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    <title language="eng">A Novel Framework for the Assessment of the Impact of Affective UI Sounds in a Driving Situation Through Virtual Reality and Physiological Signals</title>
    <abstract language="eng">The objective of this study was to develop an experimental framework that allows research on whether the combined use of a virtual reality (VR) driving simulator, electroencephalography signals collected during driving tests and designed attentional paradigms could be applied to assess the performance of affective warning sounds for re-channelling driver’s attention in certain driving situations. Three VR driving scenarios with different stimuli and two driving tasks were designed and implemented. Acquisition of driver’s physiological data and behavioural metrics were integrated to be gathered synchronously. In addition, an experimental protocol was designed for the assessment of the impact of affective warning sounds. The technical feasibility of the framework was evaluated with the results showing that the proposed framework enables the data acquisition and analysis for a variety of warning stimuli. After successful technical deployment, a study with experimental subjects will follow to evaluate the effectiveness of affective warning sounds.</abstract>
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    <author>Lisa Marie Sparrer</author>
    <author>Sven Winkelmann</author>
    <author>Alexander von Hoffmann</author>
    <author>Daniel Sánchez Morillo</author>
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      <language>deu</language>
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      <value>Driving simulator, attention, mental workload, sound, physiologic sensors, electroencephalogram, virtual reality</value>
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    <title language="eng">Non-Contact In-Car Monitoring of Heart Rate: Evaluating the Eulerian Video Magnification Algorithm in a Driving Simulator Study</title>
    <abstract language="eng">Monitoring drivers’ health is crucial for saving lives in emergencies and enabling in-car health applications. The state of the art in pulse monitoring is contact-based sensors which impair the driving experience and have to be applied manually before driving. This paper focuses on automated hyper parameter optimizing the Eulerian Video Magnification (EVM) algorithm, which detects heart rates through non-contact facial camera images, for use in driving scenarios. We conducted a user study where 21 participants performed a driving simulation while their heart rates were recorded by a wearable fitness tracker (serving as ground truth) and facial images with an RGB camera. Our findings indicate that, despite using the optuna library for hyper parameter tuning, the Eulerian Video Magnification algorithm is insufficient for accurate pulse detection in a driving simulator environment.</abstract>
    <parentTitle language="eng">Proceedings of Mensch und Computer 2024</parentTitle>
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    <author>Philipp Renner</author>
    <author>Johanna Gleichauf</author>
    <author>Sven Winkelmann</author>
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    <title language="eng">Using Node-RED as a Low-Code Approach to Model Interaction Logic of Machine-Learning-Supported eHMIs for the Virtual Driving Simulator Carla</title>
    <abstract language="eng">External Human-Machine Interfaces (eHMI) enable interaction between vehicles and Vulnerable Road Users (VRU), for example, to warn VRUs of the car’s presence. Warning systems should warn of the situation’s urgency, which can be achieved using Machine Learning (ML)-based VRU detection models. ML models and eHMI interaction concepts are usually developed by different teams and tested separately, often resulting in integration problems. This work contributes to a low-code approach to model interaction concepts involving ML models to enable end-to-end prototypes for early integration and User eXperience (UX) testing. We use flow-based modeling with Node-RED, the virtual driving simulator CARLA and YOLOv5 as state-of-the-art deep learning techniques for VRU detection. We show two scenarios (cornering lights and context-aware VRU warning) in an interactive demonstrator, meaning a manual live control of pedestrian and car. We consider our approach to model and evaluate interaction concepts without writing code feasible for non-computer scientists.</abstract>
    <parentTitle language="eng">AutomotiveUI '23 Adjunct: Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications</parentTitle>
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    <author>Sven Winkelmann</author>
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    <author>Dharani Deivasihamani,</author>
    <author>Alexander von Hoffmann</author>
    <author>Fabian Flohr</author>
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    <title language="eng">Using Mask R-CNN for Image-Based Wear Classification of Solid Carbide Milling and Drilling Tools</title>
    <abstract language="eng">In order to ensure high productivity and quality in industrial production, early identification of tool wear is needed. Within the context of Industry 4.0, we integrate wear monitoring of solid carbide milling and drilling cutters automatically into the production process. Therefore, we propose to analyze wear types with image instance segmentation using Mask R-CNN with feature pyramid and bounding box regression. Our approach is able to recognize the five most important wear types: flank wear, crater wear, fracture, built-up edge and plastic deformation. While other methods use image classification and classify only one wear type for each image, our model is able to detect multiple wear types. Over 35 models with different hyperparameter settings were trained on 5,000 labeled images to establish a reliable classifier. The results show up to 82.03% accuracy and benefit for overlapping wear types, which is crucial for using the model in production.</abstract>
    <parentTitle language="eng">Artificial Neural Networks in Pattern Recognition</parentTitle>
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