Papers
6
Total Citations
203
H-Index
6
About
Babak Taati is a leading researcher in rehabilitation robotics and computer vision, with a focus on improving stroke therapy outcomes. His major contributions center on developing automated systems to detect and categorize compensatory movements—where patients use unaffected muscles to compensate for impaired ones—during robotic rehabilitation. This work is critical because unchecked compensation can undermine therapy effectiveness. Taati’s 2017 paper on automatic detection of compensation during robotic stroke rehabilitation therapy (69 citations) and his introduction of the Toronto Rehab Stroke Pose Dataset (49 citations) have provided foundational tools and data for the field. His earlier vision-based posture assessment system (39 citations) demonstrated real-time monitoring capabilities, while his work on haptic device dynamics (29 citations) advanced robotic hardware understanding. More recently, Taati has explored visual attention modeling with his 2025 paper on Saliency Unification Through Mamba (10 citations), showing his evolving research scope. His contributions have been widely cited, reflecting their impact on both clinical rehabilitation and robotics, and his work continues to shape how technology can enhance patient recovery.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5SUM: Saliency Unification Through Mamba for Visual Attention Modeling10 citations · 2025
- 6