Alejandro Luzanto

Papers

Total Citations

H-Index

About

Alejandro Luzanto is a researcher at the forefront of human-robot interaction (HRI), specializing in affective computing and health-aware robotics. His work bridges the gap between deep learning and real-world robotic perception, with a primary focus on speech emotion recognition and automatic detection of respiratory distress. In his most-cited study (2024, 4 citations), Luzanto introduced a novel deep learning beamforming approach for speech emotion recognition in distant HRI scenarios, significantly improving the robustness of emotional state detection in noisy, dynamic environments. His earlier work (2023, 2 citations) adapted a telephony-based dyspnea detection technique for real HRI settings, demonstrating a practical system that re-recorded telephone datasets using a custom robotic platform. This contribution is particularly notable for its potential to enhance robot-assisted healthcare, enabling robots to identify respiratory distress in real-time during physical interactions. Luzanto’s research is distinguished by its emphasis on ecological validity—testing algorithms not in sterile labs but in realistic, static and dynamic HRI scenarios. His achievements underscore a commitment to making robots more perceptive and responsive to human health and emotional states, paving the way for safer, more empathetic autonomous systems.