Human–Robot Interaction (HRI) is a rapidly evolving research area focused on enabling intuitive, efficient, and reliable communication between humans and robotic systems. Unlike conventional robotic control interfaces, we propose a contactless control system that leverages American Sign Language (ASL) as a natural, non-intrusive, and accessible modality for remote robot operation. Our framework integrates computer vision, gesture recognition, and machine learning to achieve accurate real-time interpretation of ASL gestures and their direct translation into robotic commands. To ensure robust gesture understanding, the system employs a Spatial–Temporal Network that captures both the spatial relationships of hand and body positions as well as the temporal dynamics of gesture sequences. Recognized gestures are mapped to precise control commands that drive the motors of a robotic car, enabling responsive and accurate navigation based solely on sign-based inputs. Extensive experiments demonstrate that the proposed system achieves high gesture recognition accuracy across multiple conditions, including variations in speed, angle, and handedness, while maintaining safe and reliable robot operation. Tasks executed using this framework are performed consistently and accurately, validating its effectiveness. This paper highlights the potential of ASL-based, contactless robotic control to enhance accessibility, safety, and intuitiveness in human–robot interaction, paving the way for more natural and inclusive interfaces in autonomous systems.
Deaira Carrington, Wassila Lalouani· International Conference on...· 0 citations
Depression and anxiety disorders are among the most prevalent and debilitating mental health conditions worldwide, imposing substantial personal, social, and economic burdens. Although recent advances in Large Language Models (LLMs) have shown promise in supporting mental health assessment and intervention, existing approaches often lack contextual awareness, real-time adaptability, and privacy-preserving personalization. To address these limitations, we propose a novel, context-aware and privacy-preserving mental health evaluation architecture that synergistically integrates LLM-driven intelligence. The proposed system enables personalized, continuous, and stigma-free mental health support by combining structured multiple-choice questionnaires with advanced language models, including GPT-3.5-turbo and Groq, to analyze user inputs, identify behavioral patterns, and predict potential mental health conditions such as depression and anxiety. Furthermore, the platform provides individualized recommendations, including self-care strategies, lifestyle adjustments, mindfulness practices, and referrals to healthcare professionals when appropriate. Recognizing the critical importance of reliability in sensitive healthcare settings, we introduce an ensemble-based aggregation framework that explicitly incorporates classification confidence and uncertainty quantification across multiple LLMs. Experimental results demonstrate that the proposed approach outperforms existing LLM models. By prioritizing user anonymity and data privacy, the proposed system reduces psychological barriers to seeking mental health support and promotes early intervention.
Jashraj Jani, Sara Akif, Wassila Lalouani· International Conference on...· 0 citations