Ergonomic assessment of office worker postures using 3D automated joint angle assessment

Rodrigues, Patrick B.; Xiao, Yue; Fukumura, Yoko E.; Awada, Mohamad; Aryal, Ashrant; Becerik-Gerber, Burçin; Lucas, Gale M.; Roll, Shawn C.

doi:10.1016/j.aei.2022.101596

Cited by 15 publications

(4 citation statements)

References 57 publications

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“… Small number of participants. Rodrigues et al [ [ 44 ]] Quantitative 3D Automated Joint Angle Assessment (3D-AJA), Rapid Upper Limb Assessment (RULA) Mix RGB-D camera, Questionnaire Kinect SDK 3D-AJA outperformed the Kinect SDK body joint capture method for estimating all three selected angles with significant differences in shoulder flexion and elbow flexion angles. In terms of determining the resulting classification model for RULA score A, the dataset from 3D-AJA provided a better classification model for all RULA score A classes included in the dataset.…”

Section: Methodsmentioning

confidence: 99%

A systematic review of research on sitting and working furniture ergonomic from 2012 to 2022: Analysis of assessment approaches

Bai,

Kamarudin,

Alli

2024

Heliyon

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Section: Methodsmentioning

confidence: 99%

A systematic review of research on sitting and working furniture ergonomic from 2012 to 2022: Analysis of assessment approaches

Bai,

Kamarudin,

Alli

2024

Heliyon

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“…Kinect software development kit for 3D automated joint evaluation. Standard deviations of the mean absolute error range from ±5.6 deg, ±5.1 deg to ±8.5 deg and ±8.1 deg [2]. An RGB-Depth camerabased automated system is developed to detect and identify activities involving the upper limbs [6].…”

Section: Related Workmentioning

confidence: 99%

“…The personnel in the IT sector have their pattern of work in stable body gestures and insufficient movements of smaller body parts like the wrist and fingers. This way of working style makes the person experience symptoms like stiffness of joints, redness, and swelling in the affected areas of body parts, the surface color of a human also may change, and it may cause the reduction of sweat flowing [2]. Also, psychology is like social risk factors Work-related stress, lack of support from colleagues or Managers, excessive mental workload, and lack of recognition Jobs done are add-ons in trigger-Development of WMSDs in this field.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence (AI) Powered Precise Classification of Recuperation Exercises for Musculoskeletal Disorders

Ekambaram¹,

Ponnusamy²,

Natarajan³

et al. 2023

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Musculoskeletal pain is one of the significant health issues faced by the Information Technology (IT) industries and health-care professional personnel. The current IT sector requires people working on sitting in one place for long hours (~3-4 hours). This causes severe hip, neck, and shoulder pain and may lead to paralysis. Convergence of a threedimensional (3D) image into a plane-based projection to precisely classify the trunk extension and flexion, wrist extension and flexion exercises posture images. Because the predictions in different planes are incorrectly detected during the convergence process, a deep learning algorithm is a superior technique for improving recognition accuracy and processing speed. 200 image datasets of the wrist, trunk extension, and flexion exercise posture are created at various planes. The proposed deep learning algorithm performance is compared with CNN with accelerometer sensing image data, DNN with RGB images, CNN-GRU with Kinect Depth images, Deep Hybrid CNN with body portion keyframe images, Spatial Transform Networks (STN) with attention-based multi-scale CNN with Grad CAM images. The observation demonstrates the efficiency of these systems in musculoskeletal rehabilitation therapy in that the suggested deep learning-based system successfully identifies the completion of rehabilitation activities with a recognition of training accuracy of 98.12% and validation accuracy of 95%. Our approach can track and enhance the efficiency of patients' rehabilitation training with greater satisfactory precision than some other cutting-edge conventional CNN-based baseline architecture.

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“…After obtaining the worker's pose skeletal data, WMSD risk assessment can be conducted using risk assessment rules. Tis approach is currently a trend in machine vision-based human-machine engineering risk assessment [7]. However, there are still some issues to be addressed.…”

Section: Introductionmentioning

confidence: 99%

Posture Risk Assessment and Workload Estimation for Material Handling by Computer Vision

Wang,

Chen

et al. 2023

International Journal of Intelligent Systems

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Workers in material handling tasks often suffer from work-related musculoskeletal disorders (WMSDs) caused by inaccurate work postures or the lifting of excessively heavy loads. Therefore, effective ergonomic assessment of workers is needed to improve worker productivity while reducing the risk of musculoskeletal disorders. This paper proposes a noninvasive method for evaluating posture risks and load analysis in manual material handling tasks. The study focuses on three main aspects: first, using 3D pose recognition technology to extract the 3D coordinates and joint angles of the human body. Second, the REBA method was improved by using fuzzy logic theory to more effectively capture the slow transition features of continuous movement by humans without abruptly altering risk scores, as well as to increase the accuracy and consistency of posture risk evaluation. Third, joint torque and workloads were estimated using biomechanical calculations by integrating pressure insoles and 3D joint coordinate data. Experiments show that this method can effectively evaluate posture risks and workloads in manual material handling tasks, with a correlation coefficient of 0.817 ( p < 0.01 ) between fuzzy logic REBA and REBA and an error rate of 15% in estimating workloads of eight joints. This method can help reduce occupational health risks for workers and industries and improve work efficiency.

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Ergonomic assessment of office worker postures using 3D automated joint angle assessment

Cited by 15 publications

References 57 publications

A systematic review of research on sitting and working furniture ergonomic from 2012 to 2022: Analysis of assessment approaches

A systematic review of research on sitting and working furniture ergonomic from 2012 to 2022: Analysis of assessment approaches

Artificial Intelligence (AI) Powered Precise Classification of Recuperation Exercises for Musculoskeletal Disorders

Posture Risk Assessment and Workload Estimation for Material Handling by Computer Vision

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