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在视觉传感器系统中高效地识别和评估人类姿势:一项实验研究

Lei Lei1, Haonan Zhang2, Qi Zhang3

  • 1School of Information Engineering, Xi'an University, Xi'an 710065, China.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
概括

这项研究引入了使用视觉传感器进行人体姿势识别和评估的新架构. 该系统达到96%以上的准确性,为各种练习提供实时性能和可扩展性.

关键词:
深度学习是一种深度学习.人类姿势识别技术姿势评估系统 姿势评估系统视觉传感器系统 视觉传感器系统

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科学领域:

  • 生物力学 生物力学
  • 计算机视觉 计算机视觉
  • 人与计算机的交互

背景情况:

  • 传统的手动姿势评估方法受到疲劳,经验变化和不一致的标准的影响.
  • 视觉传感器系统提供了一个替代方案,但面临着大规模实施的挑战.
  • 需要强大且可扩展的系统来准确识别和评估人体姿势.

研究的目的:

  • 提出和验证人类姿势识别和评估的新型架构.
  • 在大规模应用中克服视觉传感器系统的实施挑战.
  • 开发一个提供高精度,实时处理和可扩展性的系统.

主要方法:

  • 设计了四个子系统架构:视觉传感器子系统 (VSS),姿势评估子系统 (PAS),控制显示子系统和存储管理子系统.
  • 该架构通过子系统合作支持并行数据处理.
  • 建造了一个实验测试台来实施和验证拟议的架构.

主要成果:

  • 拟议的架构通过实验验证证明了高可行性和合理性.
  • 使用拉力和推力练习的评估结果的整体准确率超过96%.
  • 该系统在不同评估场景中展示了卓越的实时性能和可扩展性.

结论:

  • 开发的架构有效地解决了传统姿势评估的局限性.
  • 该系统为人类姿势识别和评估提供了可靠,准确和可扩展的解决方案.
  • 这些发现支持这种架构在各种炼和康复环境中的实际应用.