Smart vision-based analysis and error deduction of human pose to reduce musculoskeletal disorders in construction

IF 3.5 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY
M. Purushothaman, Kasun Moolika Gedara
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引用次数: 0

Abstract

PurposeThis pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.Design/methodology/approachUsing a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).FindingsResults show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.Research limitations/implicationsLiterature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.Practical implicationsThe theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.Social implicationsBy using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.Originality/valueHuman pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.
基于智能视觉的人体姿态分析和误差推断,减少施工中的肌肉骨骼疾病
目的本实用研究论文旨在揭示基于智能视觉的方法(SVBM),这是一种将计算机视觉(使用移动和嵌入式摄像头录制和直播视频)关联起来的人工智能程序,有助于建筑行业手动提升人体姿势的推导、分析和训练。设计/方法论/方法本研究采用务实的方法,结合文献综述,讨论SVBM。研究方法包括文献综述,然后是务实的方法和对所获得数据的实验室验证。采用实用的方法,本文作者开发了一个SVBM,这是一个将计算机视觉(使用移动和嵌入式相机录制和直播视频)关联起来的人工智能程序。结果表明,SVBM在没有附加到人体的情况下观察相关事件,并将其与标准轴进行比较,以使用移动和其他相机识别异常姿势。利用一种新颖的软件程序和移动应用程序,通过人体姿态检测和计算身体部位运动角度来投影关键节点的角度。SVBM展示了其使用先前录制的视频实时和离线进行数据捕获和分析的能力,并验证了程序编码和结果可重复性。研究局限性/含义文献综述方法的局限性包括与最新的领域知识不同步。通过选择过去二十年内的文献综述范围来抵消这一限制。这篇文献综述可能没有捕捉到所有发表的文章,因为数据库访问和搜索的限制仅基于英语。此外,作者可能遗漏了隐藏在不太受欢迎的期刊上的富有成效的文章。这些限制是公认的。关键的限制是SVBM中没有解决信任、隐私和心理问题,这是公认的。然而,SVBM的好处自然抵消了实际采用的这一限制。实际含义理论和实际含义包括定制和个性化的预测,以及在严重伤害发生之前预防大多数与姿势相关的危险行为。理论含义包括模拟人体姿势和基于实验室的分析,而无需连接自然改变工作姿势的传感器。SVBM将帮助研究人员开发更准确的数据和接近实际的理论模型。社会意义通过使用SVBM,早期推断和预防肌肉骨骼疾病的可能性很高;社会影响包括成为一个更健康的社会和关注健康的建筑行业的好处。独创性/价值人体姿势检测,尤其是工作环境中的关节角度计算,对于肌肉骨骼疾病的早期推断至关重要。传统的基于数字技术的姿势缺陷检测方法侧重于来自可穿戴设备和实验室控制的运动传感器的位置信息。本文首次提出了一种新颖的计算机视觉(使用移动和嵌入式相机录制和直播视频)和数字图像相关的深度学习方法,无需连接人体,用于在实际施工工作环境中手动处理姿势推导和角度、领口和躯干线的分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Smart and Sustainable Built Environment
Smart and Sustainable Built Environment GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY-
CiteScore
9.20
自引率
8.30%
发文量
53
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