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基于雷达的多人姿势识别,使用深度学习,对距离和角度不敏感.

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科学领域:

  • 使用雷达信号处理和深度学习来实现人机交互.
  • 专注于医疗保健技术中的计算机视觉和模式识别.

背景情况:

  • 人体姿势识别对于预防性医疗保健和监测至关重要.
  • 现有的基于雷达的方法面临着与静态的人体和多个个体的挑战.

研究的目的:

  • 开发一种新的框架,使用FMCW雷达点云来识别靠近两个人的姿势.
  • 为了使个人监控的隐私保护医疗保健传感,如老年夫妇.

主要方法:

  • 从FMCW雷达中提取范围,速度和角度信息,以创建笛卡尔点云.
  • 使用无监督的集群来分离个人人类点云.
  • 应用深度学习模型 (DenseNet) 来对个体受试者进行姿势分类.

主要成果:

  • 在对两个不重叠的人类 (站立,坐在椅子上,坐在地板上,躺下) 进行十种姿势组合的分类中,平均准确率为96%.
  • 介绍了一种使用中心点信息来检测和分类重叠的人类参与者的方法.
  • 在五种重叠的人体姿势组合中,获得了超过96%的准确性.

结论:

  • 拟议的FMCW雷达框架有效地识别了多个个体的人体姿势,包括重叠的场景.
  • 展示了作为隐私保护的远程医疗监控传感平台的潜力.
  • 在侦探和预防性医疗保健行业推进雷达技术的应用.