PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition
Abstract
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Citation: D. Nshimyimana, V. F. Rey, S. Suh, B. Zhou and P. Lukowicz, “PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition,” 2025 International Conference on Activity and Behavior Computing (ABC), Al Ain, United Arab Emirates, 2025, pp. 1-11, doi: 10.1109/ABC64332.2025.11118365. keywords: {Accuracy;Training data;Self-supervised learning;Benchmark testing;Multitasking;Data augmentation;Mathematical models;Data models;Human activity recognition;Wearable sensors;Human activity recognition;Self-supervised learning;Physics-inspired neural networks;Wearable sensors;Pretext tasks},
Dr. Sungho Suh
Senior Researcher
Human Activity Recognition, Safe and Trusted Human Centric Artificial Intelligence in Future Manufacturing Lines
Prof. Dr. rer. nat. Paul (Pawel) Lukowicz
Professor (W3) “Embedded Intelligence”
Paul Lukowicz is Full Professor of AI at the Technical University of Kaiserslautern in Germany where he is heading the Embedded Intelligence group at DFKI. From 2006 till 2011 he has been full Professor (W3) of Computer Science at the University of Passau. He has also been a senior researcher (“Oberassistent”) at the Electronics Laboratory at the Department of Information Technology and Electrical Engineering of ETH Zurich Paul Lukowicz has MSc. (Dipl. Inf.) and a Ph.D. (Dr. rer nat.) in Computer Science a MSc. in Physics (Dipl. Phys.). His research focus are context aware ubiquitous and wearable systems including sensing, pattern recognition, system architectures, models of large scale self-organized systems, and applications. Paul Lukowicz coordinates the FP7-FET SOCIONICAL projects, is Associate Editor in Chief of IEEE Pervasive Computing Magazine, and has been serving as TPC Chair of a number of international events in the area