@article{Jekauc:2024:RecognizingAffectiveStates,

title = {Recognizing affective states from the expressive behavior of tennis players using Convolutional Neural Networks},

author = {Darko Jekauc and Diana Burkart and Julian Fritsch and Marc Hesenius and Ole Meyer and Saquib Sarfraz and Rainer Stiefelhagen},

doi = {10.1016/j.knosys.2024.111856},

issn = {0950-7051},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Knowledge-Based Systems},

volume = {295},

abstract = {This study describes an AI model by leveraging advanced Convolutional Neural Networks (CNNs) to recognize affective states in real-world sports settings, particularly tennis matches. In contrast to prior studies that primarily utilized data acquired from actors and rudimentary statistical methods, the present research emphasizes the analysis of bodily expressions in real-life contexts, aiming for a more naturalistic representation of human emotions. Our CNN-based models demonstrate an accuracy rate of up to 68.9%, outperforming or matching human observers in many instances. Intriguingly, both the machine learning models and human observers exhibited a shared propensity to more effectively identify negative affective states, which may be attributed to the more intense and straightforward expression of these states. These results not only advance the state of the art in affective state recognition but also pave the way for broader applications, including in healthcare and automotive safety sectors, thereby constituting a significant advancement in the development of sophisticated and universally applicable emotional recognition systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Felsch:2023:CariesVisionTransformer,

title = {Detection and localization of caries and hypomineralization on dental photographs with a vision transformer model},

author = {Marco Felsch and Ole Meyer and Anne Schlickenrieder and Paula Engels and Jule Schönewolf and Felicitas Zöllner and Roswitha Heinrich-Weltzien and Marc Hesenius and Reinhard Hickel and Volker Gruhn and Jan Kühnisch},

url = {https://www.nature.com/articles/s41746-023-00944-2},

doi = {10.1038/s41746-023-00944-2},

isbn = {2398-6352},

year  = {2023},

date = {2023-10-25},

journal = {npj Digital Medicine},

volume = {6},

issue = {1},

pages = {198},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ruiner:2023:JobDemandsTechnology,

title = {Job demands and resources when using technologies at work – development of a digital work typology},

author = {Caroline Ruiner and Christina Elisabeth Debbing and Vera Hagemann and Martina Schaper and Matthias Klumpp and Marc Hesenius},

url = {https://doi.org/10.1108/ER-11-2021-0468},

doi = {10.1108/ER-11-2021-0468},

isbn = {0142-5455},

year  = {2023},

date = {2023-01-01},

journal = {Employee Relations: The International Journal},

volume = {45},

number = {1},

pages = {190–208},

publisher = {Emerald Publishing Limited},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{diagnostics13233562,

title = {Automatized Detection of Periodontal Bone Loss on Periapical Radiographs by Vision Transformer Networks},

author = {Helena Dujic and Ole Meyer and Patrick Hoss and Uta Christine Wölfle and Annika Wülk and Theresa Meusburger and Leon Meier and Volker Gruhn and Marc Hesenius and Reinhard Hickel and Jan Kühnisch},

url = {https://www.mdpi.com/2075-4418/13/23/3562},

doi = {10.3390/diagnostics13233562},

issn = {2075-4418},

year  = {2023},

date = {2023-01-01},

journal = {Diagnostics},

volume = {13},

number = {23},

abstract = {Several artificial intelligence-based models have been presented for the detection of periodontal bone loss (PBL), mostly using convolutional neural networks, which are the state of the art in deep learning. Given the emerging breakthrough of transformer networks in computer vision, we aimed to evaluate various models for automatized PBL detection. An image data set of 21,819 anonymized periapical radiographs from the upper/lower and anterior/posterior regions was assessed by calibrated dentists according to PBL. Five vision transformer networks (ViT-base/ViT-large from Google, BEiT-base/BEiT-large from Microsoft, DeiT-base from Facebook/Meta) were utilized and evaluated. Accuracy (ACC), sensitivity (SE), specificity (SP), positive/negative predictive value (PPV/NPV) and area under the ROC curve (AUC) were statistically determined. The overall diagnostic ACC and AUC values ranged from 83.4 to 85.2% and 0.899 to 0.918 for all evaluated transformer networks, respectively. Differences in diagnostic performance were evident for lower (ACC 94.1-96.7%; AUC 0.944-0.970) and upper anterior (86.7-90.2%; 0.948-0.958) and lower (85.6-87.2%; 0.913-0.937) and upper posterior teeth (78.1-81.0%; 0.851-0.875). In this study, only minor differences among the tested networks were detected for PBL detection. To increase the diagnostic performance and to support the clinical use of such networks, further optimisations with larger and manually annotated image data sets are needed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hagemann:2023:MESTAT,

title = {Mental Stress and Strain Assessment in Digital Work},

author = {Vera Hagemann and Jonathan Meinecke and Martina Schaper and Christina Debbing and Caroline Ruiner and Matthias Klumpp and Marc Hesenius},

doi = {10.1026/0932-4089/a000387},

year  = {2023},

date = {2023-01-01},

journal = {Zeitschrift für Arbeits- und Organisationspsychologie A&O},

volume = {67},

number = {1},

pages = {16-30},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Schwenzfeier:2023:EmbeddingsPalletReID,

title = {Generating embedding spaces for re-identifying pallets from their chipwood patterns},

author = {Nils Schwenzfeier and Jérôme Rutinowski and Marc Hesenius and Christopher Reining and Maribel Acosta},

doi = {10.1016/j.engappai.2023.106905},

issn = {0952-1976},

year  = {2023},

date = {2023-01-01},

journal = {Engineering Applications of Artificial Intelligence},

volume = {126},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hoss:2023:PeriodontalBoneLoss,

title = {Detection of Periodontal Bone Loss on Periapical Radiographs - A Diagnostic Study Using Different Convolutional Neural Networks},

author = {Patrick Hoss and Ole Meyer and Uta Christine Wölfle and Annika Wülk and Theresa Meusburger and Leon Meier and Reinhard Hickel and Volker Gruhn and Marc Hesenius and Jan Kühnisch and Helena Dujic},

doi = {10.3390/jcm12227189},

issn = {2077-0383},

year  = {2023},

date = {2023-01-01},

journal = {Journal of Clinical Medicine},

volume = {12},

number = {22},

abstract = {Interest in machine learning models and convolutional neural networks (CNNs) for diagnostic purposes is steadily increasing in dentistry. Here, CNNs can potentially help in the classification of periodontal bone loss (PBL). In this study, the diagnostic performance of five CNNs in detecting PBL on periapical radiographs was analyzed. A set of anonymized periapical radiographs (N = 21,819) was evaluated by a group of trained and calibrated dentists and classified into radiographs without PBL or with mild, moderate, or severe PBL. Five CNNs were trained over five epochs. Statistically, diagnostic performance was analyzed using accuracy (ACC), sensitivity (SE), specificity (SP), and area under the receiver operating curve (AUC). Here, overall ACC ranged from 82.0% to 84.8%, SE 88.8-90.7%, SP 66.2-71.2%, and AUC 0.884-0.913, indicating similar diagnostic performance of the five CNNs. Furthermore, performance differences were evident in the individual sextant groups. Here, the highest values were found for the mandibular anterior teeth (ACC 94.9-96.0%) and the lowest values for the maxillary posterior teeth (78.0-80.7%). It can be concluded that automatic assessment of PBL seems to be possible, but that diagnostic accuracy varies depending on the location in the dentition. Future research is needed to improve performance for all tooth groups.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3532205,

title = {Software Engineering for Augmented Reality - A Research Agenda},

author = {Ingo Börsting and Markus Heikamp and Marc Hesenius and Wilhelm Koop and Volker Gruhn},

url = {https://doi.org/10.1145/3532205},

doi = {10.1145/3532205},

year  = {2022},

date = {2022-06-01},

journal = {Proc. ACM Hum.-Comput. Interact.},

volume = {6},

number = {EICS},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {Augmented reality changes the way we perceive reality and how we interact with computers. However, we argue that to create augmented reality solutions, we need to rethink the way we develop software. In this paper, we review the state of the art in software engineering for augmented reality applications, derive open questions, and define a research agenda. For this purpose, we consider different engineering phases and evaluate conventional techniques regarding their applicability for AR development. In requirements engineering, we found the integration of AR experts and the associated collaboration between actors to be of key aspect in the development process. Additionally, requirements about the physical world must be considered, which in turn has a huge impact on UI design. The relevance of the physical environment is not yet sufficiently addressed in applicable techniques, which also applies to current implementation frameworks and tools, complicating the AR development process. When evaluating AR software iterations, we found interaction testing and test automation to have great potential, although they have not yet been sufficiently researched. Our paper contributes to AR research by revealing current core challenges within the AR development process and formulating explicit research questions that should be considered by future research.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}