Advanced Topics in AI and Robotics

Abstract

Wearable devices, combined with Artificial Intelligence (AI) methods, can bring significant and sustainable improvements to our lives – from improved patient monitoring and decreased healthcare costs to enhanced sports performance and improved quality of life. Affective computing utilizes the combination of wearables and AI to provide methods for tracking factors related to affective states (e.g., emotions, cognitive load, and stress). Such methods have valuable applicability, including improved humancomputer interaction and advanced mental-health management tools. In this talk, Dr. Gjoreski will introduce affective computing and will explain Machine Learning (ML) processing pipelines that can transform raw sensor data into valuable information related to human behavior and affective states. More specifically, he will present examples of classical (feature-based) ML, and deep learning approaches in affective computing. He will also discuss how these approaches can be made more privacy-friendly and explainable.

Short Bio

Martin Gjoreski received his Ph.D. degree in computer science from the Jožef Stefan Institute, Slovenia, as part of the Department of Intelligent Systems. Since 2021, he has been a Postdoctoral Researcher with the Faculty of Informatics, Università della Svizzera italiana, Switzerland. His research interest includes the development of machine learning methods for monitoring human behavior. Jointly with several research teams, he has won five machine-learning competitions, including the Sussex-Huawei Locomotion Challenge, in 2018 and 2019. In 2021, he was awarded the national award “Jožef Stefan golden emblem,” signifying an outstanding PhD thesis in Slovenia. In 2022, Dr. Gjoreski was ranked in the top 2% of scientists in the world (“single-year impact” category).

Link: https://martingjoreski.github.io/

Papers relevant for the talk

1. M. Gjoreski, M. Ž. Gams, M. Luštrek, P. Genc, J. -U. Garbas and T. Hassan, "Machine Learning and End-to-End Deep Learning for Monitoring Driver Distractions From Physiological and Visual Signals," in IEEE Access, vol. 8, pp. 70590- 70603, 2020, doi: 10.1109/ACCESS.2020.2986810.

2. H. Gjoreski et al. 2023. OCOsense Glasses – Monitoring Facial Gestures and Expressions for Augmented HumanComputer Interaction. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (CHI EA '23). Association for Computing Machinery, New York, NY, USA, Article 465, 1–4. https://doi.org/10.1145/3544549.3583918

3. Gjoreski, M., Kiprijanovska, I., Stankoski, S. et al. Facial EMG sensing for monitoring affect using a wearable device. Sci Rep 12, 16876 (2022). https://doi.org/10.1038/s41598-022-21456-1

The lecture will be held in English and is aimed at students and staff of the H-BRS. Interested parties are cordially invited.