Assessment of the Human-Robot Collaborative Polishing Task by Using EMG Sensors and 3D Pose Estimation

Petrović, Miloš; Vukićević, Arso M.; Lukić, Branko; Jovanović, Kosta

doi:10.1007/978-3-031-04870-8_66

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…Additionally, innovative methods such as the use of brain–computer interface (BCI), EEG-based attention test with CPT and TOVA, and eye-tracking systems could be helpful to further examine attention levels [ 40 , 41 , 42 , 43 ]. To enhance safety, industrial workplaces of the future will rely on the integration of new industry 4.0 technologies: Safety 4.0 [ 44 , 45 ], Quality 4.0 [ 46 ], Logistics 4.0 [ 47 ], and collaborative robotics, especially from the perspective of ergonomics [ 48 ] and neuroergonomics [ 49 ].…”

Section: Discussionmentioning

confidence: 99%

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

Petrović

Vukićević²,

Djapan³

et al. 2022

Sensors

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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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Section: Discussionmentioning

confidence: 99%

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

Petrović

Vukićević²,

Djapan³

et al. 2022

Sensors

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Advances in the Use of Artificial Intelligence and Sensor Technologies for Managing Industrial Workplace Safety

Vukićević

Petrović

2023

Lecture Notes in Networks and Systems

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The compliance of head-mounted industrial PPE by using deep learning object detectors

Isailović

Peulić

Djapan

et al. 2022

Sci Rep

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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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Assessment of the Human-Robot Collaborative Polishing Task by Using EMG Sensors and 3D Pose Estimation

Cited by 4 publications

References 12 publications

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

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

Advances in the Use of Artificial Intelligence and Sensor Technologies for Managing Industrial Workplace Safety

The compliance of head-mounted industrial PPE by using deep learning object detectors

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