Improving regression-based myoelectric control: User compliance, simultaneity, and incremental learning
| dc.contributor.advisor | Scheme, Erik | |
| dc.contributor.author | Morrell, Christian | |
| dc.date.accessioned | 2025-05-22T13:27:36Z | |
| dc.date.available | 2025-05-22T13:27:36Z | |
| dc.date.issued | 2025-03 | |
| dc.description.abstract | Regression-based myoelectric control offers the potential for simultaneous, independent, proportional control, but current implementations are limited by inconsistent training protocols and robustness issues. This thesis aims to address these limitations by providing a novel training protocol that overcomes key robustness issues that have hindered regression-based myoelectric controllers for decades. Two studies are performed, one demonstrating the volatility of current training protocols and the other proposing an alternative paradigm leveraging context-informed incremental learning. Results first show that models trained using existing protocols are affected by simply changing the visual prompting style. In response, the novel training paradigm is shown to significantly improve performance compared to traditional approaches. Furthermore, multiple co-adaptive approaches are contrasted to demonstrate the importance of building tolerance to user behaviours into myoelectric controllers. The results presented in this work provide important considerations for training myoelectric controllers and emphasize the value of designing with the user in mind. | |
| dc.description.copyright | © Christian Morrell, 2025 | |
| dc.format.extent | xii, 80 | |
| dc.format.medium | electronic | |
| dc.identifier.uri | https://unbscholar.lib.unb.ca/handle/1882/38301 | |
| dc.language.iso | en | |
| dc.publisher | University of New Brunswick | |
| dc.rights | http://purl.org/coar/access_right/c_abf2 | |
| dc.subject.discipline | Electrical and Computer Engineering | |
| dc.title | Improving regression-based myoelectric control: User compliance, simultaneity, and incremental learning | |
| dc.type | master thesis | |
| oaire.license.condition | other | |
| thesis.degree.discipline | Electrical and Computer Engineering | |
| thesis.degree.grantor | University of New Brunswick | |
| thesis.degree.level | masters | |
| thesis.degree.name | M.Sc.E. |