Wearable Technology for Smart Manufacturing in Industry 5.0

Tho Nguyen, Kim Duc Tran, Ali Raza, Quoc-Thông Nguyen, Huong Mai Bui & Kim Phuc Tran 

International Research Institute for Artificial Intelligence and Data Science, Dong A University, Da Nang, Vietnam

Chapter First Online: 02 June 2023

Part of the Springer Series in Reliability Engineering book series (RELIABILITY)

Abstract

The innovation of wearable Internet of Things devices has fuelled the transition from Industry 4.0 to Industry 5.0. Increasing resource efficiency, safety, and economic efficiency are some of the main goals of Industry 5.0. Herein, wearable Internet of Things devices is parallel to humans to optimize human tasks and meet a new Industry’s requirements. Integrating artificial intelligence algorithms and IoT into wearable technologies and the progress of sensors has created significant innovations in many fields, such as manufacturing, health, sports, etc.. However, wearable technologies have faced challenges and difficulties such as security, privacy, accuracy, latency, and connectivity. More specifically, the increasingly massive and complex data volume has dramatically influenced the improvement of the limits. However, these challenges have created a new solution: the federated Learning algorithm. In recent years, federated learning has been implemented with deep learning and AI to enhance powerful computing with big data, stable accuracy, and ensure the security of edge devices. In this chapter, the first objective is to survey the applications of wearable Internet of Things devices in industrial sectors, particularly in manufacturing. Second, the challenges of wearable Internet of Things devices are discussed. Finally, this chapter provides case studies applying machine learning, deep learning, and federated learning in fall and fatigue classification. These cases are the two most concerning work efficiency and safety topics in Smart Manufacturing 5.0.

Source: https://doi.org/10.1007/978-3-031-30510-8_11