A Deep Learning-Based Platform for Workers' Stress Detection Using Minimally Intrusive Multisensory Devices

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 3; p. 947
• Main Authors: Rescio, Gabriele, Manni, Andrea, Ciccarelli, Marianna, Papetti, Alessandra, Caroppo, Andrea, Leone, Alessandro
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01-02-2024
• Subjects: Accuracy; Data collection; Deep learning; Electrocardiography; Electrodes; Health aspects; Job stress; Neural networks; Physiology; Sensors; Skin; smart systems; Software; Stress; stress detection; Wearable computers; Work environment; Workers; workers’ health; smart systems; deep learning; sensors; workers’ health; stress detection
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24030947

The advent of Industry 4.0 necessitates substantial interaction between humans and machines, presenting new challenges when it comes to evaluating the stress levels of workers who operate in increasingly intricate work environments. Undoubtedly, work-related stress exerts a significant influence on individuals' overall stress levels, leading to enduring health issues and adverse impacts on their quality of life. Although psychological questionnaires have traditionally been employed to assess stress, they lack the capability to monitor stress levels in real-time or on an ongoing basis, thus making it arduous to identify the causes and demanding aspects of work. To surmount this limitation, an effective solution lies in the analysis of physiological signals that can be continuously measured through wearable or ambient sensors. Previous studies in this field have mainly focused on stress assessment through intrusive wearable systems susceptible to noise and artifacts that degrade performance. One of our recently published papers presented a wearable and ambient hardware-software platform that is minimally intrusive, able to detect human stress without hindering normal work activities, and slightly susceptible to artifacts due to movements. A limitation of this system is its not very high performance in terms of the accuracy of detecting multiple stress levels; therefore, in this work, the focus was on improving the software performance of the platform, using a deep learning approach. To this purpose, three neural networks were implemented, and the best performance was achieved by the 1D-convolutional neural network with an accuracy of 95.38% for the identification of two levels of stress, which is a significant improvement over those obtained previously.