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Raul Beltrán Beltrán¹, Julia Richter¹, Guido Köstermeyer²

¹Professorship Circuit and System Design, Chemnitz University of Technology, Reichenhainer Straße 70, 09126 Chemnitz, Germany.

Sensors (Basel, Switzerland)

|October 14, 2023

概括

本研究引入了一种基于摄像头的新系统,用于分析登运动,使用3D运动分析自动检测常见的初学者错误. 该系统提供即时反,帮助登山者改进技术并减少错误.

科学领域:

运动科学和生物力学
计算机视觉和人机交互的人机交互

背景情况:

越来越多的人对登的兴趣需要非侵入性的运动分析技术.
现有的方法通常依赖于侵入性技术或专注于精英运动员.
新手登山者经常表现出特定的,可识别的运动错误.

研究的目的:

开发和评估一个基于摄像头的系统,用于自动检测初学者登山错误.
为登山者提供实时,可操作的反,以改进技术.
使用3D运动分析来提高错误识别的准确性.

主要方法:

使用带有LiDAR的iPad Pro捕获3D爬运动数据.
采用了果的Vision框架来进行二维骨提取,以及LiDAR用于深度信息.
开发了一个有限状态机器来识别登阶段并检测特定阶段的错误.
实施了一个应用程序,以提供即时的虚拟导师反.

主要成果:

该系统成功地识别了六个常见的初学者登错误,准确度足以提供反.
精度回忆曲线表明了可接受的虚假阳性范围.
确定了限制,主要与影响关节定位的LiDAR传感器范围有关.

结论:

开发的系统为分析初学者登技术提供了一个有希望的非侵入性解决方案.
虚拟导师应用程序有效地提供了立即的,建设性的反.
预计未来的姿势估计和传感器技术的进步将进一步提高系统性能.

关键词:

登运动分析人类姿势估计估计关键点检测关键点检测运动和计算机科学视频分析视频分析

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