Smart Vision-Based Analysis and Error Deduction of Human Pose to Reduce Musculoskeletal Disorders in Construction

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aut.relation.journalSmart and Sustainable Built Environment
dc.contributor.authorPurushothaman, Mahesh Babu
dc.contributor.authorGedara, Kasun Moolika
dc.date.accessioned2023-08-22T01:50:47Z
dc.date.available2023-08-22T01:50:47Z
dc.date.issued2023-08-22
dc.description.abstractPurpose This 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/approach Using 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). Findings Results 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/implications Literature 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 implications The 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 implications By 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/value Human 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.
dc.identifier.citationSmart and Sustainable Built Environment, ISSN: 2046-6099 (Print), Emerald. doi: 10.1108/sasbe-02-2023-0037
dc.identifier.doi10.1108/sasbe-02-2023-0037
dc.identifier.issn2046-6099
dc.identifier.urihttp://hdl.handle.net/10292/16588
dc.languageen
dc.publisherEmerald
dc.relation.urihttps://www.emerald.com/insight/content/doi/10.1108/SASBE-02-2023-0037/full/html
dc.rights.accessrightsOpenAccess
dc.rights.urihttp://creativecommons.org/licences/by/4.0/legalcode
dc.subject12 Built Environment and Design
dc.subject33 Built environment and design
dc.titleSmart Vision-Based Analysis and Error Deduction of Human Pose to Reduce Musculoskeletal Disorders in Construction
dc.typeJournal Article
pubs.elements-id521342
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