Accurate 3D tracking of hand and fingers movements poses significant
challenges in computer vision. The potential applications span across multiple
domains, including human-computer interaction, virtual reality, industry, and
medicine. While gesture recognition has achieved remarkable accuracy,
quantifying fine movements remains a hurdle, particularly in clinical
applications where the assessment of hand dysfunctions and rehabilitation
training outcomes necessitate precise measurements. Several novel and
lightweight frameworks based on Deep Learning have emerged to address this
issue; however, their performance in accurately and reliably measuring fingers
movements requires validation against well-established gold standard systems.
In this paper, the aim is to validate the handtracking framework implemented by
Google MediaPipe Hand (GMH) and an innovative enhanced version, GMH-D, that
exploits the depth estimation of an RGB-Depth camera to achieve more accurate
tracking of 3D movements. Three dynamic exercises commonly administered by
clinicians to assess hand dysfunctions, namely Hand Opening-Closing, Single
Finger Tapping and Multiple Finger Tapping are considered. Results demonstrate
high temporal and spectral consistency of both frameworks with the gold
standard. However, the enhanced GMH-D framework exhibits superior accuracy in
spatial measurements compared to the baseline GMH, for both slow and fast
movements. Overall, our study contributes to the advancement of hand tracking
technology, the establishment of a validation procedure as a good-practice to
prove efficacy of deep-learning-based hand-tracking, and proves the
effectiveness of GMH-D as a reliable framework for assessing 3D hand movements
in clinical applications.

验证 Google MediaPipe Hand (GMH) 及其改进版本 GMH-D 对 3D 手部运动的准确跟踪的有效性，并证明 GMH-D 在临床应用中评估 3D 手部运动方面的优越性。

临床应用中的手部追踪：Google MediaPipe Hand (GMH) 和深度增强 GMH-D 框架的验证

Hand tracking for clinical applications: validation of the Google  MediaPipe Hand (GMH) and the depth-enhanced GMH-D frameworks

Egocentric vision is an emerging field of computer vision that is
characterized by the acquisition of images and video from the first person
perspective. In this paper we address the challenge of egocentric human action
recognition by utilizing the presence and position of detected regions of
interest in the scene explicitly, without further use of visual features.
Initially, we recognize that human hands are essential in the execution of
actions and focus on obtaining their movements as the principal cues that
define actions. We employ object detection and region tracking techniques to
locate hands and capture their movements. Prior knowledge about egocentric
views facilitates hand identification between left and right. With regard to
detection and tracking, we contribute a pipeline that successfully operates on
unseen egocentric videos to find the camera wearer's hands and associate them
through time. Moreover, we emphasize on the value of scene information for
action recognition. We acknowledge that the presence of objects is significant
for the execution of actions by humans and in general for the description of a
scene. To acquire this information, we utilize object detection for specific
classes that are relevant to the actions we want to recognize.
Our experiments are targeted on videos of kitchen activities from the
Epic-Kitchens dataset. We model action recognition as a sequence learning
problem of the detected spatial positions in the frames. Our results show that
explicit hand and object detections with no other visual information can be
relied upon to classify hand-related human actions. Testing against methods
fully dependent on visual features, signals that for actions where hand motions
are conceptually important, a region-of-interest-based description of a video
contains equally expressive information with comparable classification
performance.

该论文研究了从第一人称视角获取图像和视频的发展趋势，采用图像识别和区域跟踪技术进行视觉场景中手的识别和动作的分类，表明使用区域兴趣描述视频的信息可以被依靠，来对与手有关的人体动作进行分类。