Daniela Cardone
Papers
12
Total Citations
335
H-Index
9
About
Daniela Cardone is a multidisciplinary researcher whose work sits at the intersection of affective computing, human-robot interaction, and neurorehabilitation. Her scholarship centers on equipping social robots with the ability to perceive and respond to human emotional states, leveraging cutting-edge technologies such as thermal infrared imaging and machine learning to bridge the gap between artificial and natural interaction. Her highly cited 2020 review on thermal infrared imaging-based affective computing (113 citations) established her as a leading voice in emotion-aware robotics, while subsequent work on platforms such as NAO and Pepper robots demonstrated practical implementations of these principles in educational and clinical settings. Cardone has made particularly significant contributions to pediatric rehabilitation, investigating how robotic-assisted gait training induces functional cortical plasticity in children with cerebral palsy using fNIRS neuroimaging, and conducting psychophysiological assessments through infrared imaging during therapy. More recently, her research has expanded into integrating machine learning to predict motor recovery in stroke survivors, underscoring her commitment to personalized, data-driven rehabilitation. With over 300 cumulative citations, Cardone's body of work meaningfully advances the development of empathetic, clinically effective robotic systems for vulnerable populations across the lifespan.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7Thermal Imaging Based Affective Computing for Educational Robot17 citations · 2019
- 8
- 9
- 10