TY  - JOUR
AU  - Isailović, Velibor
AU  - Peulić, Aleksandar
AU  - Đapan, Marko
AU  - Savković, Marija
AU  - Vukićević, Arso M.
PY  - 2022
UR  - http://gery.gef.bg.ac.rs/handle/123456789/1218
AB  - The compliance of industrial personal protective equipment (PPE) still represents a challenging problem considering size of industrial halls and number of employees that operate within them. Since there is a high variability of PPE types/designs that could be used for protecting various body parts and physiological functions, this study was focused on assessing the use of computer vision algorithms to automate the compliance of head-mounted PPE. As a solution, we propose a pipeline that couples the head ROI estimation with the PPE detection. Compared to alternative approaches, it excludes false positive cases while it largely speeds up data collection and labeling. A comprehensive dataset was created by merging public datasets PictorPPE and Roboflow with author’s collected images, containing twelve different types of PPE was used for the development and assessment of three deep learning architectures (Faster R-CNN, MobileNetV2-SSD and YOLOv5)—which in literature were studied only separately. The obtained results indicated that various deep learning architectures reached different performances for the compliance of various PPE types—while the YOLOv5 slightly outperformed considered alternatives (precision 0.920 ± 0.147, and recall 0.611 ± 0.287). It is concluded that further studies on the topic should invest more effort into assessing various deep learning architectures in order to objectively find the optimal ones for the compliance of a particular PPE type. Considering the present technological and data privacy barriers, the proposed solution may be applicable for the PPE compliance at certain checkpoints where employees can confirm their identity.
PB  - Nature Research
T2  - Scientific Reports
T1  - The compliance of head-mounted industrial PPE by using deep learning object detectors
VL  - 12
SP  - 16347
DO  - 10.1038/s41598-022-20282-9
ER  - 

@article{
author = "Isailović, Velibor and Peulić, Aleksandar and Đapan, Marko and Savković, Marija and Vukićević, Arso M.",
year = "2022",
abstract = "The compliance of industrial personal protective equipment (PPE) still represents a challenging problem considering size of industrial halls and number of employees that operate within them. Since there is a high variability of PPE types/designs that could be used for protecting various body parts and physiological functions, this study was focused on assessing the use of computer vision algorithms to automate the compliance of head-mounted PPE. As a solution, we propose a pipeline that couples the head ROI estimation with the PPE detection. Compared to alternative approaches, it excludes false positive cases while it largely speeds up data collection and labeling. A comprehensive dataset was created by merging public datasets PictorPPE and Roboflow with author’s collected images, containing twelve different types of PPE was used for the development and assessment of three deep learning architectures (Faster R-CNN, MobileNetV2-SSD and YOLOv5)—which in literature were studied only separately. The obtained results indicated that various deep learning architectures reached different performances for the compliance of various PPE types—while the YOLOv5 slightly outperformed considered alternatives (precision 0.920 ± 0.147, and recall 0.611 ± 0.287). It is concluded that further studies on the topic should invest more effort into assessing various deep learning architectures in order to objectively find the optimal ones for the compliance of a particular PPE type. Considering the present technological and data privacy barriers, the proposed solution may be applicable for the PPE compliance at certain checkpoints where employees can confirm their identity.",
publisher = "Nature Research",
journal = "Scientific Reports",
title = "The compliance of head-mounted industrial PPE by using deep learning object detectors",
volume = "12",
pages = "16347",
doi = "10.1038/s41598-022-20282-9"
}

Isailović, V., Peulić, A., Đapan, M., Savković, M.,& Vukićević, A. M.. (2022). The compliance of head-mounted industrial PPE by using deep learning object detectors. in Scientific Reports
Nature Research., 12, 16347.
https://doi.org/10.1038/s41598-022-20282-9

Isailović V, Peulić A, Đapan M, Savković M, Vukićević AM. The compliance of head-mounted industrial PPE by using deep learning object detectors. in Scientific Reports. 2022;12:16347.
doi:10.1038/s41598-022-20282-9 .

Isailović, Velibor, Peulić, Aleksandar, Đapan, Marko, Savković, Marija, Vukićević, Arso M., "The compliance of head-mounted industrial PPE by using deep learning object detectors" in Scientific Reports, 12 (2022):16347,
https://doi.org/10.1038/s41598-022-20282-9 . .

TY  - JOUR
AU  - Petrović, Miloš
AU  - Vukićević, Arso M.
AU  - Đapan, Marko
AU  - Peulić, Aleksandar
AU  - Jovičić, Miloš
AU  - Mijailović, Nikola
AU  - Milovanović, Petar
AU  - Grajić, Mirko
AU  - Savković, Marija
AU  - Caiazzo, Carlo
AU  - Isailović, Velibor
AU  - Mačužić, Ivan
AU  - Jovanović, Kosta
PY  - 2022
UR  - http://gery.gef.bg.ac.rs/handle/123456789/1217
AB  - Non-ergonomic execution of repetitive physical tasks represents a major cause of work-related musculoskeletal disorders (WMSD). This study was focused on the pushing and pulling (P&P) of an industrial handcart (which is a generic physical task present across many industries), with the aim to investigate the dependence of P&P execution on the operators’ psychological status and the presence of pain syndromes of the upper limbs and spine. The developed acquisition system integrated two three-axis force sensors (placed on the left and right arm) and six electromyography (EMG) electrodes (placed on the chest, back, and hand flexor muscles). The conducted experiment involved two groups of participants (with and without increased psychological scores and pain syndromes). Ten force parameters (for both left and right side), one EMG parameter (for three different muscles, both left and right side), and two time-domain parameters were extracted from the acquired signals. Data analysis showed intergroup differences in the examined parameters, especially in force integral values and EMG mean absolute values. To the best of our knowledge, this is the first study that evaluated the composite effects of pain syndromes, spine mobility, and psychological status of the participants on the execution of P&P tasks—concluding that they have a significant impact on the P&P task execution and potentially on the risk of WMSD. The future work will be directed towards the development of a personalized risk assessment system by considering more muscle groups, supplementary data derived from operators’ poses (extracted with computer vision algorithms), and cognitive parameters (extracted with EEG sensors).
PB  - MDIP, Basel
T2  - Sensors
T1  - Experimental Analysis of Handcart Pushing and Pulling Safety in an Industrial Environment by Using IoT Force and EMG Sensors: Relationship with Operators’ Psychological Status and Pain Syndromes
VL  - 22
IS  - 19
SP  - 7467
DO  - 10.3390/s22197467
ER  - 

@article{
author = "Petrović, Miloš and Vukićević, Arso M. and Đapan, Marko and Peulić, Aleksandar and Jovičić, Miloš and Mijailović, Nikola and Milovanović, Petar and Grajić, Mirko and Savković, Marija and Caiazzo, Carlo and Isailović, Velibor and Mačužić, Ivan and Jovanović, Kosta",
year = "2022",
abstract = "Non-ergonomic execution of repetitive physical tasks represents a major cause of work-related musculoskeletal disorders (WMSD). This study was focused on the pushing and pulling (P&P) of an industrial handcart (which is a generic physical task present across many industries), with the aim to investigate the dependence of P&P execution on the operators’ psychological status and the presence of pain syndromes of the upper limbs and spine. The developed acquisition system integrated two three-axis force sensors (placed on the left and right arm) and six electromyography (EMG) electrodes (placed on the chest, back, and hand flexor muscles). The conducted experiment involved two groups of participants (with and without increased psychological scores and pain syndromes). Ten force parameters (for both left and right side), one EMG parameter (for three different muscles, both left and right side), and two time-domain parameters were extracted from the acquired signals. Data analysis showed intergroup differences in the examined parameters, especially in force integral values and EMG mean absolute values. To the best of our knowledge, this is the first study that evaluated the composite effects of pain syndromes, spine mobility, and psychological status of the participants on the execution of P&P tasks—concluding that they have a significant impact on the P&P task execution and potentially on the risk of WMSD. The future work will be directed towards the development of a personalized risk assessment system by considering more muscle groups, supplementary data derived from operators’ poses (extracted with computer vision algorithms), and cognitive parameters (extracted with EEG sensors).",
publisher = "MDIP, Basel",
journal = "Sensors",
title = "Experimental Analysis of Handcart Pushing and Pulling Safety in an Industrial Environment by Using IoT Force and EMG Sensors: Relationship with Operators’ Psychological Status and Pain Syndromes",
volume = "22",
number = "19",
pages = "7467",
doi = "10.3390/s22197467"
}

Petrović, M., Vukićević, A. M., Đapan, M., Peulić, A., Jovičić, M., Mijailović, N., Milovanović, P., Grajić, M., Savković, M., Caiazzo, C., Isailović, V., Mačužić, I.,& Jovanović, K.. (2022). Experimental Analysis of Handcart Pushing and Pulling Safety in an Industrial Environment by Using IoT Force and EMG Sensors: Relationship with Operators’ Psychological Status and Pain Syndromes. in Sensors
MDIP, Basel., 22(19), 7467.
https://doi.org/10.3390/s22197467

Petrović M, Vukićević AM, Đapan M, Peulić A, Jovičić M, Mijailović N, Milovanović P, Grajić M, Savković M, Caiazzo C, Isailović V, Mačužić I, Jovanović K. Experimental Analysis of Handcart Pushing and Pulling Safety in an Industrial Environment by Using IoT Force and EMG Sensors: Relationship with Operators’ Psychological Status and Pain Syndromes. in Sensors. 2022;22(19):7467.
doi:10.3390/s22197467 .

Petrović, Miloš, Vukićević, Arso M., Đapan, Marko, Peulić, Aleksandar, Jovičić, Miloš, Mijailović, Nikola, Milovanović, Petar, Grajić, Mirko, Savković, Marija, Caiazzo, Carlo, Isailović, Velibor, Mačužić, Ivan, Jovanović, Kosta, "Experimental Analysis of Handcart Pushing and Pulling Safety in an Industrial Environment by Using IoT Force and EMG Sensors: Relationship with Operators’ Psychological Status and Pain Syndromes" in Sensors, 22, no. 19 (2022):7467,
https://doi.org/10.3390/s22197467 . .

TY  - 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 . .