An Approach Leveraging Deep Learning and Stream Reasoning for Dynamic Task Assignments Balancing Productivity and Well Being

Employee well-being is an increasingly crucial factor for sustainability and efficiency in modern industry, especially within the context of Industry 5.0, which places humans at the center of production processes. This article presents some innovative aspects of the InCoP platform, which is part of a more general project developed under the NRRP MUR initiative FAIR (Future AI Research): Green-aware AI, and integrates Stream Reasoning (SR) and Machine Learning (ML). In particular, we focus on the real-time monitoring of operators’ psychophysical well-being and optimization of task assignment in a real industrial bakery environment. Here, an Assignment Module integrates results from ML-based models, used to predict stress, with the deductive capabilities of the SR engine, used to dynamically reassign operators to departments and suggest breaks in cases of persistent or dangerous stress levels. The goal is to minimize worker stress and prevent overload, while maintaining adherence to the production plan. Notably, the dynamic assignment module is fully integrated within the InCoP platform and leverages also sensors data coming from smartwatches and environmental monitoring devices which are fed into the system through Kafka, Elasticsearch, and MongoDB based technologies.