Ugrasen Suman
Papers
1
Total Citations
19
H-Index
1
About
Ugrasen Suman is a leading researcher at the intersection of deep learning, wearable technology, and edge computing. His primary focus lies in developing intelligent systems for human activity recognition (HAR), leveraging hybrid deep learning models to process data from wearable sensors in real time. In his most-cited work, "Hybrid Deep Learning-Based Human Activity Recognition (HAR) Using Wearable Sensors: An Edge Computing Approach" (2024), Suman introduced a novel framework that combines convolutional and recurrent neural networks to achieve high-accuracy activity classification while minimizing latency through edge-based processing. This contribution is pivotal for applications in healthcare monitoring, fitness tracking, and smart environments, where real-time, privacy-preserving analytics are essential. With 19 citations to this paper, his work is gaining traction among researchers seeking efficient, deployable solutions for ubiquitous computing. Suman’s research not only advances algorithmic performance but also addresses practical constraints of resource-limited devices, making him a key figure in the evolution of edge AI. His achievements underscore a commitment to bridging theoretical deep learning with tangible, real-world impact.
Research Focus
Key Achievements
Top Papers
- 1