Manar Jaradat

Papers

Total Citations

H-Index

About

Manar Jaradat is a researcher whose work lies at the intersection of human-computer interaction and biomedical signal processing, with a primary focus on hand gesture recognition. Her most-cited paper, "Features Selection for Force Myography Based Hand Gesture Recognition" (2023), addresses a critical challenge in assistive technology: improving the accuracy and efficiency of recognizing American Sign Language (ASL) gestures using force myography (FMG). By systematically identifying the most informative features from FMG signals, Jaradat’s work enhances the reliability of gesture-based interfaces, which have broad applications in robotics, game control, and communication for the deaf and hard-of-hearing. This contribution is particularly valuable for developing low-cost, non-invasive prosthetics and wearable devices. Though early in her career, with 2 citations on this key paper, Jaradat’s research demonstrates a clear impact on advancing practical, real-world gesture recognition systems. Her work underscores a commitment to making technology more accessible and intuitive, bridging the gap between signal processing and user-centered design. As she continues to explore feature selection and machine learning for FMG, Jaradat is poised to contribute significantly to the fields of rehabilitation engineering and human-robot interaction.