基于深度神经网络的coot优化,使用快速里埃转换进行人手手势识别
Arumugam Arulkumar1, Palanisamy Babu2
1Department of Electrical and Electronics Engineering, Nehru Institute of Engineering and Technology, Coimbatore, India.
概括
这项研究引入了一种新的深度神经网络 (DNN),用于在截肢者中准确检测手部运动. 该方法达到95%的准确性,改善了假肢控制和肢体差异的个体的生活质量.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 康复技术 康复技术 康复技术
背景情况:
- 手动探测对于截肢者的假肢控制至关重要.
- 现有的手动检测算法通常是复杂的,缺乏准确性.
- 需要有效和准确的方法来解释残余肢体信号.
研究的目的:
- 开发和验证一个深度神经网络 (DNN) 模型来识别人类手部运动.
- 克服现有的复杂和耗时的算法的局限性.
- 改善对截肢者的假肢设备的控制.
主要方法:
- 用高通路Butterworth和低通路过器捕获和预处理电肌图 (EMG) 信号.
- 信号使用滑动窗口技术进行细分,并通过快速里埃转换提取特征.
- 库特优化算法选择了输入到深度神经网络分类器的最佳特征.
主要成果:
- 拟议的DNN模型实现了95%的准确性,0.05%的错误率,94%的精度和92%的特异性.
- 与现有方法相比,开发的方法表现出优越的性能.
- 该预测模型有效地识别了人类手的动作.
结论:
- 这种基于DNN的新方法为截肢者手部运动检测提供了高度准确和高效的解决方案.
- 这项技术有可能显著改善假肢控制和肢体截肢患者的生活质量.
- 该方法为基于EMG信号的假肢控制提供了一个有前途的预测模型.
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