Mohammad Nabati

Papers

Total Citations

H-Index

About

Mohammad Nabati is a researcher at the forefront of WiFi-based human activity recognition (HAR), a transformative technology with applications spanning health monitoring, smart cities, and human-computer interaction. His work focuses on leveraging Channel State Information (CSI) from WiFi signals to detect and classify human movements without the need for wearable sensors or cameras. Nabati’s most-cited paper, “CSI-Based Human Activity Recognition using Convolutional Neural Networks” (2021, 28 citations), demonstrates his pioneering use of deep learning to interpret WiFi signal patterns for HAR. He further refined this approach in “A CSI-Based Human Activity Recognition Using Canny Edge Detector” (2024), integrating edge detection techniques to enhance signal feature extraction. With a total of 30 citations across his key works, Nabati’s contributions are gaining traction in the growing field of contactless sensing. His research is particularly notable for its potential to enable privacy-preserving, low-cost monitoring systems in healthcare and smart environments, making him a rising voice in the intersection of wireless communications and artificial intelligence.