Yahya Abbass
Papers
7
Total Citations
112
H-Index
4
About
Yahya Abbass is a leading researcher at the intersection of tactile sensing, embedded intelligence, and human-machine interaction. His work focuses on creating smart tactile systems that combine piezoelectric materials—such as PVDF polymers—with advanced machine learning to give robots, prosthetics, and virtual reality interfaces a sophisticated sense of touch. Abbass’s most cited paper (38 citations) demonstrates how embedding convolutional neural networks directly into tactile sensor hardware enables real-time, intelligent data decoding. He has also pioneered full-hand electrotactile feedback systems using electronic skin and matrix electrodes (22 citations), dramatically expanding the bandwidth of information that can be conveyed to users in teleoperation and VR. His experimental work on interface electronics for PVDF-based sensors (35 citations) has been critical in optimizing system performance beyond material selection alone. More recently, Abbass has applied machine learning—including support vector machines—to enable tactile sensors to discriminate material hardness (7 citations), a key capability for dexterous manipulation. His systematic investigations of sensor response to indentation and slippage further establish foundational knowledge for next-generation haptic interfaces.
Research Focus
Key Achievements
Top Papers
- 1Smart Tactile Sensing Systems Based on Embedded CNN Implementations38 citations · 2020
- 2
- 3
- 4
- 5
- 6
- 7Pilot Study: Experimental Analysis of PVDF Sensors Response to Slippage3 citations · 2025