双边消除基于规则的有限类贝叶斯推理系统用于循环和线性行走预测
Wentao Sheng1, Tianyu Gao2, Keyao Liang3
1School of Mechanical Engineering, Jiangsu University of Technology (JSUT), Changzhou 213001, China.
Biomimetics (Basel, Switzerland)
|May 24, 2024
概括
这项研究引入了一种新系统,用于预测下肢辅助设备的走路过渡. 基于双边淘汰规则的有限类贝叶斯推理系统 (BER-FC-BesIS) 在预测即将到来的行走活动方面达到93.98%的准确性.
科学领域:
- 机器人技术和人机交互的人机交互
- 生物力学和辅助技术
- 机器学习用于预测系统
背景情况:
- 预测步行过渡对于下肢辅助设备的安全性和可用性至关重要.
- 当前的系统可能会与复杂的,非线性行走模式作斗争.
- 准确的预测增强了辅助设备和用户之间的无互动.
研究的目的:
- 开发一种用于预测线性和圆形行走之间的过渡的新系统.
- 利用惯性测量单元来增强步行活动识别.
- 提高下肢辅助设备的安全性和功能.
主要方法:
- 实现基于双边消除规则的有限类贝叶斯推理系统 (BER-FC-BesIS).
- 利用双边人体运动数据来提高预测准确度.
- 使用惯性测量单元进行实时运动捕获.
主要成果:
- 伯尔-FC-贝西斯实现了93.98%的预测准确度.
- 即将进行的步行活动的平均预测时间为119.32 ± 9.71 ms (左) 和113.75 ± 11.83 ms (右).
- 预测和实际时间之间的低平均时间差 (14.22 ± 3.74 ms左,13.59 ± 4.92 ms右).
结论:
- BER-FC-BesIS准确地预测即将到来的稳步步行活动,包括线性和圆形模式.
- 该系统提供了用于增强下肢辅助设备的创新功能.
- 这项研究有助于改善日常生活应用的非线性行走活动预测.
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