Papers
2
Total Citations
46
H-Index
2
About
Zohaib Mushtaq is a researcher at the forefront of intelligent human–computer interaction (HCI) and environmental sensing. His primary research areas span biomedical signal processing, deep learning, and remote sensing, with a focus on translating physiological and spectral data into practical, real-world applications. Mushtaq’s most impactful work, “Spectral Image-Based Multiday Surface Electromyography Classification of Hand Motions Using CNN for Human–Computer Interaction” (2022, 42 citations), introduces a novel approach to myoelectric control. By converting multiday surface EMG signals into spectral images and classifying them with a convolutional neural network, he addresses a critical challenge in wearable prosthetics—robust, long-term gesture recognition. This work directly advances the reliability of robotic limbs and assistive devices for amputees. In parallel, his research on “Detection, Localization and Analysis of Oil Spills in Water Through Wireless Thermal Imaging and Spectrometer Based Intelligent System” (2019) demonstrates his versatility, applying wireless sensor fusion to environmental monitoring. Mushtaq’s contributions bridge the gap between machine learning and human-centric engineering, offering scalable solutions for both medical rehabilitation and ecological protection. His work is a compelling example of how deep learning can decode complex biological and environmental signals for tangible societal benefit.
Research Focus
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Top Papers
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