Maya Rathore
Papers
1
Total Citations
19
H-Index
1
About
Maya Rathore is a researcher at the forefront of human activity recognition (HAR) and edge computing, with a focus on integrating hybrid deep learning models with wearable sensor data. Her most-cited work, "Hybrid Deep Learning-Based Human Activity Recognition (HAR) Using Wearable Sensors: An Edge Computing Approach" (2024, 19 citations), introduces a novel framework that combines convolutional and recurrent neural networks to achieve real-time, energy-efficient activity classification directly on edge devices. This contribution addresses critical challenges in latency and privacy for applications in healthcare, fitness tracking, and smart environments. Rathore’s research bridges the gap between advanced AI algorithms and practical deployment, demonstrating how lightweight models can maintain high accuracy while reducing computational overhead. Her work has been recognized for its potential to enable continuous, unobtrusive monitoring in resource-constrained settings. With a growing citation impact, Rathore is establishing herself as a key voice in the intersection of deep learning, wearable technology, and edge computing, paving the way for more responsive and autonomous health-monitoring systems.
Research Focus
Key Achievements
Top Papers
- 1