Auflistung nach Autor:in "Lukowicz, Paul"
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- WorkshopAI and Health: Using Digital Twins to Foster Healthy Behavior(Mensch und Computer 2024 - Workshopband, 2024) Keppel, Jonas; Ivezić, Dijana; Gruenefeld, Uwe; Lukowicz, Paul; Amft, Oliver; Schneegass, StefanThis workshop brings researchers together to discuss and explore how artificial intelligence (AI) can be used to improve general health. During our workshop at the MuC conference, we will focus on three main areas: developing ethical AI health recommendations, exploring how smart technologies in our homes can influence our health habits, and understanding how different types of feedback can change our health behaviors. The workshop aims to be a space where various research areas meet, encouraging a shared understanding and creating new ways to use AI to encourage healthy living. By focusing on real-world applications of AI and digital twins, we seek to guide our discussions toward strategies that have a direct and positive impact on individual and societal health.
- KonferenzbeitragGame of TUK: deploying a large-scale activity-boosting gamification project in a university context(Mensch und Computer 2020 - Tagungsband, 2020) Müller, Julia; Sprenger, Max; Franke, Tobias; Lukowicz, Paul; Reidick, Claudia; Herrlich, MarcWe present Game of TUK, a gamified mobile app to increase physical activity among students at TU Kaiserslautern. The scale of our project with almost 2,000 players over the course of four weeks is unique for a project in a university context. We present feedback we received and share our insights. Our results show that location-based activities in particular were very popular. In contrast, mini-games included in the app did not contribute as much to user activity as expected.
- WorkshopbeitragHeterogeneous Data in Health Applications: An Algorithmic Approach Leveraging Continual Learning(Mensch und Computer 2024 - Workshopband, 2024) Liu, Mengxi; Karolus, Jakob; Zhou, Bo; Lukowicz, PaulOver the past decades, data-driven AI-based methods have been widely used in human activity recognition, leading to the successful fostering of healthy behavior through health applications. Apart from advanced neural network model algorithms, researchers also focused on novel and reliable sensing modalities to monitor more complex activities, resulting in even more diverse and heterogeneous data over time. Inference and drawing implications from those heterogeneous data stream is still a great challenge. Besides, the heterogeneous data stream's diverse dimension is another issue in making one neural model for continual learning. In this abstract, we present a recent algorithmic solution based on the novel Kolmogorov Arnold Network to address these issues simultaneously, leading to the efficient utilization of heterogeneous data in health applications.
- KonferenzbeitragMobile and embedded interactive systems(Informatik 2009 – Im Focus das Leben, 2009) Rohs, Michael; Holleis, Paul; Kranz, Matthias; Hußmann, Heinrich; Lukowicz, Paul
- KonferenzbeitragMobile and Embedded Interactive Systems (MEIS’09)(Informatik 2009 – Im Focus das Leben, 2009) Rohs, Michael; Holleis, Paul; Kranz, Matthias; Hußmann, Heinrich; Lukowicz, Paul