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Prediction of Intermuscular Co-contraction Based on the sEMG of Only One Muscle With the Same Biomechanical Direction of Action

N. Grimmelsmann, M. Mechtenberg, M. Vieth, B. Hammer, A. Schneider, in: U. Kuhl (Ed.), Shaping Trustworthy AI: Opportunities, Innovation, and Achievements for Reliable Approaches, DataNinja sAIOnARA Conference, 2024.

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Konferenzbeitrag | Veröffentlicht | Englisch
Autor*in
Grimmelsmann, NilsFH Bielefeld ; Mechtenberg, MalteFH Bielefeld ; Vieth, Markus; Hammer, Barbara; Schneider, AxelFH Bielefeld
Herausgeber*in
Kuhl, Ulrike
Abstract
Research aims to enhance physical abilities using exoskeletons and limb movement prediction. SEMG signals are used for intuitive control, but their measurement is limited to shallowly under-the-skin muscles, making deep muscle signals less frequently used. Here we extended a previously proposed method to train a virtual sensor for the difficult to access muscles (deep muscles e.g. brachialis). The method is extended from signals from the same muscle to intermuscular signals and the results confirm simple biomechanical assumptions. The trained virtual sensors are ready for further investigations by being used in a biomechanical model.
Erscheinungsjahr
Titel des Konferenzbandes
Shaping Trustworthy AI: Opportunities, Innovation, and Achievements for Reliable Approaches
Konferenz
DataNinja sAIOnARA 2024 Conference
Konferenzort
Bielefeld
Konferenzdatum
2024-06-25 – 2024-06-27
FH-PUB-ID

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Grimmelsmann, Nils ; Mechtenberg, Malte ; Vieth, Markus ; Hammer, Barbara ; Schneider, Axel: Prediction of Intermuscular Co-contraction Based on the sEMG of Only One Muscle With the Same Biomechanical Direction of Action. In: Kuhl, U. (Hrsg.): Shaping Trustworthy AI: Opportunities, Innovation, and Achievements for Reliable Approaches : DataNinja sAIOnARA Conference, 2024
Grimmelsmann N, Mechtenberg M, Vieth M, Hammer B, Schneider A. Prediction of Intermuscular Co-contraction Based on the sEMG of Only One Muscle With the Same Biomechanical Direction of Action. In: Kuhl U, ed. Shaping Trustworthy AI: Opportunities, Innovation, and Achievements for Reliable Approaches. DataNinja sAIOnARA Conference; 2024. doi:10.11576/DATANINJA-1168
Grimmelsmann, N., Mechtenberg, M., Vieth, M., Hammer, B., & Schneider, A. (2024). Prediction of Intermuscular Co-contraction Based on the sEMG of Only One Muscle With the Same Biomechanical Direction of Action. In U. Kuhl (Ed.), Shaping Trustworthy AI: Opportunities, Innovation, and Achievements for Reliable Approaches. Bielefeld: DataNinja sAIOnARA Conference. https://doi.org/10.11576/DATANINJA-1168
@inproceedings{Grimmelsmann_Mechtenberg_Vieth_Hammer_Schneider_2024, title={Prediction of Intermuscular Co-contraction Based on the sEMG of Only One Muscle With the Same Biomechanical Direction of Action}, DOI={10.11576/DATANINJA-1168}, booktitle={Shaping Trustworthy AI: Opportunities, Innovation, and Achievements for Reliable Approaches}, publisher={DataNinja sAIOnARA Conference}, author={Grimmelsmann, Nils and Mechtenberg, Malte and Vieth, Markus and Hammer, Barbara and Schneider, Axel}, editor={Kuhl, Ulrike Editor}, year={2024} }
Grimmelsmann, Nils, Malte Mechtenberg, Markus Vieth, Barbara Hammer, and Axel Schneider. “Prediction of Intermuscular Co-Contraction Based on the SEMG of Only One Muscle With the Same Biomechanical Direction of Action.” In Shaping Trustworthy AI: Opportunities, Innovation, and Achievements for Reliable Approaches, edited by Ulrike Kuhl. DataNinja sAIOnARA Conference, 2024. https://doi.org/10.11576/DATANINJA-1168.
N. Grimmelsmann, M. Mechtenberg, M. Vieth, B. Hammer, and A. Schneider, “Prediction of Intermuscular Co-contraction Based on the sEMG of Only One Muscle With the Same Biomechanical Direction of Action,” in Shaping Trustworthy AI: Opportunities, Innovation, and Achievements for Reliable Approaches, Bielefeld, 2024.
Grimmelsmann, Nils, et al. “Prediction of Intermuscular Co-Contraction Based on the SEMG of Only One Muscle With the Same Biomechanical Direction of Action.” Shaping Trustworthy AI: Opportunities, Innovation, and Achievements for Reliable Approaches, edited by Ulrike Kuhl, DataNinja sAIOnARA Conference, 2024, doi:10.11576/DATANINJA-1168.

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