Arjun Modi
Papers
1
Total Citations
13
H-Index
1
About
Arjun Modi is a researcher at the forefront of pediatric rehabilitation robotics and human action recognition. His work focuses on developing lightweight, computationally efficient algorithms to enable autonomous assistive technologies for infants and children. Modi’s major contribution is the creation of **BabyNet**, a pioneering deep learning framework specifically designed for infant reaching action recognition in unconstrained, real-world environments. This work, which has garnered 13 citations, directly addresses a critical gap in the field, where most action recognition models are tailored for adults. By enabling the accurate detection of reaching motions in infants, BabyNet lays the essential groundwork for future pediatric rehabilitation applications, such as wearable robotic exoskeletons that can adaptively support motor development. Modi’s research is notable for its practical, application-driven approach, bridging the gap between advanced computer vision and the pressing clinical need for early intervention tools. His contributions are poised to significantly impact the design of next-generation, child-friendly assistive devices, making rehabilitation more responsive and effective for the youngest patients.
Research Focus
Key Achievements
Top Papers
- 1