通过条件循环一致的生成对抗网络,识别跨跑步,行走和手写模式的潜在个性
Johannes Burdack1, Sven Giesselbach2,3, Marvin L Simak1
1Department of Training and Movement Science, Institute of Sport Science, Johannes Gutenberg-University, Mainz, Germany.
Frontiers in bioengineering and biotechnology
|August 21, 2023
概括
这项研究表明,个人运动模式,如走路,跑步和手写,在不同活动中都有共同点. 使用先进的AI (CycleGAN),研究人员成功识别了这些交叉运动模式,从而实现了更好的分析和数据生成.
科学领域:
- 生物力学 生物力学
- 人类运动分析分析
- 机器学习 机器学习
背景情况:
- 人类运动模式中强烈的个性化现已被接受.
- 之前的研究主要比较来自相同运动类型的信号.
- 在分析不同运动模式的共同点方面存在方法上的差距.
研究的目的:
- 在个人步行,跑步和手写模式中检测交叉运动的共同点.
- 为了利用数据增强技术进行交叉运动分析.
- 在运动模式研究中探索生成对抗网络的潜力.
主要方法:
- 采用条件循环一致的生成对抗网络 (CycleGAN) 来对对移动数据转换.
- 从原始数据集中生成人工运动数据 (行走,跑步,手写).
- 使用支持矢量机 (SVM) 进行分类,以测试个人从生成数据的识别能力.
主要成果:
- 成功学习了垂直地面反应力 (行走/跑步) 和垂直笔压力 (手写) 之间的双向转换.
- 获得高分类F1分数,从46.8% (从走路生成手写) 到98.9% (从跑步生成走路).
- 证明了交叉运动的个体模式的识别,证实了共同的特征.
结论:
- 个体运动模式在不同的活动中表现出共同点,例如走路,跑步和手写.
- 提出的方法使交叉运动分析和人工数据生成成为可能.
- 这种方法有可能推进人类运动分析,并为研究创建更大的数据集.
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