Praveen Nuwantha
Papers
1
Total Citations
5
H-Index
1
About
Praveen Nuwantha’s research lies at the intersection of biomedical engineering and intelligent control, with a primary focus on developing assistive robotic systems for gait rehabilitation. His most cited work, “Comparison of Deep Neural Network Models and Effectiveness of EMG Signal Feature Value for Estimating Dorsiflexion” (2021, 5 citations), tackles a critical challenge in robotic ankle–foot orthoses (AFO) design: maintaining high gait estimation accuracy while minimizing sensor inputs. By systematically comparing deep neural network models and evaluating electromyography (EMG) signal features, Nuwantha demonstrated how to optimize prediction of dorsiflexion—a key movement for walking—using fewer, more efficient sensors. This contribution is vital for creating lightweight, cost-effective rehabilitation devices that can be deployed in clinical or home settings. His work bridges machine learning and biomechanics, offering practical pathways to enhance user comfort and device accessibility. Though early in his career, Nuwantha’s focus on sensor reduction without sacrificing performance signals a promising trajectory in human-centered robotics, with potential to improve quality of life for individuals with mobility impairments.
Research Focus
Key Achievements
Top Papers
- 1