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基于深度学习的头部姿势估计从RGB图像序列的医学应用.

Kittisak Chotikkakamthorn1, Wen-Nung Lie2, Panrasee Ritthipravat3

  • 1Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, 999 Phutthamonthon 4 Road, Salaya, Nakhon Pathom, 73170, Thailand; Department of Electrical Engineering, College of Engineering, National Chung Cheng University, No. 168, Section 1, University Rd, Minxiong Township, Chia-Yi, 621301, Taiwan.

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概括
此摘要是机器生成的。

这项研究引入了一个深度学习模型,用于在远程医疗中准确测量头部运动,以改善部运动范围 (CROM) 对移动性问题患者的评估.

关键词:
宫运动范围 (CROM)深度学习是一种深度学习.估计头部姿势的估计.医疗应用 医学应用远程医疗远程医疗

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科学领域:

  • 计算机视觉 计算机视觉
  • 医疗技术 医疗技术 医学技术
  • 人工智能的人工智能

背景情况:

  • 远程医疗解决了医疗保健获取方面的挑战,包括远程咨询和患者移动限制.
  • 测量头部运动对于日常活动至关重要,但通常因衰老,受伤或疾病而受损.
  • 目前存在的基于视觉的宫运动范围 (CROM) 方法缺乏准确性,需要专门的设备.

研究的目的:

  • 为远程医疗应用开发和评估一种新型的深度神经网络,用于精确的头部姿势估计 (HPE).
  • 在临床环境中应用开发的HPE技术进行准确的CROM测量.
  • 为远程CROM评估提供一个计算效率高和成本效益高的解决方案.

主要方法:

  • 一个包含多层次金字塔特征提取和金字塔特征聚合结构 (PFAS) 的深度神经网络.
  • 一个修改过的Atrous空间金字塔聚合 (ASPP) 模块,用于增强功能表示.
  • 一个多箱分类和回归模块,用于导出头姿势参数的欧勒角.

主要成果:

  • 该模型在公开的HPE数据集上实现了可比性能 (平均MAE:2.16°-3.50°).
  • 在一个私人医疗数据集上,该方法在CROM测量中产生了最低的平均绝对误差 (MAE) 3.73°.
  • 该模型展示了每张图像2.27毫秒的快速推断速度.

结论:

  • 拟议的深度学习方法为头部姿势估计提供了准确有效的方法.
  • 这种技术适用于远程医疗中的CROM测量,克服了当前方法的局限性.
  • 该解决方案为远程医疗保健应用提供了准确性,方便性和低运营成本.