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Petrovic M, Vukicevic AM, Djapan M, Peulic A, Jovicic M, Mijailovic N, Milovanovic P, Grajic M, Savkovic M, Caiazzo C, Isailovic V, Macuzic I, Jovanovic 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. SENSORS (BASEL, SWITZERLAND) 2022; 22:7467. [PMID: 36236564 PMCID: PMC9572849 DOI: 10.3390/s22197467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 09/27/2022] [Accepted: 09/28/2022] [Indexed: 06/16/2023]
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).
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Affiliation(s)
- Milos Petrovic
- School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia
| | - Arso M. Vukicevic
- Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac, Serbia
| | - Marko Djapan
- Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac, Serbia
| | - Aleksandar Peulic
- Faculty of Geography, University of Belgrade, Studentski trg 3, 11000 Belgrade, Serbia
| | - Milos Jovicic
- The AI Institute of Serbia, Fruškogorska 1, 21000 Novi Sad, Serbia
| | - Nikola Mijailovic
- Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac, Serbia
| | - Petar Milovanovic
- Center of Bone Biology, Faculty of Medicine, University of Belgrade, Dr Subotica 4/2, 11000 Belgrade, Serbia
- Laboratory of Bone Biology and Bioanthropology, Faculty of Medicine, Institute of Anatomy, University of Belgrade, Dr Subotica 4/2, 11000 Belgrade, Serbia
| | - Mirko Grajic
- Center for Physical Medicine and Rehabilitation, Faculty of Medicine, University Clinical Center of Serbia, University of Belgrade, Pasterova 2, 11000 Belgrade, Serbia
| | - Marija Savkovic
- Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac, Serbia
| | - Carlo Caiazzo
- Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac, Serbia
| | - Velibor Isailovic
- Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac, Serbia
| | - Ivan Macuzic
- Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac, Serbia
| | - Kosta Jovanovic
- School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia
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The compliance of head-mounted industrial PPE by using deep learning object detectors. Sci Rep 2022; 12:16347. [PMID: 36175434 PMCID: PMC9523037 DOI: 10.1038/s41598-022-20282-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 09/12/2022] [Indexed: 11/08/2022] Open
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.
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