Motion Capture Technology in Industrial Applications: A Systematic Review.
Motion Capture Technology in Industrial Applications: A Systematic Review.
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DOI：
10.3390/s20195687
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发表时间：
2020-10-05
期刊：
影响因子：
--
通讯作者：
Walsh M
中科院分区：
文献类型：
--
作者：
Menolotto M;Komaris DS;Tedesco S;O'Flynn B;Walsh M
工业4.0的快速技术进步为新型工业过程开辟了新的载体，这些过程需要先进的传感解决方案来实现。运动捕捉（MoCap）传感器，例如视觉相机和惯性测量单元（伊穆斯），经常在工业环境中采用，以支持机器人、增材制造、远程工作和人类安全中的解决方案。本文综述和评估的研究调查使用的MoCap技术在行业相关的研究。在Embase、Scopus、Web of Science和Google Scholar中进行了检索。从2015年起，只考虑了关于初级和次级工业应用的英语研究。使用AXIS工具评价文章的质量。研究根据使用的传感器类型、受益行业部门和应用类型进行分类。最后总结了本研究的特点、主要方法和研究结果。共识别出1682条记录，59条纳入本次审查。分别有21项和38项研究被评估为倾向于中等和低偏倚风险。基于摄像头的传感器和伊穆斯分别用于40%和70%的研究。建筑（30.5%）、机器人（15.3%）和汽车（10.2%）是研究最多的行业，而健康和安全（64.4%）以及工业流程或产品的改进（17%）是最有针对性的应用。惯性传感器是工业MoCap应用的首选。基于摄像头的MoCap系统在机器人应用中表现更好，但工人和机器造成的摄像头障碍是最具挑战性的问题。机器学习算法的进步已被证明可以提高MoCap系统在活动和疲劳检测以及工具状态监控和对象识别等应用中的能力。
The rapid technological advancements of Industry 4.0 have opened up new vectors for novel industrial processes that require advanced sensing solutions for their realization. Motion capture (MoCap) sensors, such as visual cameras and inertial measurement units (IMUs), are frequently adopted in industrial settings to support solutions in robotics, additive manufacturing, teleworking and human safety. This review synthesizes and evaluates studies investigating the use of MoCap technologies in industry-related research. A search was performed in the Embase, Scopus, Web of Science and Google Scholar. Only studies in English, from 2015 onwards, on primary and secondary industrial applications were considered. The quality of the articles was appraised with the AXIS tool. Studies were categorized based on type of used sensors, beneficiary industry sector, and type of application. Study characteristics, key methods and findings were also summarized. In total, 1682 records were identified, and 59 were included in this review. Twenty-one and 38 studies were assessed as being prone to medium and low risks of bias, respectively. Camera-based sensors and IMUs were used in 40% and 70% of the studies, respectively. Construction (30.5%), robotics (15.3%) and automotive (10.2%) were the most researched industry sectors, whilst health and safety (64.4%) and the improvement of industrial processes or products (17%) were the most targeted applications. Inertial sensors were the first choice for industrial MoCap applications. Camera-based MoCap systems performed better in robotic applications, but camera obstructions caused by workers and machinery was the most challenging issue. Advancements in machine learning algorithms have been shown to increase the capabilities of MoCap systems in applications such as activity and fatigue detection as well as tool condition monitoring and object recognition.
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影响因子：
2.4
作者：
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