A systematic review on digital human models in assembly process planning

Yin, Ming-Yue; Li, Jianguang

doi:10.1007/s00170-023-10804-8

Cited by 9 publications

(3 citation statements)

References 155 publications

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“…After locating and 3D labeling the outlet, obtaining virtual images and annotation information is necessary. This paper combines critical dimensions such as height and arm length of the human body in ergonomics [10], as well as the range of waist bending (within 45 ) and head rotation ( 45 ), to simulate the motion, head rotation, waist bending, and other actions of a virtual agent. It aims to simulate the position and observation situation of a person in a real-world scene and obtain the appropriate position and perspective of the virtual agent observation outlet, including the range of movement (120 ), observation distance (1m), and observation angle (20 ), as shown in Figure 2 (bd).…”

Section: Self-labeling Of Outlet Images Based On Virtual Agentmentioning

confidence: 99%

Outlet Recognition Based on Deep Learning and Sensors for Large-Scale Cable Laying

Chen,

Wang,

Geng

2024

Frontiers in Artificial Intelligence and Applications

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The outlet recognition for large-scale cable laying has problems such as difficulty obtaining prior information, similarity in local recognition information, and low recognition accuracy, making it difficult for most outlet recognition based on real prior information for large-scale cable laying to proceed smoothly. In response to the above issues, this paper proposes an outlet recognition method based on deep learning and sensors for large-scale cable laying. A system of outlet recognition based on deep learning and sensors for large-scale cable laying is formed based on the self-labeling of outlet images, data augmentation of outlet features, and relative pose constraints. The feasibility and effectiveness of this technology have been verified by recognizing outlets at different locations in a large-scale cable laying scene. This technology can recognize and locate outlets, effectively solving the problem of misrecognition at different positions.

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Section: Self-labeling Of Outlet Images Based On Virtual Agentmentioning

confidence: 99%

Outlet Recognition Based on Deep Learning and Sensors for Large-Scale Cable Laying

Chen,

Wang,

Geng

2024

Frontiers in Artificial Intelligence and Applications

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“…The collection, transfer, and exchange of data from IoT devices via communication networks enable real-time interaction and cooperation among physical objects [ 3 ]. A series of virtual simulation-based solutions have been proposed, such as Digital Twin (DT), Cyber-Physical Systems (CPSs), and Digital Human Modelling (DHM), paving the way for fully digitising industrial shop floors [ 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

On the Evaluation of Diverse Vision Systems towards Detecting Human Pose in Collaborative Robot Applications

Ramasubramanian,

Kazasidis,

Fay

et al. 2024

Sensors

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Tracking human operators working in the vicinity of collaborative robots can improve the design of safety architecture, ergonomics, and the execution of assembly tasks in a human–robot collaboration scenario. Three commercial spatial computation kits were used along with their Software Development Kits that provide various real-time functionalities to track human poses. The paper explored the possibility of combining the capabilities of different hardware systems and software frameworks that may lead to better performance and accuracy in detecting the human pose in collaborative robotic applications. This study assessed their performance in two different human poses at six depth levels, comparing the raw data and noise-reducing filtered data. In addition, a laser measurement device was employed as a ground truth indicator, together with the average Root Mean Square Error as an error metric. The obtained results were analysed and compared in terms of positional accuracy and repeatability, indicating the dependence of the sensors’ performance on the tracking distance. A Kalman-based filter was applied to fuse the human skeleton data and then to reconstruct the operator’s poses considering their performance in different distance zones. The results indicated that at a distance less than 3 m, Microsoft Azure Kinect demonstrated better tracking performance, followed by Intel RealSense D455 and Stereolabs ZED2, while at ranges higher than 3 m, ZED2 had superior tracking performance.

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“…On the other side, virtual reality and DHM is very effective to evaluate the human posture in the product development industry (8) . Application of DHM can assess the existing posture in the assembly process and recommend the improved posture alongside the improvement of the assembly process efficiency (9) . When integrated into CATIA's DHM software, the RULA tools, have marked a significant leap in digital ergonomics, enabling detailed simulations of workplace tasks and postures for precise ergonomic analysis.…”

Section: Introductionmentioning

confidence: 99%

Comparative Ergonomic Posture Analysis of CNC Milling Machine Workers through Digital Human Modeling and Artificial Neural Networks

Roy,

Murad,

Billah

et al. 2024

IJST

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Objectives: To analyze the critical postures of the CNC milling machine operators by RULA (Rapid Upper Limb Assessment) scores and develop an ANN (Artificial Neural Network) prediction model. Methods: The methodology includes a postural analysis of 40 male CNC milling machine operators across Bangladesh, employing both manual (using manual RULA assessment worksheet) and digital (using CATIA V5R21 software) RULA methods complemented by an ANN prediction model. Finally, Digital RULA scores through DHM (Digital Human Modeling) and ANN predicted RULA scores would be compared. Findings: Digital RULA analysis reveals that lifting, carrying, and positioning are the most crucial ergonomic postures, and the most prominent high-risk category limbs are wrist and arm. The overall initial RULA score for lifting, carrying, and positioning are 7, 6, and 7, respectively, and reduced to 3, 3 and 4 respectively for ergonomically designed posture. The ANN model, structured with input, hidden, and output layers of 7, 10, and 1 nodes, significantly refines ergonomic risk prediction by aligning predicted scores closely with actual outcomes during the first stage, emphasized for training. It demonstrates a perfect correlation (R=1) in training, testing, validation, and overall performance for using manual RULA scores. The model's accuracy is further evidenced by minimal prediction offsets across all datasets for digital RULA score in the second stage, with correlation coefficients of 0.87003 (training), 0.93676 (validation), 0.89113 (testing), and (0.88395) for overall. This study contributes significant advancements in ergonomic risk assessment, highlighting the adoption of improved postures to reduce musculoskeletal disorders. Novelty: Employing both manual and DHM methods for RULA score calculation combined with ANN model, which can predict postural risk as floating number and fit a wider range of parameters. Keywords: ANN, CNC, Digital Human Modeling (DHM), Ergonomics, RULA

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A systematic review on digital human models in assembly process planning

Cited by 9 publications

References 155 publications

Outlet Recognition Based on Deep Learning and Sensors for Large-Scale Cable Laying

Outlet Recognition Based on Deep Learning and Sensors for Large-Scale Cable Laying

On the Evaluation of Diverse Vision Systems towards Detecting Human Pose in Collaborative Robot Applications

Comparative Ergonomic Posture Analysis of CNC Milling Machine Workers through Digital Human Modeling and Artificial Neural Networks

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