Parisa Fard Moshiri

Papers

Total Citations

H-Index

About

Parisa Fard Moshiri is a researcher at the forefront of **WiFi-based Human Activity Recognition (HAR)** and **human-computer interaction**, specializing in non-invasive sensing technologies. Her major contributions lie in leveraging **Channel State Information (CSI)** from commercial WiFi devices to recognize human movements without requiring cameras or wearable sensors. In her most-cited work, "CSI-Based Human Activity Recognition using Convolutional Neural Networks" (2021, 28 citations), she pioneered the application of deep learning to classify activities from WiFi signal patterns, demonstrating how CNNs can effectively decode complex CSI data for applications in health monitoring, smart homes, and robotics. She further advanced the field with "A CSI-Based Human Activity Recognition Using Canny Edge Detector" (2024), introducing edge detection techniques to enhance signal feature extraction. With a cumulative impact of over 30 citations, Moshiri’s research addresses critical challenges in privacy-preserving sensing and context-aware systems. Her work is particularly notable for its potential to transform elderly care and rehabilitation by enabling passive, ubiquitous activity monitoring. As an emerging voice in next-generation HAR, Moshiri continues to push the boundaries of how everyday wireless signals can be repurposed for intelligent, human-centric computing.