一个基于深层信念网络的多式联络框架,用于预测人类运动意图.
Jiayi Li1, Jianhua Zhang2, Kexiang Li3
1School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401 China.
Biomedical engineering letters
|April 22, 2024
概括
这项研究开发了一个深度信念网络 (DBN),以准确预测下肢外骨的人类运动意图. 多式联络框架在识别行走模式和预测过渡方面取得了很高的准确性,提高了安全性.
科学领域:
- 机器人和人机交互的人机交互
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 准确预测人类的运动意图对于下肢外骨架的安全和无运行至关重要.
- 现有的方法可能难以识别不同的运动方式,并预测它们之间的过渡,特别是在不同的地形上.
研究的目的:
- 开发一种使用深度信念网络 (DBN) 的多式联络框架,用于识别人类运动模式和预测过渡任务.
- 在 DBN 框架内评估不同数据融合策略 (数据,特征和决策级别) 的有效性.
主要方法:
- 深度信念网络 (DBN) 被设计为一种多式联网框架,用于处理和整合各种移动数据来源.
- 研究了三种不同的融合策略,以优化网络在识别机动意图方面的性能.
- 该模型在公共数据集上进行了稳定状态和过渡运动状态的测试.
主要成果:
- 基于DBN的多式联运框架实现了高预测准确度:97.64% (用户依赖) 和96.80% (用户独立) 稳定状态机动.
- 该系统在过渡过程中表现出了卓越的性能,准确地预测了所有过渡,准确率为96.37% (用户依赖) 和95.01% (用户独立).
- 通过探索不同的融合策略,可以实现最佳的网络性能.
结论:
- 基于DBN开发的多式联络框架准确地预测了人类的运动意图,包括不同步态之间的复杂过渡.
- 这些发现强调了这个DBN模型在下肢外骨架中控制意志的潜力,改善了用户的安全性和互动.
- 拟议的方法显示了在辅助机器人和人类增强领域的现实应用的前景.
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