Publikation
Wearable Sensor-Based Human Activity Recognition for Worker Safety in Manufacturing Line
Sungho Suh; Vitor Fortes Rey; Paul Lukowicz
In: John Soldatos. Artificial Intelligence in Manufacturing: Enabling Intelligent, Flexible and Cost-Effective Production Through AI. Pages 303-317, ISBN 978-3-031-46452-2, Springer Nature Switzerland, Cham, 2024.
Zusammenfassung
Improving worker safety and productivity is of paramount importance in the manufacturing industry, driving the adoption of advanced sensing and control systems. This concern is particularly relevant within the framework of Industry 5.0. In this context, wearable sensors offer a promising solution by enabling continuous and unobtrusive monitoring of workers' activities in the manufacturing line. This book chapter focuses on wearable sensor-based human activity recognition and its role in promoting worker safety in manufacturing environments. Specifically, we present a case study on wearable sensor-based worker activity recognition in a manufacturing line with a mobile robot. As wearable sensors comprise various sensor types, we investigate and compare sensor data fusion approaches using neural network models to effectively handle the multimodal sensor data. In addition, we introduce several deep learning-based techniques to improve the performance of human activity recognition. By harnessing wearable sensors for human activity recognition, this book chapter provides valuable insights into improving worker safety on the manufacturing line, aligning with the principles of the Industry 5.0 paradigm. The chapter sheds light on the potential of wearable sensor technologies and offers avenues for future research in this field.
Projekte
QUETAL - A multi-lingual template-based question/answering system for exploring large collections of annotated free text documents,
STAR - Safe and Trusted Human Centric ARtificial Intelligence in Future Manufacturing Lines
STAR - Safe and Trusted Human Centric ARtificial Intelligence in Future Manufacturing Lines