Sajjad Taghvaei
Papers
9
Total Citations
142
H-Index
6
About
Sajjad Taghvaei is a robotics researcher whose work sits at the intersection of nonlinear dynamics, assistive robotics, and human-robot interaction. His key research areas include chaos theory for robotic surveillance, fall detection and prediction for elderly assistive devices, and control of human-walker coupled systems. His most cited work, “Using chaotic maps for 3D boundary surveillance by quadrotor robot” (64 citations), demonstrates an innovative application of chaos theory to enable unpredictable and efficient aerial monitoring. Taghvaei has made significant contributions to assistive technology, particularly through vision-based fall prediction using autoregressive-moving-average hidden Markov models (17 citations) and image-based fall detection and classification (13 citations). These works address the critical challenge of providing safe, dependable support for aging populations. His research on HMM-based state classification using PCA features (7 citations) and comparative studies of visual human state classification (5 citations) further establish his expertise in real-time user state recognition. Taghvaei’s recent work extends to adaptive neuro-fuzzy sliding mode control for wheelchair propulsion and optimal navigation control of endovascular microrobots, showcasing his versatility across robotic domains. With a publication record spanning from 2012 to 2024, Taghvaei continues to advance both theoretical and applied aspects of robotics for human assistance and safety.
Research Focus
Key Achievements
Top Papers
- 1Using chaotic maps for 3D boundary surveillance by quadrotor robot64 citations · 2018
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- 8Optimal Robust Navigation Control of an Endovascular Microrobot2 citations · 2020
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