基于参数自更新机制模型的膝关节连续运动估计
Jiayi Li1, Kexiang Li2, Jianhua Zhang3
1School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China.
Bioengineering (Basel, Switzerland)
|September 28, 2023
概括
这项研究引入了一种新的方法,使用粒子群优化和深度信念网络来准确地估计表面肌电图 (sEMG) 信号的连续膝关节运动,改进康复应用.
科学领域:
- 生物医学工程 生物医学工程
- 康复技术 康复技术 康复技术
- 信号处理 信号处理
背景情况:
- 从表面电肌图 (sEMG) 估计人类连续关节运动对于智能康复至关重要.
- 传统模型与个体差异扎,并信号非静止性,限制了概括性.
- 精确的基于sEMG的运动估计对于推进人机交互至关重要.
研究的目的:
- 为人类膝关节关节开发一个强大的连续运动估计模型.
- 为了应对sEMG信号的非静止性和个体变异性的挑战.
- 提高基于sEMG的联合运动估计方法的通用性.
主要方法:
- 提出了一种融合粒子群优化 (PSO) 和深度信念网络 (DBN) 的新型模型.
- 该方法具有参数自更新机制,用于自适应DBN优化.
- 用高维信号特征重建进行了最佳估计.
主要成果:
- 拟议的PSO-DBN模型实现了膝关节关节运动的平均根平均平方误差 (RMSEs) 为9.42°和7.36°.
- 在实验评估中,性能超过了常见的神经网络方法的性能.
- 该方法证明了有效的自适应优化和特征重建.
结论:
- 开发的PSO-DBN模型显著提高了从sEMG的连续膝关节运动估计的准确性.
- 这种方法在不同学科中提供了更好的概括性.
- 这些发现为外骨架机器人技术中先进的人机交互提供了基础.
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