在医院病房的多LiDAR人类关节识别算法基于改进的V2V-Posenet
Yezhao Ju1,2, Haiyang Zhang1,2, Xuan Wu1,2
1School of Optics and Photonics, Beijing Institute of Technology, Beijing, China.
Heliyon
|July 19, 2024
概括
这项研究引入了一种改进的V2V-Posenet模型,使用多LiDAR系统在医院病房中准确检测人类关节. 改进后的模型通过达到91.6%的平均精度,显著改善了患者监测和跌倒预防.
科学领域:
- 医学技术 医学技术 医学技术
- 计算机视觉 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
背景情况:
- 医院的患者安全需要持续监测,特别是为了防止员工缺席时的跌倒和.
- 现有的人类联合识别方法面临着诸如遮蔽和复杂背景等挑战,限制了它们的现实应用.
研究的目的:
- 开发一个强大的人类关节检测系统用于医院病房,使用多LiDAR方法.
- 提高患者监测系统的准确性和效率,以提高安全性.
主要方法:
- 一个多LiDAR系统被用来捕捉多视图的人体点云.
- 改进的V2V-Posenet模型被用于精确的人类关节位置检测.
- 实施了包括点云拼接,声化,数据增强,损失函数约束和3D高斯过在内的技术.
主要成果:
- 改进的V2V-Posenet模型实现了91.6%的平均精度,比原始模型的80.1%显著增加.
- 性能超过了A2J-Posenet (77.4% mAP) 的比较算法.
- 该模型展示了实时目标检测能力,满足实际要求.
结论:
- 拟议的多LiDAR系统和改进的V2V-Posenet模型为医院环境中的人类关节检测提供了一个高度有效的解决方案.
- 这项技术可以通过可靠的监测和及时干预来提高患者的安全性.
- 该系统的准确性和速度使其适合在医疗保健机构的实际部署.
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