基于机器学习和深度学习的肌电控制系统用于上肢康复使用EEG和EMG信号:系统性审查
Tala Zaim1, Sara Abdel-Hadi1, Rana Mahmoud1
1Department of Electrical Engineering, Qatar University, Doha 2713, Qatar.
Bioengineering (Basel, Switzerland)
|February 26, 2025
概括
机器学习和深度学习通过改进肌电控制系统来增强上肢康复. 这些先进的技术使用电脑学和电肌学信号解码运动意图,以更好地实现辅助设备的功能.
科学领域:
- 生物医学工程 生物医学工程
- 神经康复疗法 神经康复疗法
- 医疗保健中的人工智能
背景情况:
- 上肢残疾严重影响日常活动和生活质量.
- 有效的康复技术对于恢复运动功能至关重要.
- 肌电控制系统为辅助康复提供了一个有希望的途径.
研究的目的:
- 系统地审查机器学习和深度学习在肌电上肢康复中的应用.
- 在这些系统中分析电脑学和电肌学信号的使用.
- 识别用于辅助设备解码运动意图的进步和挑战.
主要方法:
- 对2015年1月至2024年7月期间发表的研究进行系统性文献综述.
- 包括14项专注于肌电控制上肢康复的研究.
- 分析机器学习和深度学习模型,包括LSTM,SVM和CNN.
主要成果:
- 机器学习和深度学习模型在肌电控制中表现出更高的准确性和效率.
- 像长期短期记忆网络,支向量机器和卷积神经网络这样的特定模型显示了改进的控制精度.
- 与计算模型集成的非侵入性信号采集可以提高康复设备的性能.
结论:
- 先进的计算模型显示了改善上肢康复结果的巨大潜力.
- 模型稳定性,计算复杂性和实时应用方面的挑战需要进一步研究.
- 弥合实验技术和实际临床应用之间的差距对于未来的进步至关重要.
相关概念视频
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Arrhythmias are categorized by their speed, rhythm, and origin. A slow heart...
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