基于物理的贝叶斯融合后处理器,用于使用表面电肌图进行增强的步态相识别
概括
这项研究引入了基于物理的贝叶斯融合 (PI-BF) 系统,以改善下肢假肢的肌电控制. PI-BF提高了步态识别的准确性和稳定性,确保了更安全,更可靠的辅助设备性能.
科学领域:
- 生物医学工程
- 机器人技术
- 康复技术
背景情况:
- 肌电模式识别系统对于控制下肢假肢和外骨至关重要.
- 肌电控制中的信号变化可能导致不安全,不自然的步态转变.
研究的目的:
- 开发和验证一个新的物理信息化贝叶斯融合 (PI-BF) 后处理器.
- 提高下肢辅助器件的安全性,可靠性和自然进展.
主要方法:
- 从表面电肌图 (sEMG) 信号中提取时间域 (TD) 和时间依赖功率谱描述器 (TD-PSD) 的特征.
- 使用SVM,ANN,KNN和CNN-LSTM模型进行分类.
- 将PI-BF后处理器应用于分类器输出,将其与贝叶斯融合和没有后处理进行比较.
主要成果:
- 在使用SVM的SIAT-LLMD数据集中,PI-BF的分类精度提高了5.5%,达到85%.
- 降低过渡检测差异 (TDD) 至0.1±59.8毫秒,并提高了5%的输出稳定性 (INS指数).
- 实现了90%左右的实时步行阶段识别准确度.
结论:
- PI-BF有效地抑制不稳定的过渡,并促进肌电控制中的自然步行进展.
- 拟议的PI-BF为改善下肢辅助器件的性能提供了一种实用而低复杂的解决方案.
- PI-BF提高了肌电控制系统的安全性,可靠性和实时性.
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