Harshala Gammulle

Vision Australia, University of Peradeniya

Papers

Total Citations

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About

Harshala Gammulle is a researcher whose work bridges computer vision, robotics, and artificial intelligence, with a primary focus on gesture recognition and autonomous navigation. Her most impactful contribution is the development of **TMMF (Temporal Multi-Modal Fusion)**, a pioneering framework for single-stage continuous gesture recognition. Published in 2021 and garnering **48 citations**, this work addresses a critical gap in the field: while most gesture recognition systems handle isolated gestures, TMMF enables real-time, seamless recognition of continuous gestures—a vital capability for applications in robotics and human-machine interaction. By fusing temporal and multi-modal data in a single stage, her method significantly improves both efficiency and accuracy. Earlier in her career, Gammulle explored intelligent control systems for mobile robotics, as seen in her 2015 paper on **fuzzy logic-based target tracking in dynamic, hostile environments** (9 citations). This work demonstrated how fuzzy logic can enable robots to navigate cluttered spaces while avoiding moving obstacles and hostile zones—a foundational contribution to autonomous navigation under uncertainty. Gammulle’s research is notable for its practical impact, directly addressing real-world challenges in human-robot interaction and autonomous systems. Her work continues to influence the development of more intuitive, responsive machines.