Experimental Study of a Deep-Learning RGB-D Tracker for Virtual Remote Human Model Reconstruction.

IF 3.1 Q2 HEALTH CARE SCIENCES & SERVICES
International Journal of Telemedicine and Applications Pub Date : 2021-09-15 eCollection Date: 2021-01-01 DOI:10.1155/2021/5551753
Shahram Payandeh, Jeffrey Wael
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引用次数: 0

Abstract

Tracking movements of the body in a natural living environment of a person is a challenging undertaking. Such tracking information can be used as a part of detecting any onsets of anomalies in movement patterns or as a part of a remote monitoring environment. The tracking information can be mapped and visualized using a virtual avatar model of the tracked person. This paper presents an initial novel experimental study of using a commercially available deep-learning body tracking system based on an RGB-D sensor for virtual human model reconstruction. We carried out our study in an indoor environment under natural conditions. To study the performance of the tracker, we experimentally study the output of the tracker which is in the form of a skeleton (stick-figure) data structure under several conditions in order to observe its robustness and identify its drawbacks. In addition, we show and study how the generic model can be mapped for virtual human model reconstruction. It was found that the deep-learning tracking approach using an RGB-D sensor is susceptible to various environmental factors which result in the absence and presence of noise in estimating the resulting locations of skeleton joints. This as a result introduces challenges for further virtual model reconstruction. We present an initial approach for compensating for such noise resulting in a better temporal variation of the joint coordinates in the captured skeleton data. We explored how the extracted joint position information of the skeleton data can be used as a part of the virtual human model reconstruction.

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用于虚拟远程人体模型重建的深度学习 RGB-D 追踪器的实验研究
跟踪人在自然生活环境中的身体运动是一项具有挑战性的工作。这些跟踪信息可用于检测运动模式中的任何异常情况,或作为远程监控环境的一部分。跟踪信息可以通过被跟踪者的虚拟化身模型进行映射和可视化。本文介绍了使用基于 RGB-D 传感器的市售深度学习人体跟踪系统进行虚拟人体模型重建的初步新型实验研究。我们在室内环境的自然条件下进行了研究。为了研究跟踪器的性能,我们在多种条件下对以骨架(棒状图)数据结构形式输出的跟踪器进行了实验研究,以观察其鲁棒性并找出其缺点。此外,我们还展示并研究了如何将通用模型映射到虚拟人体模型重建中。研究发现,使用 RGB-D 传感器的深度学习跟踪方法容易受到各种环境因素的影响,导致在估计骨骼关节位置时出现或不出现噪声。因此,这给进一步的虚拟模型重建带来了挑战。我们提出了一种初步方法来补偿这些噪声,从而使捕获的骨骼数据中的关节坐标具有更好的时间变化。我们探讨了如何将提取的骨骼数据关节位置信息用作虚拟人体模型重建的一部分。
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来源期刊
CiteScore
6.90
自引率
2.30%
发文量
19
审稿时长
12 weeks
期刊介绍: The overall aim of the International Journal of Telemedicine and Applications is to bring together science and applications of medical practice and medical care at a distance as well as their supporting technologies such as, computing, communications, and networking technologies with emphasis on telemedicine techniques and telemedicine applications. It is directed at practicing engineers, academic researchers, as well as doctors, nurses, etc. Telemedicine is an information technology that enables doctors to perform medical consultations, diagnoses, and treatments, as well as medical education, away from patients. For example, doctors can remotely examine patients via remote viewing monitors and sound devices, and/or sampling physiological data using telecommunication. Telemedicine technology is applied to areas of emergency healthcare, videoconsulting, telecardiology, telepathology, teledermatology, teleophthalmology, teleoncology, telepsychiatry, teledentistry, etc. International Journal of Telemedicine and Applications will highlight the continued growth and new challenges in telemedicine, applications, and their supporting technologies, for both application development and basic research. Papers should emphasize original results or case studies relating to the theory and/or applications of telemedicine. Tutorial papers, especially those emphasizing multidisciplinary views of telemedicine, are also welcome. International Journal of Telemedicine and Applications employs a paperless, electronic submission and evaluation system to promote a rapid turnaround in the peer-review process.
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