Chinmay Kapoor

Papers

Total Citations

H-Index

About

Chinmay Kapoor is an emerging researcher at the forefront of deep learning applications in human-centric computing. His primary research focuses on Human Activity Recognition (HAR), where he leverages advanced deep learning architectures to interpret and classify complex human movements from sensor data. Kapoor’s most cited work, “Analysis of Human Activity Recognition using Deep Learning” (2021, 15 citations), provides a critical comparative analysis of various deep learning models—including CNNs, RNNs, and LSTMs—for HAR tasks. This study systematically evaluates their accuracy and computational efficiency, offering a practical roadmap for deploying HAR systems in real-world scenarios such as robotics and the Internet of Things (IoT). By addressing the challenges posed by the growing deluge of sensor data, Kapoor’s contributions help bridge the gap between theoretical model performance and practical implementation. His work is particularly notable for its relevance to smart environments and assistive technologies, where precise activity recognition can enhance automation and user interaction. As a rising voice in the field, Kapoor’s research continues to influence the development of more robust and scalable deep learning solutions for understanding human behavior.