Abstract：
Head pose assessment can reveal important clinical information on human motor control. Quantitative assessment have the potential to objectively evaluate head pose and movements\' specifics, in order to monitor the progression of a disease or the effectiveness of a treatment. Optoelectronic camera-based motion-capture systems, recognized as a gold standard in clinical biomechanics, have been proposed for head pose estimation. However, these systems require markers to be positioned on the person\'s face which is impractical for everyday clinical practice. Furthermore, the limited access to this type of equipment and the emerging trend to assess mobility in natural environments support the development of algorithms capable of estimating head orientation using off-the-shelf sensors, such as RGB cameras. Although artificial vision is a popular field of research, limited validation of human pose estimation based on image recognition suitable for clinical applications has been performed. This paper first provides a brief review of available head pose estimation algorithms in the literature. Current state-of-the-art head pose algorithms designed to capture the facial geometry from videos, OpenFace 2.0, MediaPipe and 3DDFA_V2, are then further evaluated and compared. Accuracy is assessed by comparing both approaches to a baseline, measured with an optoelectronic camera-based motion-capture system. Results reveal a mean error lower or equal to 5.6∘ for 3DDFA_V2 depending on the plane of movement, while the mean error reaches 14.1∘ and 11.0∘ for OpenFace 2.0 and MediaPipe, respectively. This demonstrates the superiority of the 3DDFA_V2 algorithm in estimating head pose, in different directions of motion, and suggests that this algorithm can be used in clinical scenarios.
摘要：
头部姿势评估可以揭示有关人体运动控制的重要临床信息。定量评估有可能客观地评估头部姿势和动作的细节，以监测疾病的进展或治疗的有效性。基于光电摄像机的运动捕捉系统，被公认为临床生物力学的黄金标准，已经被提出用于头部姿势估计。然而,这些系统需要将标记物放置在人的脸上，这对于日常临床实践是不切实际的。此外，对这类设备的有限访问以及在自然环境中评估移动性的新兴趋势支持了能够使用现成传感器估计头部方向的算法的开发，例如RGB相机。虽然人工视觉是一个热门的研究领域，基于适用于临床应用的图像识别的人体姿态估计的有限验证。本文首先简要介绍了文献中可用的头部姿态估计算法。当前最先进的头部姿势算法，旨在从视频中捕获面部几何形状，然后进一步评估和比较OpenFace2.0、MediaPipe和3DDFA_V2。通过将两种方法与基线进行比较来评估准确性，用基于光电相机的运动捕捉系统测量。结果表明，根据运动平面的不同，3DDFA_V2的平均误差小于或等于5.6。而OpenFace2.0和MediaPipe的平均误差达到14.1和11.0。分别。这证明了3DDFA_V2算法在估计头部姿势方面的优越性，在不同的运动方向，并表明该算法可用于临床场景。
