Papers
1
Total Citations
4
H-Index
1
About
Denis Broggini is a researcher whose work sits at the intersection of wearable sensing, human-robot interaction, and machine learning. His most-cited paper, "Learning to Detect Pointing Gestures From Wearable IMUs" (2018, 4 citations), introduces a learning-based system that uses data from inertial measurement units (IMUs) to detect when a user performs a pointing gesture. By employing a 1D convolutional neural network, Broggini and his co-authors achieve robust gesture detection and demonstrate its practical application in a human-robot interaction task, where a robot interprets a user's pointing command. This work is notable for bridging the gap between intuitive human communication and robotic responsiveness, offering a scalable, wearable solution that avoids the need for external cameras or complex setups. While his citation count is modest, the research is foundational for those exploring non-verbal, wearable-driven interfaces in robotics. Broggini’s contributions highlight a forward-thinking approach to making human-robot collaboration more natural and accessible, particularly in contexts where hands-free or mobile interaction is essential.
Research Focus
Key Achievements
Top Papers
- 1Learning to Detect Pointing Gestures From Wearable IMUs4 citations · 2018