Shafivulla Mohammad

Papers

Total Citations

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About

Shafivulla Mohammad is a researcher at the forefront of assistive technology and human-machine interaction, with a focused expertise in surface electromyography (sEMG) signal processing and neural network applications. His work centers on developing intuitive control systems for individuals with spinal cord injuries (SCI), particularly through the creation of hands-free interfaces for robotic wheelchairs. In his most-cited paper, "sEMG Based Human Computer Interaction for Robotic Wheel Chair Using ANN" (2016), Mohammad pioneered a method to interpret hand gestures by analyzing muscle activity from just three arm muscles using Artificial Neural Networks (ANN), achieving a constraint-free user environment. His subsequent work, "Development of sEMG based human machine interface control system for robotic watch" (2016), introduced novel feature extraction techniques for EMG signal segmentation, specifically targeting noise-filtered electrode signals from the Abductor pollicis longus. With over 7 cumulative citations, Mohammad’s contributions are pivotal in bridging the gap between biomedical signal processing and practical rehabilitation robotics, offering scalable, non-invasive solutions that enhance mobility and independence for individuals with severe motor impairments.