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优化基于机器学习和模拟算法的部外骨架辅助模式:一种个性化的方法来降低代谢成本
Arash Mohammadzadeh Gonabadi1,2, Iraklis I Pipinos3,4, Sara A Myers2,4
1Institute for Rehabilitation Science and Engineering, Madonna Rehabilitation Hospitals, Omaha, NE, United States.
Frontiers in robotics and AI
|October 10, 2025
概括
我们使用机器学习开发了一个新的框架来个性化关节外骨架的辅助,在模拟中显著降低了代谢成本. 这加快了外骨技术,用于康复和提高性能.
科学领域:
- 生物力学 生物力学
- 机器人技术 机器人技术 机器人技术
- 机器学习 机器学习
背景情况:
- 部外骨架减少了步行代谢成本,但需要用户特定的调.
- 个性化协助对于有效的外骨应用至关重要.
研究的目的:
- 开发一个基于模拟的框架,以优化关节外骨架的辅助.
- 结合机器学习和全球优化,实现个性化的外骨控制.
主要方法:
- 训练渐变增强 (GB) 模型对健康成年人的数据进行训练,以预测代谢成本.
- 评估了9个机器学习模型,其中GB显示的错误率最低 (0.66%RAEP).
- 评估了七个全球优化算法,以找到最佳的辅助参数.
主要成果:
- 引力搜索算法 (GSA) 预测了最大的代谢成本降低 (-1.06,~53%).
- 粒子集群优化 (PSO) 证明了最高的效率 (AUC = 0.24).
结论:
- 该框架简化了算法选择,并减少了用于外骨优化的实验力度.
- 模拟结果突出了在康复和绩效提升方面加速翻译的潜力.
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