Ahad Nasab
Papers
3
Total Citations
30
H-Index
3
About
Ahad Nasab is a researcher at the forefront of human-robot interaction and assistive technology, with a primary focus on enhancing prosthetic control and human activity recognition. His work centers on leveraging Inertial Measurement Unit (IMU) sensors and machine learning algorithms to bridge the gap between human intent and robotic response. His most impactful contribution, "Human Activity Recognition Using Machine Learning Algorithms Based on IMU Data" (2023, 24 citations), demonstrates how wearable sensor data can be used to accurately classify user movements, a critical capability for responsive prosthetic limbs and wearable robotics. Nasab has further refined this approach in "Enhancing Prosthetic Hand Control" (2024), where he applies machine learning to precisely classify hand orientation, directly improving dexterity for amputees. Beyond healthcare, his research in "Hand Gesture Based Motion Control of Collaborative Robot In Assembly Line" (2021) extends these principles to manufacturing, enabling intuitive, gesture-based control of industrial robots. By combining sensor fusion with advanced classification models, Nasab is creating more intuitive and adaptive interfaces that allow humans and machines to work together seamlessly, from the factory floor to the rehabilitation clinic.
Research Focus
Key Achievements
Top Papers
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