INCODE – Application Area 3: Smart Worker Assistant

January 23, 2024



INCODE’s Application Area 3 aims to improve adaptive human-machine interaction in smart factories, while promoting healthy operators through the use of Edge-to-Cloud solutions. To achieve these goals, MADE Competence Center Industry 4.0, together with Politecnico di Milano is validating a Use Case (UC) that demonstrates how to reduce the fatigue and stress experienced by human operators during repetitive manual tasks.


Use Case and Application Scenario


UC in Application Area 3 uses advanced technologies to increase the efficiency of human operators in manufacturing by modifying the working environment. These technologies include cobots that can perform tasks side-by-side with humans – without the need for dedicated spaces – wearable technologies equipped with sensors that measure the wearer’s biometric parameters, and an exoskeleton.

The UC will be implemented in a “human in the loop” scenario, mirroring the work of a human operator performing a repetitive task involving the handling of material, equipped with an exoskeleton designed to relieve muscle effort. As soon as the sensors of the wearable device combine their variable in a fatigue-like situation, a cobot is re-instructed on the effective position where to deliver the goods to be moved, ensuring a safer handling by the operator and/or an alert to the operator to use the exoskeleton.In this case, the production system is re-instructed to handle the fatigue-like situation by slowing down the production speed of the line until the operator’s parameters return to the usual set or until the operator is replaced by the next shift’s colleague. It is important to note that the system only monitors the biometric parameters of the body, without being informed of the change of identity. Privacy issues are therefore mitigated, as no data is tagged with any parameter that could potentially be linked to a physical person.


Our colleague Maria Rossetti, Project manager at MADE explains the objectives of the smart worker assistant use case



INCODE unique contribution


However, the identification of the actual fatigue/stress status of operators relies on Machine Learning algorithms, which usually require significant amounts of data and computing power, which cannot always be guaranteed by the infrastructure installed on company premises. A scalable solution therefore involves deploying a cloud infrastructure to which the various data sources and users can connect in order to share and exploit the useful information derived from the data.

The human loop is therefore not limited to a logical level close to the device, but flows through a series of intermediate levels, ensuring uniform data semantics and common language interfaces, with the advantage of faster and scalable deployment. The use of mobile ICT protocols (i.e. 5G) by the INCODE solution also guarantees isolated data pipelines, guaranteeing the security of sensitive information contained in the company’s information systems.


Read the full post here.