AI4WORK

Human-centric Digital Twin Approaches to Trustworthy AI and Robotics for Improved Working Conditions

In a rapidly evolving technological landscape, the collaboration between humans and machines poses a pressing challenge to the modern workforce. As artificial intelligence (AI) and robotics become integral to various industries, striking the right balance between human ingenuity and machine efficiency remains elusive. The need for optimal work-sharing methods becomes paramount, spanning from manual labour to intricate decision-making processes. In this context, the EU-funded AI4Work project aims to explore and implement practical solutions for the seamless collaboration between humans and AI/robots. The key challenge lies in developing versatile tools like the sliding work sharing (SWS) approach, adapting the balance between human and machine activities based on situational context and interactions.

Project domains

Interaction & Enabling Technologies
AI / ML
Digital Twins
Robotics
Purposes
Working

Publications

Title Type of output
Manufacturing workers fatigue: an exploratory study on predictive machine learning and cross-subject generalization with implications for work design Conference proceedings
Exploration of core concepts required for mid- and domain-level ontology development to facilitate explainable-AI-readiness of data and models Conference proceedings
AI4WORK Project: Human Centric Digital Twin Approaches to Trustworthy AI and Robotics for Improved Working Conditions in Healthcare and Education Sectors Peer reviewed articles
Students' Burnout Symptoms Detection Using Smartwatch Wearable Devices: A Systematic Literature Review Peer reviewed articles
An Overview of Tools and Technologies for Anxiety and Depression Management Using AI Peer reviewed articles
A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements Book chapters
Adaptive Human-Robot Collaborative Missions using Hybrid Task Planning Conference proceedings
Temporal Task and Motion Planning with Metric Time for Multiple Object Navigation Conference proceedings

Do you want to learn more or discuss about this project?
Visit the Openverse Wiki page for AI4WORK

Disclaimer and intellectual property acknowledgement: the materials showcased on this page have been produced by third parties, and the OPENVERSE consortium accepts no liability for any inaccuracies therein. Responsibility for the content of each asset rests solely with its original creator. The inclusion of these outputs on this page does not, in any way, constitute an endorsement on the part of the OPENVERSE consortium. All intellectual property rights relating to the content published here remain vested in their respective owners. This page should be understood simply as a practical point of access.