Assessment of the handcart pushing and pulling safety by using deep learning 3D pose estimation and IoT force sensors
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 cau...se 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.
Keywords:
Deep learning / Ergonomics / Pushing and pulling / Handcart / Unsafe actsSource:
Expert Systems with Applications, 2021, 183, 115371-Publisher:
- Elsevier
Funding / projects:
- AI4WorkplaceSafety - Artificial Intelligence for Managing Workplace Safety (RS-ScienceFundRS-AI-6524219)
DOI: 10.1016/j.eswa.2021.115371
ISSN: 0957-4174
WoS: 000692076300001
Scopus: 2-s2.0-85108249525
Collections
Institution/Community
Geografski fakultetTY - JOUR AU - Vukićević, Arso M. AU - Mačužić, Ivan AU - Mijailović, Nikola AU - Peulić, Aleksandar AU - Radović, Miloš PY - 2021 UR - http://gery.gef.bg.ac.rs/handle/123456789/1222 AB - 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. PB - Elsevier T2 - Expert Systems with Applications T1 - Assessment of the handcart pushing and pulling safety by using deep learning 3D pose estimation and IoT force sensors VL - 183 SP - 115371 DO - 10.1016/j.eswa.2021.115371 ER -
@article{ author = "Vukićević, Arso M. and Mačužić, Ivan and Mijailović, Nikola and Peulić, Aleksandar and Radović, Miloš", year = "2021", 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.", publisher = "Elsevier", journal = "Expert Systems with Applications", title = "Assessment of the handcart pushing and pulling safety by using deep learning 3D pose estimation and IoT force sensors", volume = "183", pages = "115371", doi = "10.1016/j.eswa.2021.115371" }
Vukićević, A. M., Mačužić, I., Mijailović, N., Peulić, A.,& Radović, M.. (2021). Assessment of the handcart pushing and pulling safety by using deep learning 3D pose estimation and IoT force sensors. in Expert Systems with Applications Elsevier., 183, 115371. https://doi.org/10.1016/j.eswa.2021.115371
Vukićević AM, Mačužić I, Mijailović N, Peulić A, Radović M. Assessment of the handcart pushing and pulling safety by using deep learning 3D pose estimation and IoT force sensors. in Expert Systems with Applications. 2021;183:115371. doi:10.1016/j.eswa.2021.115371 .
Vukićević, Arso M., Mačužić, Ivan, Mijailović, Nikola, Peulić, Aleksandar, Radović, Miloš, "Assessment of the handcart pushing and pulling safety by using deep learning 3D pose estimation and IoT force sensors" in Expert Systems with Applications, 183 (2021):115371, https://doi.org/10.1016/j.eswa.2021.115371 . .