Moreira Rayele, Teixeira Silmar, Fialho Renan, Miranda Aline, Lima Lucas Daniel Batista, Carvalho Maria Beatriz, Alves Ana Beatriz, Bastos Victor Hugo Vale, Teles Ariel Soares
Postgraduate Program in Biotechnology, Parnaíba Delta Federal University, Parnaíba 64202-020, Brazil.
University Center Inta, Sobral 62050-100, Brazil.
Sensors (Basel). 2024 Dec 14;24(24):7983. doi: 10.3390/s24247983.
Human Pose Estimation (HPE) is a computer vision application that utilizes deep learning techniques to precisely locate Key Joint Points (KJPs), enabling the accurate description of a person's pose. HPE models can be extended to facilitate Range of Motion (ROM) assessment by leveraging patient photographs. This study aims to evaluate and compare the performance of HPE models for assessing upper limbs ROM. A physiotherapist evaluated the degrees of ROM in shoulders (flexion, extension, and abduction) and elbows (flexion and extension) for fifty-two participants using both Universal Goniometer (UG) and five HPE models. Participants were instructed to repeat each movement three times to obtain measurements with the UG, then positioned while photos were captured using the mobile application. The paired -test, bias, and error measures were employed to evaluate the difference and agreement between measurement methods. Results indicated that the INT16 model exhibited superior performance. Root Mean Square Errors obtained through this model were <10° in 8 of 10 analyzed movements. HPE models demonstrated better performance in shoulder flexion and abduction movements while exhibiting unsatisfactory performance in elbow flexion. Challenges such as image perspective distortion, environmental lighting conditions, images in monocular view, and complications in the pose may influence the models' performance. Nevertheless, HPE models show promise in identifying KJPs and facilitating ROM measurements, potentially enhancing convenience and efficiency in assessments. However, their current accuracy for this application is unsatisfactory, highlighting the need for caution when considering automated upper limb ROM measurement with them. The implementation of these models in clinical practice does not diminish the crucial role of examiners in carefully inspecting images and making adjustments to ensure measurement reliability.
人体姿态估计（HPE）是一种计算机视觉应用，它利用深度学习技术精确定位关键关节点（KJPs），从而能够准确描述人的姿态。通过利用患者照片，HPE模型可以扩展以促进运动范围（ROM）评估。本研究旨在评估和比较HPE模型在评估上肢ROM方面的性能。一名物理治疗师使用通用测角仪（UG）和五种HPE模型，对52名参与者的肩部（前屈、后伸和外展）和肘部（屈曲和伸展）的ROM度数进行了评估。参与者被要求每个动作重复三次，以便用UG获得测量值，然后在使用移动应用程序拍摄照片时摆好姿势。采用配对检验、偏差和误差测量来评估测量方法之间的差异和一致性。结果表明，INT16模型表现出卓越的性能。通过该模型获得的均方根误差在10项分析动作中的8项中小于10°。HPE模型在肩部前屈和外展动作中表现较好，而在肘部屈曲动作中表现不尽人意。图像透视失真、环境光照条件、单目视图中的图像以及姿态中的复杂性等挑战可能会影响模型的性能。尽管如此，HPE模型在识别关键关节点和促进ROM测量方面显示出前景，有可能提高评估的便利性和效率。然而，它们目前在此应用中的准确性并不令人满意，这凸显了在考虑使用它们进行自动化上肢ROM测量时需要谨慎。这些模型在临床实践中的应用并不会削弱检查人员仔细检查图像并进行调整以确保测量可靠性的关键作用。
