基内卡尔:用于布风险评估和余额损失分析的数据集
Sean Maudsley-Barton1, Moi Hoon Yap2
1Department of Computing and Mathematics, Manchester Metropolitan University, Faculty of Science and Engineering, Manchester, M1 5GD, UK. s.maudsley-barton@mmu.ac.uk.
Scientific data
|September 18, 2023
概括
这项研究介绍了KINECAL,这是使用Kinect识别人类行动的新数据集. 它提供了临床相关的运动数据和元数据,以推进Kinect.
科学领域:
- 生物医学工程 生物医学工程
- 人与计算机的交互
- 临床生物力学 临床生物力学
背景情况:
- 由于动作捕捉数据集,特别是使用Kinect的数据集,人类动作识别已经取得了进步.
- 由于缺乏具有相关临床运动和元数据的专用数据集,Kinect的临床应用受到限制.
研究的目的:
- 介绍KINECAL,这是一个新的数据集,旨在弥合运动捕捉技术和临床实践之间的差距.
- 为研究人员提供临床相关数据,用于在医疗保健环境中开发和验证人类行为识别算法.
主要方法:
- KINECAL数据集包括90个个体执行11个不同的运动的记录.
- 包含的运动通常用于临床平衡评估.
- 数据集得到了元数据的丰富,包括临床标签,跌倒史和姿势摇摆指标.
主要成果:
- KINECAL提供了全面的动作捕捉数据集合,用于平衡评估.
- 综合元数据为临床分析和算法开发提供了有价值的背景.
- 该数据集有助于研究运动分析的临床实用性.
结论:
- KINECAL解决了对临床相关的运动捕捉数据集的需求.
- 预计这项资源将加速临床运动分析研究和Kinect技术在医疗保健中的应用.
- 该数据集支持开发用于临床使用的先进的人类行为识别系统.
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