Voluntary or involuntary muscle activation can be captured by surface electromyography
(EMG), which detects muscle action potentials via sensors on the surface of the skin. The technique has been prominent in the study of physiological underpinnings of movement for over 80 years and continues to be an essential tool in scientific research. Its research topic applications include motor disorders caused by stroke, spinal cord injury, cerebral palsy, multiple sclerosis, and many others. Benefits of integrating surface EMG into healthcare have been extensively argued and supported by scientific research, but adoption in clinical settings has been frustratingly slow. The overall goal of this thesis is to advance the clinical adoption of surface EMG by developing techniques that emphasize accessibility and the needs of the end-user (i.e., clinicians). In the first chapter, this dissertation leverages theoretical and empirical literature concerning influencers of adoption, and published clinician perspectives, to determine an effective translation strategy. Developing enhanced therapeutic surface EMG techniques and complementary assessments techniques were identified as key strategic goals. In Chapter 2, I develop a new classification-based surface EMG biofeedback system designed to emphasize tailorability, flexibility, and accessibility. The system performed well during a single session in healthy participants and one individual with multiple sclerosis. In Chapter 3, tailored interventions were implemented across multiple sessions in a group of multiple sclerosis patients with severe motor impairment. Implementation was found to be feasible, and the classification record emerged as an efficient and intuitive means to monitor and assess characteristics of a training session. In Chapter 4, I develop and test an easy-to-replicate surface EMG acquisition approach, and an analysis method using simple cursor placements. The analysis method was reliable between raters and sessions in healthy male and female participants. Overall, this thesis contributes to the translation of surface EMG methods into clinical practice. / Thesis / Doctor of Science (PhD) / Surface electromyography (EMG) is the recording of electrical potentials within the muscle that drive muscle contraction, and ultimately movement. There are many surface EMG techniques that provide insightful glimpses of the processes governing movement, and they have long been used to study movement impairments caused by traumatic injuries,
neurodevelopmental disorders, and neurodegenerative diseases. Use of surface EMG to
inform treatment decisions and optimize therapeutic interventions may significantly
improve health outcomes. However, clinicians across the various healthcare fields have
been slow to take advantage of surface EMG, and it remains underutilized despite
significant efforts promote its use. The goal of this thesis is to develop accessible surface
EMG techniques that can be applied in therapy and assessment scenarios. Ultimately,
beyond the thesis, this work is intended to advance the clinical adoption of surface EMG
so that its benefits may be accessed by a greater portion of practicing clinicians and their
patients.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/29825 |
| Date | January 2024 |
| Creators | Toepp, Stephen |
| Contributors | Nelson, Aimee, Kinesiology |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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