James Brady

About

James Brady is a researcher at the intersection of human-robot interaction, multimodal sensing, and rehabilitation robotics. His work focuses on developing adaptive robotic systems that can assess and respond to human physical and cognitive states, particularly fatigue. Brady’s major contributions include pioneering a multimodal framework that combines body postures, facial expressions, and EEG signals to predict cognitive task performance—a method that has garnered 23 citations and holds promise for personalized training in education and industry. He has also advanced robotic rehabilitation by using muscle fatigue as a trigger for adaptive assistance, with his 2019 paper on this topic receiving 6 citations. His 2020 study on a serious game-based human-robot framework for fatigue assessment (14 citations) further demonstrates his innovative approach to studying both physical and mental fatigue simultaneously. Brady’s kinematic estimation work for robotic manipulators (17 citations) showcases his technical depth in neural networks. His research is notable for its practical applications in rehabilitation and assistive technologies, aiming to improve user performance and recovery through intelligent, adaptive robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Multimodal approach for cognitive task performance prediction from body postures, facial expressions and EEG signal

   23 citations · 2018
2. 2
   Kinematic Estimation with Neural Networks for Robotic Manipulators

   17 citations · 2018
3. 3
   Towards a serious game based human-robot framework for fatigue assessment

   14 citations · 2020
4. 4
   Adaptive robotic rehabilitation using muscle fatigue as a trigger

   6 citations · 2019
5. 5
   Towards a robot-based multimodal framework to assess the impact of fatigue on user behavior and performance

   5 citations · 2019

Key Collaborators

Contact & Links