172 citations · 274 across the 6 of their papers we have counts for
8 papers
Neuro-Symbolic Graph Transformers for Hierarchical Intention Recognition from Multimodal Human Activity Sequences
Farnaz Soleimani, Abdelghani Chibani, Yacine Amirat +1
Proactive human-robot collaboration requires autonomous systems to infer not only immediate actions but also the underlying hierarchical intentions governing human behavior. Curren…
Neuro-Symbolic Hierarchical Intention Anticipation in Human Behavior
Farnaz Soleimani, Abdelghani Chibani, Yacine Amirat +1
Assistive autonomous systems must anticipate human goals before an observed behavior is complete. This article formulates anticipation as goal inference from a partially observed m…
Compositional Benchmark Synthesis for Hierarchical Human Action Recognition
Farnaz Soleimani, Abdelghani Chibani, Yacine Amirat +1
Recognizing human behavior across levels of abstraction, from atomic actions to long-horizon intentions, requires data annotated along a semantic hierarchy. Large corpora provide i…
Generic Semi-Supervised Adversarial Subject Translation for Sensor-Based Human Activity Recognition
Elnaz Soleimani, Ghazaleh Khodabandelou, Abdelghani Chibani +1
The performance of Human Activity Recognition (HAR) models, particularly deep neural networks, is highly contingent upon the availability of the massive amount of annotated trainin…
Deep HMResNet Model for Human Activity-Aware Robotic Systems
Hazem Abdelkawy, Naouel Ayari, Abdelghani Chibani +2
Endowing the robotic systems with cognitive capabilities for recognizing daily activities of humans is an important challenge, which requires sophisticated and novel approaches. Mo…
An Event Calculus Production Rule System for Reasoning in Dynamic and Uncertain Domains
Theodore Patkos, Dimitris Plexousakis, Abdelghani Chibani +1
Action languages have emerged as an important field of Knowledge Representation for reasoning about change and causality in dynamic domains. This article presents Cerbere, a produc…
An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression
Dorra Trabelsi, Samer Mohammed, Faicel Chamroukhi +2
Using supervised machine learning approaches to recognize human activities from on-body wearable accelerometers generally requires a large amount of labelled data. When ground trut…
Joint segmentation of multivariate time series with hidden process regression for human activity recognition
Faicel Chamroukhi, Samer Mohammed, Dorra Trabelsi +2
The problem of human activity recognition is central for understanding and predicting the human behavior, in particular in a prospective of assistive services to humans, such as he…