人类中心-外运输轨迹的手动目录在3D任务空间中多余测量
Tim Sziburis1,2,3, Susanne Blex4,5, Tobias Glasmachers6
1Institute of Computer Science, Ruhr West University of Applied Sciences, 45479, Mülheim an der Ruhr, Germany. tim.sziburis@alumni.cern.
Scientific data
|July 24, 2025
概括
这项研究提出了人类手部运动轨迹的新数据集,用于分析运动病理. 使用标准化方法和光学和IMU传感器收集的数据可以帮助开发便携式诊断工具.
科学领域:
- 生物力学 生物力学
- 人类运动控制人体运动控制
- 医疗技术 医疗技术 医学技术
背景情况:
- 识别运动病理需要广泛的数据来进行运动建模和可变性分析.
- 现有的方法往往缺乏标准化和可转移性.
- 需要可复制和便携式系统来进行运动分析.
研究的目的:
- 介绍一个系统的数据集的3D人手运输运动轨迹.
- 建立一个标准化和可重复的数据收集环境.
- 促进便携式,低成本的运动分析工具的开发.
主要方法:
- 收集了手运输运动的3D中心外任务空间轨迹.
- 使用了一个标准化的设置,具有统一的起始位置和九个目标位置.
- 每个参与者每个目标地点自动记录10个试验的数据.
- 采用光学跟踪系统和单个惯性测量单元 (IMU) 传感器用于冗余数据捕获.
- 使用爱丁堡手性库存 (EHI) 确定手性.
主要成果:
- 从31名没有已知的运动障碍的参与者 (年龄21-78岁) 产生了5580个运动轨迹的数据集.
- 数据集包括每只手的90个运动轨迹.
- 证明了标准化数据收集设置的可复制性和可转移性.
- 突出了IMU传感器作为一个便携式,低成本的替代固定系统.
结论:
- 本文所介绍的数据集为动作建模和人类手部动作的可变性分析提供了坚实的基础.
- 标准化的方法确保了数据的可靠性,并促进了数据的可转移性.
- 集成IMU传感器为开发可访问和高效的医疗评估和运动障碍识别工具提供了一个有希望的途径.
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