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通过单位能耗和PSO优化SVM进行机器人脊椎板切割的动态骨识别
Heqiang Tian1, Jing Zhao2, Ying Sun2
1College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, 266590, China. tianhq26@sdust.edu.cn.
Scientific reports
|May 11, 2025
概括
这项研究为骨科机器人引入了一种新的骨识别模型,提高了脊椎板切割精度. 新模型通过准确识别骨类型,增强了手术期间的实时机器人调整.
科学领域:
- 整形机器人 整形机器人 整形机器人
- 手术技术 手术技术
- 生物医学工程 生物医学工程
背景情况:
- 当前的骨科机器人在脊椎减压手术期间,在实时骨层识别方面扎.
- 这种局限性阻碍了动态调整,与熟练的人类外科医生不同.
研究的目的:
- 开发一个先进的骨识别模型用于机器人脊椎板切割.
- 提高骨科机器人手术的精度和适应性.
主要方法:
- 使用了单位能耗特征向量和支持向量机 (SVM) 优化了粒子优化 (PSO).
- 分析了力信号,应用波段值降噪,并使用主要组件分析 (PCA) 进行特征提取和维度降低.
- 开发和优化基于SVM的骨识别模型.
主要成果:
- 优化的模型实现了90.64%的骨识别准确度,超过了传统方法 (83.56%).
- 通过实验交叉验证,通过实验交叉验证来证明分类准确度有7.08%的改善.
- 证实了用于实时动态调整的预测性骨识别模型的可行性.
结论:
- 开发的骨识别模型显著提高了机器人脊椎板切割的精度.
- 能够根据骨类型实时动态调整切割参数.
- 为更复杂和适应性的骨科机器人系统铺平了道路.
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