activity
20142026
most citedStress Detection Using Wearable Physiological and Sociometric Sensors

208 citations · 456 across the 16 of their papers we have counts for

collaborators

16 papers

cs.LG2026★ 208 cited

Stress Detection Using Wearable Physiological and Sociometric Sensors

Oscar Martinez Mozos, Virginia Sandulescu, Sally Andrews +4

Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in…

cs.AI2023

Qualitative Prediction of Multi-Agent Spatial Interactions

Sariah Mghames, Luca Castri, Marc Hanheide +1

Deploying service robots in our daily life, whether in restaurants, warehouses or hospitals, calls for the need to reason on the interactions happening in dense and dynamic scenes.…

cs.AI2023

A Neuro-Symbolic Approach for Enhanced Human Motion Prediction

Sariah Mghames, Luca Castri, Marc Hanheide +1

Reasoning on the context of human beings is crucial for many real-world applications especially for those deploying autonomous systems (e.g. robots). In this paper, we present a ne…

cs.RO2023★ 1 cited

Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios

Luca Castri, Sariah Mghames, Marc Hanheide +1

Identifying the main features and learning the causal relationships of a dynamic system from time-series of sensor data are key problems in many real-world robot applications. In t…

cs.RO2023★ 1 cited

Towards Long-term Autonomy: A Perspective from Robot Learning

Zhi Yan, Li Sun, Tomas Krajnik +2

In the future, service robots are expected to be able to operate autonomously for long periods of time without human intervention. Many work striving for this goal have been emergi…

cs.RO2023

From Continual Learning to Causal Discovery in Robotics

Luca Castri, Sariah Mghames, Nicola Bellotto

Reconstructing accurate causal models of dynamic systems from time-series of sensor data is a key problem in many real-world scenarios. In this paper, we present an overview based…