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Assessment of the handcart pushing and pulling safety by using deep learning 3D pose estimation and IoT force sensors
dc.creator | Vukićević, Arso M. | |
dc.creator | Mačužić, Ivan | |
dc.creator | Mijailović, Nikola | |
dc.creator | Peulić, Aleksandar | |
dc.creator | Radović, Miloš | |
dc.date.accessioned | 2023-03-24T11:54:22Z | |
dc.date.available | 2023-03-24T11:54:22Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 0957-4174 | |
dc.identifier.uri | http://gery.gef.bg.ac.rs/handle/123456789/1222 | |
dc.description.abstract | Pushing and pulling (P&P) are common and repetitive tasks in industry, which non-ergonomic execution is among major causes of musculoskeletal disorders (MSD). The current safety management of P&P assumes restrictions of maximal weight, distance, height – while variable individual parameters (such as the P&P pose ergonomic) remain difficult to account for with the standardized guides. Since manual detection of unsafe P&P acts is subjective and inefficient, the aim of this study was to utilize IoT force sensors and IP cameras to detect unsafe P&P acts timely and objectively. Briefly, after the IoT module detects moments with increased P&P forces, the assessment of pose ergonomics was performed from the employee pose reconstructed with the VIBE algorithm. The experiments showed that turn-points correspond to the high torsion of torso, and that in such moments poses are commonly non ergonomic (although P&P forces are below values defined as critical in previous studies – their momentum cause serious load on the human body). Moreover, the analysis revealed that the loading/unloading of a cargo are also moments of frequent unsafe P&P acts – although they are commonly neglected when studying P&P. The experimental validation of the solution showed good agreement with motion sensors and high potential for monitoring and improving P&P workplace safety. Accordingly, future research will be directed towards: 1) acquisition of P&P data sets for direct recognition and classification of unsafe P&P acts; 2) incorporation of wearable sensors (EMG and EEG) for detecting fatigue and decrease of physical abilities. | sr |
dc.language.iso | en | sr |
dc.publisher | Elsevier | sr |
dc.relation | info:eu-repo/grantAgreement/ScienceFundRS/AI/6524219/RS// | sr |
dc.rights | restrictedAccess | sr |
dc.source | Expert Systems with Applications | sr |
dc.subject | Deep learning | sr |
dc.subject | Ergonomics | sr |
dc.subject | Pushing and pulling | sr |
dc.subject | Handcart | sr |
dc.subject | Unsafe acts | sr |
dc.title | Assessment of the handcart pushing and pulling safety by using deep learning 3D pose estimation and IoT force sensors | sr |
dc.type | article | sr |
dc.rights.license | ARR | sr |
dc.citation.volume | 183 | |
dc.citation.spage | 115371 | |
dc.citation.rank | M21 | |
dc.identifier.wos | 000692076300001 | |
dc.identifier.doi | 10.1016/j.eswa.2021.115371 | |
dc.identifier.scopus | 2-s2.0-85108249525 | |
dc.type.version | publishedVersion | sr |