of this project are ensured by collaborations between myself and experts in signal processing (my supervisor Dr. Negro) and a company leading in multichannel electrode design. Their knowledge and expertise along with the facilities will guarantee the success of this project. The training will provide me with substantial hard and soft skills to become an independent translational scientist. |
