A Micro-moment recommendation framework in industrial environments

Abstract

Today, a large part of the labor policies in the EU aim at extending the active participation of older (i.e. 50+) employees in the workforce in order to avoid the respective pressure on the national economies and health systems as well as potential shortcomings in qualified personnel due to demographical changes in the entire population. Preventing involuntary early retirement goes hand in hand with supporting self-sufficient and healthy living. The present work considers the use and exploitation of modern technological advancements to support the achievement of the above goal. Specifically, we propose a new approach to developing complex recommendation systems, which are capable of monitoring and supporting the daily activities of employees in a personalized manner, both at work and during their broader daily activities. The proposed approach is based on the new Micro-Moments (MiMos) concept for critical event recognition, incorporating multiple streams of complementary information from a distributed sensor network that is flowing into the system based on IoT technologies. The recommendation system follows a user-centered approach for providing (personalized) suggestions that support the occupational safety of users, improve their health and enhance their productivity, in a personalized way. This paper summarizes the concept of Micro-Moments (MiMos) and how it contributes to issuing recommendations based on specific user needs. We also present the current version and implementation of the system in the field of port logistics, where it is observed that recommendations delivered at the right time to the right person can help improve the efficiency of the workforce and extend its working capacity.