Visualization of the test setup, where a crane lifts the dummy to simulate a fall near a bed equipped with sensors for data measurement.

AI-Driven Fall Detection System Developed by Prof. Dr. Bartz-Beielstein and his Team Achieves 98% Accuracy in TH Köln Study

The project „FallKI – Development of Sensor Technology and AI Algorithms for Fall Detection in the Vicinity of Nursing Beds“ was conducted by Prof. Dr. Axel Wellendorf from the Institute for General Mechanical Engineering at TH Köln, in collaboration with Prof. Dr. Thomas Bartz-Beielstein from the Institute for Data Science, Engineering and Analytics. The project partner was tecfor care GmbH, a manufacturer of nursing beds and care furniture. The project was funded by the Federal Ministry for Economic Affairs and Climate Action through the Central Innovation Program for SMEs (ZIM) with a grant of 670,000 euros.

CfP: 2025 Genetic and Evolutionary Computation Conference (GECCO 2025)

2025 Genetic and Evolutionary Computation Conference (GECCO 2025) https://gecco-2025.sigevo.org/HomePageMálaga, Spain (hybrid event)July 14-18, 2025 We are inviting you to submit … Mehr

Reading Recommendation: On the generalization of PINNs outside the training domain and the hyperparameters influencing it

The article examines the generalization of Physics-informed neural networks (PINNs) beyond training domains, emphasizing predictive accuracy and adherence to physical principles. It challenges the role of overparametrization, suggesting it may promote overfitting rather than enhancing generalization in scientific machine learning.

Interesting: Lecture Series in AI: “How Could Machines Reach Human-Level Intelligence?” by Yann LeCun

Yann LeCun’s lecture discusses the limitations of current AI, emphasizing the need for a cognitive architecture with a predictive world model. This model uses a Joint Embedding Predictive Architecture (JEPA) to enable planning and understanding, aiming for human-level intelligence.

New arXiv Preprint: Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution

The article explores the integration of Explainable Artificial Intelligence (XAI) in enhancing deep learning performance. Focusing on regression problems, it introduces a feature selection pipeline using Integrated Gradients and k-means clustering, applied to blade vibration analysis in turbo machinery development.