Scholarlink

This article presents a research project that aims to assist individuals with cerebral palsy to improve their communication abilities and achieve greater autonomy using electromyography (EMG) technology. EMG records the electrical activity of muscles, providing insights into muscle physiology and activation. The proposed system would filter EMG signals, adapt them to the user's needs, store data, and present it through a graphical interface. The system targets those with cerebral palsy, facing challenges performing body movements and communication. The article provides background on EMG and its applications, especially for those with disabilities. It outlines common motor impairments in cerebral palsy. The methodology involves classified EMG signals over time. Results demonstrate the superior performance of random forests over single decision trees for prediction. Overall, the project leverages EMG technology to enhance communication and autonomy for cerebral palsy patients.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

EMG Signal Classification Using Machine Learning for Augmentative Assistance in Cerebral Palsy

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Neri Ramirez-Lopez,
  • Bogart Yail Marquez,
  • Arnulfo Alanis,
  • Jose Sergio Magdaleno-Palencia

摘要

This article presents a research project that aims to assist individuals with cerebral palsy to improve their communication abilities and achieve greater autonomy using electromyography (EMG) technology. EMG records the electrical activity of muscles, providing insights into muscle physiology and activation. The proposed system would filter EMG signals, adapt them to the user's needs, store data, and present it through a graphical interface. The system targets those with cerebral palsy, facing challenges performing body movements and communication. The article provides background on EMG and its applications, especially for those with disabilities. It outlines common motor impairments in cerebral palsy. The methodology involves classified EMG signals over time. Results demonstrate the superior performance of random forests over single decision trees for prediction. Overall, the project leverages EMG technology to enhance communication and autonomy for cerebral palsy patients.