下肢动力学和电肌学数据库,用于正常人行走相关活动中的下肢动力学和电肌学
Robert V Schulte1,2, Erik C Prinsen3,4, Leendert Schaake5
1Roessingh Research & Development, Enschede, 7522AH, The Netherlands. robert.v.schulte@gmail.com.
Scientific data
|July 14, 2023
概括
MyPredict数据库为活动预测提供了有价值的数据,包括55个个人的各种日常活动的测量. 这种广泛的数据集支持了肌电模式识别和控制系统的进步.
科学领域:
- 生物力学 生物力学
- 康复工程 康复工程
- 可穿戴技术可穿戴技术
背景情况:
- 活动预测对于开发智能假肢和整形器件至关重要.
- 现有的数据集可能缺乏强大的模型训练所需的运动和传感器组合的多样性.
- 需要标准化,全面的数据集来推进肌电控制研究.
研究的目的:
- 介绍和描述Roessingh研究和开发-MyLeg活动预测数据库 (MyPredict).
- 为培训和验证人类运动分析算法提供丰富的多模式数据集.
- 促进肌电模式识别的研究和数据驱动应用程序的开发.
主要方法:
- 在85次测量会议中从55名身体健康的受试者收集了数据.
- 使用了八个惯性测量单元 (IMU) 和各种表面肌电图 (sEMG) 传感器配置 (双极,多阵列,组合).
- 记录了各种活动,包括坐着,站着,走路 (地面,不平坦的地形),爬楼梯/坡道/下坡道,以及模拟的封闭空间.
主要成果:
- 数据集包括详细的动力学数据 (联合角度,加速,角速度,方向,虚拟标记位置).
- 使用多种配置捕获sEMG数据,提供丰富的生理信号.
- 初步分析表明与其他可用数据集的相关性很好,这表明数据质量和可靠性很高.
结论:
- MyPredict数据库为肌电模式识别和控制开发提供了一个全面的资源.
- 多样化的活动和传感器模式可以为现实世界的应用程序提供强大的算法训练.
- 这个数据描述器为基于电肌图的人与计算机交互和辅助技术的未来研究奠定了基础.
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