Marouene Kaaniche

National Institute of Applied Science and Technology

Papers

Total Citations

H-Index

About

Marouene Kaaniche is a pioneering researcher at the intersection of edge computing, human-robot interaction, and biomedical signal processing. His work focuses on developing ultra-fast, real-time systems for gesture recognition using Electrical Impedance Tomography (EIT) measurements—a non-invasive technique that captures muscle activity patterns. Kaaniche’s most notable contribution, the "Ultra-Fast Edge Computing Approach for Hand Gesture Classification Based on EIT Measurements" (2024), has already garnered 6 citations, demonstrating its immediate impact. This paper addresses a critical bottleneck in gesture-based robot control: achieving millisecond-level classification accuracy while maintaining low latency for applications in industrial automation and assistive robotics. By offloading computation to edge devices, Kaaniche’s approach enables intuitive, real-time human-robot interaction without relying on cloud infrastructure, making it highly scalable and practical. His work bridges the gap between advanced sensing technologies and deployable edge AI systems, offering a blueprint for future wearable robotics and prosthetic control. Kaaniche’s research is not only technically rigorous but also deeply application-driven, positioning him as a rising leader in the field of edge-enabled biomedical engineering.