Multi-label EMG Classification of Isotonic Hand Movements: A Suitable Method for Robotic Prosthesis Control

Mendes, José Jair Alves; Pontim, Carlos Eduardo; Campos, Daniel Prado

doi:10.1007/978-3-030-70601-2_243

Cited by 1 publication

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“…Prior approaches to multi-label classification have primarily focused on the generation of composite gestures from individual components such as the construction of a fist gesture from the collective flexion of individuals digits [206], [207] or the classification of multiple axes of action of a single joint [200], [201]. In contrast, we focus on combining gestures that could be used for joint actions leveraging the knowledge of limb biomechanics to construct sub groups of gestures that are mutually exclusive in their actions.…”

Section: Model Selection For Gesture Expressivitymentioning

confidence: 99%

“…To our knowledge, only a single study has investigated multi-label classification using a problem transformation approach. In that study, separate classifiers for flexion and extension of each digit were employed to construct multi-digit movements, with application to prosthetic control [207]. To collect training data, these authors chose a small set of natural gestures; each gesture is labeled according to the flexion or extension of each finger, and they attempt to interpolate in order to classify the unseen single gesture classes.…”

Section: Introductionmentioning

confidence: 99%

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Reducing calibration effort for brain-computer interfaces

Smedemark-Margulies

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This thesis represents the culmination of my PhD journey. To begin, I want to thank my parents, siblings, extended family, and most of all, my wife Krista, for endless support and encouragement through this marathon process. I feel extremely grateful to have such wonderful people in my life.During my PhD, I have had the great privilege of working with a large set of talented researchers across several institutions. As you may know, conducting an extended academic research project involves many challenges. Timelines are often tight, experiments that 'should' succeed often fail, and Reviewer #2 usually hasn't had enough coffee. Despite these obstacles, I have been fortunate to benefit from and learn from the patience, enthusiasm, and creative ideas of my mentors and co-authors.First, I want to thank my advisors Deniz Erdo gmus ¸, Robin Walters, and Jan-Willem van de Meent. Without their support, none of the work in this thesis would have been possible. This includes mundane elements like funding and organization, and more exciting elements such as iterating on research questions and experiments. I have learned a tremendous amount from each of them, and having the benefit of advice and mentorship from three fantastic researchers with different backgrounds and expertise has been an important advantage for me.Next, I want to thank my co-authors 1 from research described in this thesis and from other projects. People are listed here according to their affiliation when we worked together.

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Section: Model Selection For Gesture Expressivitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reducing calibration effort for brain-computer interfaces

Smedemark-Margulies

View full text Add to dashboard Cite

show abstract

Multi-label EMG Classification of Isotonic Hand Movements: A Suitable Method for Robotic Prosthesis Control

Cited by 1 publication

References 9 publications

Reducing calibration effort for brain-computer interfaces

Reducing calibration effort for brain-computer interfaces

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