Development and design approach of an sEMG-based Eye movement control system for paralyzed individuals
DOI:
https://doi.org/10.62110/sciencein.jist.2024.v12.811Keywords:
EMG, Eye Movement, facial paralysis, biomedical signal, microcontrollerAbstract
A novel Surface Electromyography (sEMG) system has been innovatively designed for individuals with paralysis. This system utilizes EMG technology to detect and interpret muscle signals, translating them into functional control and communication. The process involves signal optimization through a pre-amplifier, noise reduction via an RC filter, and digital conversion using an analog-to-digital converter (ADC). A central microcontroller employs programming to map EMG patterns to actions, creating a direct user-system interface. We have developed a hardware module for testing purposes. The precise manipulation of the hardware module, perfectly aligned with the user's visual objectives, is the result of this complex integration. The suggested method basically creates a sophisticated interface that enables users to intuitively and successfully operate the hardware module through their eye motions, opening up new opportunities for improved interaction and communication. Real-time analysis and command execution enhance user experience, with a user-friendly display providing visual feedback for executed actions. This innovation enhances their quality of life, independence, and social engagement, bridging the gap between paralysis and active participation. Additionally, it holds broader implications for assistive technology and neuroengineering, inspiring further advancements in disability support and rehabilitation. The system's comfort-focused design incorporates fail-safe mechanisms, and its potential applications span communication, environmental control, and artistic expression. A streamlined calibration process enhances user autonomy, and our collaborative approach ensures alignment with clinical needs and daily life requirements.
URN:NBN:sciencein.jist.2024.v12.811
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Copyright (c) 2024 Yogesh Thakare, Rahul Kadam, Utkarsha Wankhade, Chetan Rawarkar, Pratik K Agrawal
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