The evolution toward Smart Production and Industry 4.0 integrates physical manufacturing systems with digital technologies and advanced analytics to create intelligent, adaptive, and human-centered production environments. Within this ecosystem, the human worker remains central, supported by data-driven assistive systems that aim to enhance decision-making, efficiency, and well-being. Despite rapid technological advancements, few studies have scientifically validated the effectiveness of these assistive systems, particularly in relation to human cognitive and emotional states.

This thesis aims to explore the applications and impacts of neurophysiological tools in manufacturing, emphasizing how they can complement behavioral and self-report measures to better understand workers’ cognitive workload, stress, attention, and mental fatigue. Neurophysiological indicators (such as EEG, heart rate variability, or skin conductance) offer real-time, objective insights into human performance, enabling the design of more adaptive and supportive human–machine interfaces. The proposed research will combine a systematic literature review, a case study in an industrial or simulated smart production environment, and the development of a conceptual framework for applying neurophysiology within manufacturing contexts. Ultimately, the study seeks to contribute to a deeper understanding of human-centered Smart Manufacturing, supporting the creation of safer, more efficient, and cognitively sustainable production systems driven by neurophysiological insight.