Giovanni Ercolano
Papers
9
Total Citations
215
H-Index
5
About
Giovanni Ercolano is a leading researcher in socially assistive robotics (SAR), with a focus on developing intelligent, personalized robotic systems for elderly care and therapeutic support. His work centers on human-robot interaction, activity recognition, and adaptive behavior, aiming to make robots that seamlessly integrate into domestic environments. Ercolano’s most cited paper (72 citations) reports on a field study where a social robot provided personalized home-care support for the elderly, demonstrating the critical role of user-specific adaptation. He has also made significant contributions to understanding how user personality and activity influence comfortable human-robot distances (64 citations), and he developed a novel deep-learning approach combining CNN and LSTM for recognizing activities of daily living from 3D skeleton data (36 citations). His research extends to non-interactive robot tasks, analyzing user disengagement and distraction, and to gesture recognition for robot-led training of children with autism spectrum disorder. With over 200 total citations, Ercolano’s work is foundational in creating socially aware, non-intrusive robots that can monitor, assist, and engage users in meaningful ways, advancing the practical deployment of assistive robotics in real-world settings.
Research Focus
Key Achievements
Top Papers
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- 2User’s Personality and Activity Influence on HRI Comfortable Distances64 citations · 2017
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- 8A Two-Step Framework for Novelty Detection in Activities of Daily Living5 citations · 2018
- 9Socially Assistive Robot’s Behaviors using Microservices2 citations · 2019