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Predicting the Extent of Co-Activation of One Muscle Head by sEMG-Measurements from the Other Muscle Head of the Biceps Brachii Using Linear Regression and Shallow Feedforward Neural Networks. In: iDaS Institute for Data Science Solutions of HSBI, Hochschule Bielefeld, eds. <i>Nr. 1 (2024): Kongress KI@HSBI2023 Solutions Im Fokus -Posterbeiträge-</i>. Vol 1. Bielefeld: Schriftenreihe des Institute for Data Science Solutions; 2024:6-8. doi:<a href=\"https://doi.org/ 10.60802/SIDAS.2024.1\"> 10.60802/SIDAS.2024.1</a>","chicago":"Grimmelsmann, Nils, Malte Mechtenberg, and Axel Schneider. “Predicting the Extent of Co-Activation of One Muscle Head by SEMG-Measurements from the Other Muscle Head of the Biceps Brachii Using Linear Regression and Shallow Feedforward Neural Networks.” In <i>Nr. 1 (2024): Kongress KI@HSBI2023 Solutions Im Fokus -Posterbeiträge-</i>, edited by iDaS Institute for Data Science Solutions of HSBI and Hochschule Bielefeld, 1:6–8. 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Schneider, “Predicting the Extent of Co-Activation of One Muscle Head by sEMG-Measurements from the Other Muscle Head of the Biceps Brachii Using Linear Regression and Shallow Feedforward Neural Networks,” in <i>Nr. 1 (2024): Kongress KI@HSBI2023 Solutions im Fokus -Posterbeiträge-</i>, Bielefeld, 2024, vol. 1, no. 2024, pp. 6–8."},"author":[{"last_name":"Grimmelsmann","orcid":"0000-0002-4864-4978","full_name":"Grimmelsmann, Nils","first_name":"Nils","id":"214493","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0002-4864-4978/work/163637503"},{"first_name":"Malte","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0002-8958-0931/work/163637504","id":"218573","last_name":"Mechtenberg","orcid":"0000-0002-8958-0931","full_name":"Mechtenberg, Malte"},{"first_name":"Axel","id":"213480","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0002-6632-3473/work/163637505","last_name":"Schneider","orcid":"0000-0002-6632-3473","full_name":"Schneider, Axel"}],"conference":{"end_date":"2023-11-30","start_date":"2023-11-30","location":"Bielefeld","name":"Kongress KI@HSBI2023 Solutions im Fokus"},"status":"public","intvolume":"         1","doi":" 10.60802/SIDAS.2024.1","page":"6-8","publisher":"Schriftenreihe des Institute for Data Science Solutions","date_updated":"2026-03-17T15:29:07Z","year":"2024","_id":"4832","language":[{"iso":"eng"}]},{"language":[{"iso":"eng"}],"year":"2024","_id":"4823","editor":[{"first_name":"Maria  ","full_name":"Pedro Guarino, Maria  ","last_name":"Pedro Guarino"},{"first_name":"Kazuhiro ","full_name":"Hotta, Kazuhiro ","last_name":"Hotta"},{"full_name":"Yousef , Malik ","last_name":"Yousef ","first_name":"Malik "},{"first_name":"Hui ","full_name":"Liu , Hui ","last_name":"Liu "},{"first_name":"Giovanni ","last_name":"Saggio ","full_name":"Saggio , Giovanni "},{"first_name":"Ana ","last_name":"Fred","full_name":"Fred, Ana "},{"first_name":"Hugo ","full_name":"Gamboa , Hugo ","last_name":"Gamboa "}],"conference":{"location":"Rome, Italy","name":"17th International Conference on Bio-inspired Systems and Signal Processing","start_date":"2024-02-21","end_date":"2024-02-23"},"status":"public","citation":{"ieee":"N. Grimmelsmann, M. Mechtenberg, M. Vieth, A. Schulz, B. Hammer, and A. Schneider, “Predicting the Level of Co-Activation of One Muscle Head from the Other Muscle Head of the Biceps Brachii Muscle by Linear Regression and Shallow Feedforward Neural Networks,” in <i>Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies</i>, Rome, Italy, 2024, pp. 611–621.","alphadin":"<span style=\"font-variant:small-caps;\">Grimmelsmann, Nils</span> ; <span style=\"font-variant:small-caps;\">Mechtenberg, Malte</span> ; <span style=\"font-variant:small-caps;\">Vieth, Markus</span> ; <span style=\"font-variant:small-caps;\">Schulz, Alexander</span> ; <span style=\"font-variant:small-caps;\">Hammer, Barbara</span> ; <span style=\"font-variant:small-caps;\">Schneider, Axel</span>: Predicting the Level of Co-Activation of One Muscle Head from the Other Muscle Head of the Biceps Brachii Muscle by Linear Regression and Shallow Feedforward Neural Networks. 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Gamboa  (Eds.), <i>Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies</i> (pp. 611–621). 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