Vikarn Bhakri
Papers
1
Total Citations
13
H-Index
1
About
Dr. Vikarn Bhakri is a researcher at the forefront of pediatric rehabilitation technology, specializing in lightweight deep learning architectures for action recognition. His most cited work introduces BabyNet, a computationally efficient neural network designed to recognize infant reaching actions in unconstrained environments. This innovation directly addresses a critical gap in the field: while most action recognition algorithms are optimized for adults, Bhakri’s work tailors these tools for pediatric applications, enabling future wearable robotic exoskeletons to better support infant motor development and rehabilitation. With 13 citations, this foundational paper demonstrates Bhakri’s impact in bridging computer vision and assistive robotics. His contributions are pivotal for creating autonomous, adaptive rehabilitation devices that can operate in real-world settings, offering new possibilities for early intervention in pediatric motor disorders. Bhakri’s research stands out for its focus on lightweight, deployable models, making advanced rehabilitation technology more accessible and practical for clinical and home environments.
Research Focus
Key Achievements
Top Papers
- 1