Industrial intelligence in the care of workers' mental health: A review of status and challenges

Mental health is a current concern because people worldwide have been committed to disorders that impair lives as a whole, affecting emotional states, behaviors, and body responses. These disorders decrease worker's productivity, impact industries economically, and cause serious psycho-physical conditions. However, technological advances have leveraged the industry to a novel phase where digitalization and automation provide a new reality. Hence, this industrial transformation may contribute to assists human beings in the workplace with a focus on mental health. This article presents a systematic literature review to investigate studies regarding technologies employed in the care of worker's mental health and the industrial role in this scenario. Three general, three focused, and three descriptive questions highlight the academic progress of industrial concern on mental health, implemented systems and cases, and research challenges. As a result, the review discussed 31 studies, extracted from an initial corpus of 25269, ranging from January 2010 to November 2020. The studies approached stress as the most frequent mental issue in the industry and Support Vector Machine (SVM) as the most used machine learning algorithm, where biomarkers presented the primary data extractors to deal with this theme. Moreover, information fusion methods improved the accuracy of specific cases. However, a growing interest in mental health care has emerged only in recent years, and several challenges require efforts before applying systems in real industrial environments.