Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Maximilian P Oppelt"'
Publikováno v:
Frontiers in Computer Science, Vol 6 (2024)
Numerous studies have focused on constructing multimodal machine learning models for estimating a person's cognitive load. However, a prevalent limitation is that these models are typically evaluated on data from the same scenario they were trained o
Externí odkaz:
https://doaj.org/article/e3a3994d9fa34d1b95b19e432953fd97
Autor:
Maximilian P. Oppelt, Andreas Foltyn, Jessica Deuschel, Nadine R. Lang, Nina Holzer, Bjoern M. Eskofier, Seung Hee Yang
Publikováno v:
Sensors, Vol 23, Iss 1, p 340 (2022)
Driver monitoring systems play an important role in lower to mid-level autonomous vehicles. Our work focuses on the detection of cognitive load as a component of driver-state estimation to improve traffic safety. By inducing single and dual-task work
Externí odkaz:
https://doaj.org/article/2d9d37abb4f143d29a67c67e47b341e2
Autor:
Jingna Qiu, Maximilian P Oppelt, Michael Nissen, Lars Anneken, Katharina Breininger, Bjoern Eskofier
Publikováno v:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2022
Automated Electrocardiogram (ECG) classification using deep neural networks requires large datasets annotated by medical professionals, which is time-consuming and expensive. This work examines ECG augmentation as a method for enriching existing data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::71a95a61834e374e0788dc367aca1f9b
https://pubmed.ncbi.nlm.nih.gov/36086450
https://pubmed.ncbi.nlm.nih.gov/36086450
Publikováno v:
CinC
An essential part for the accurate classification of electrocardiogram (ECG) signals is the extraction of informative yet general features, which are able to discriminate diseases. Cardiovascular abnormalities manifest themselves in features on diffe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1745533ff066e2452d7da98ba3767be3
https://doi.org/10.22489/cinc.2020.133
https://doi.org/10.22489/cinc.2020.133