Papers
8
Total Citations
66
H-Index
5
About
Umme Zakia is a robotics and human-robot interaction researcher whose work bridges biosignal processing, machine learning, and control systems engineering. Her research primarily focuses on force myography (FMG) — a non-invasive wearable technology that detects muscle volumetric changes — and its application in enabling intuitive physical human-robot interactions (pHRI). Zakia has made significant contributions to developing data-driven models that estimate applied hand forces during dynamic human-robot collaboration, including pioneering deep domain adaptation and cross-domain generalization techniques that address the practical challenge of limited labeled training data. Her work on synthesizing FMG biosignals using unsupervised and semi-supervised approaches further demonstrates her commitment to making human-robot systems more accessible and robust in real-world scenarios. Notably, she contributed a publicly available FMG dataset supporting the broader research community. Her earlier work on hybrid PID-SMC control for robotic manipulators, her most-cited paper with 23 citations, highlights a strong foundation in classical and robust control theory. More recently, Zakia has extended her research toward occupational safety monitoring during pHRI activities. Collectively, her publications have accumulated over 60 citations, reflecting her growing influence in intelligent human-robot collaboration systems.
Research Focus
Key Achievements
Top Papers
- 1PID-SMC controller for a 2-DOF planar robot23 citations · 2019
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- 4Dataset on Force Myography for Human–Robot Interactions6 citations · 2022
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- 8Detecting Safety Anomalies in pHRI Activities via Force Myography2 citations · 2023