Decoding the silent language: A framework for advanced visual emotion recognition to enhance worker well-being on IIoT-enabled manufacturing

Abstract

The Industrial Internet of Things (IIoT), pivotal in the Industry 4.0 revolution, has transformed manufacturing, particularly assembly lines, by enabling real-time process monitoring and data analysis to enhance productivity. Yet, its potential impact on worker well-being has been less explored. As the National Association of Manufacturers anticipates an opening of 2.1 million unfilled jobs by 2030, the necessity for frameworks that not only understand but also improve employee well-being becomes apparent. We introduce a novel, non-intrusive visual emotion recognition framework that incorporates facial emotion recognition (FER) and emotional body posture recognition (EBPR) to assess workers’ psychological states. This initiative aims to mitigate human errors and accidents linked to stress or high cognitive load, thereby enhancing the manufacturing environment. Strategies such as optimized break time planning, line balancing, procedure modifications, and improved training are proposed to ensure both employee well-being and stakeholder benefits. This framework indirectly supports the industry’s efforts to address the looming challenge of unfilled jobs by fostering a supportive work environment that enhances job satisfaction, potentially making positions more appealing and retaining valuable talent. By bridging the gap between technological advancements and human-centric considerations, our approach underscores the significance of integrating IIoT solutions with employee welfare initiatives to address both productivity and workforce sustainability challenges.