A study to trial the use of inertial non-optical motion capture for ergonomic analysis of manufacturing work

It is going to be increasingly important for manufacturing system designers to incorporate human activity data and ergonomic analysis with other performance data in digital design modelling and system monitoring. However, traditional methods of capturing human activity data are not sufficiently accurate to meet the needs of digitised data analysis; qualitative data are subject to bias and imprecision, and optically derived data are hindered by occlusions caused by structures or other people in a working environment. Therefore, to meet contemporary needs for more accurate and objective data, inertial non-optical methods of measurement appear to offer a solution. This article describes a case study conducted within the aerospace manufacturing industry, where data on the human activities involved in aircraft wing system installations was first collected via traditional ethnographic methods and found to have limited accuracy and suitability for digital modelling, but similar human activity data subsequently collected using an automatic non-optical motion capture system in a more controlled environment showed better suitability. Results demonstrate the potential benefits of applying not only the inertial non-optical method in future digital modelling and performance monitoring but also the value of continuing to include qualitative analysis for richer interpretation of important explanatory factors

Ergonomic posture assessment of butchers: a small enterprise study in Malaysia food industry

The development of small and medium-sized enterprises (SMEs) is crucial for improving the economy of a rural area. However, this can cause working posture problems, such as musculoskeletal disorders (MSDs) and cumulative trauma disorders (CTDs). This is especially true for butchers, who work in SMEs that still depend on manual handling processes without standard operating procedures. Posture analysis evaluations using the Rapid Upper Limb Assessment (RULA) and Rapid Entire Body Assessment (REBA) tools have been used to analyse the working postures of butchers working in SMEs. The aim of this study was to identify butchers’ risks of working posture problems, and to propose an ergonomic workstation designed to reduce MSDs and CTDs. This study was focused on smoked meat preparation. The butchers there spend 5–8 hours a day cutting and trimming meat. The assessment was conducted using RULA and REBA worksheets. The RULA score for the meat trimming process was 7, with a score of 6 for the meat cutting process. As for REBA, the score was 5 for both the meat trimming and meat cutting processes. Based on these scores, the butchers were at higher risks for MSDs and CTDs. Therefore, a new ergonomic workstation design was proposed based on the principles of motion economy

Design Work Station Of Pipe Welding With Ergonomic Approach

The activity of welding specimens on a big pipe that causes various problems for the body, in this activity the worker is at a risky position such as lifting a pipe weight 90 kg, lifting the specimen and welding the pipe with the specimen and the final process is to lower the pipe that has been connected. The purpose of this study is to design workstation by the principles of ergonomics to help reduce physical worker complaints. The research method in this study was the Nordic Body Map (NBM) questionnaire to determine complaints of musculoskeletal disorders (MSDs); the work posture was analyzed by the Rapid Entire Body Assessment (REBA) method. From the results of this study, it can be concluded that the welding work requires a tool in the form of a bench, pipe support, a pulley used at a new welding workstation. With a new workstation, poor work posture can be repaired. With a new work station, there is an efficiency of 8.33 minutes of work time from previous working conditions

ASSESSMENT OF WORK POSTURES ON NON-MECHANICAL RICE HARVESTING (CASE STUDIES IN BANTUL AND SLEMAN DISTRICTS, DIY PROVINCE)

Rice harvesting is a high ergonomic risk due to the working position, an awkward posture, and the repetition activity. Rice harvesting causes body pain in the part of low back, hand, and wrist. This study aims to (1) identify the characteristics of non-mechanical rice harvesting; (2) determine the working posture of rice harvesters using the Ovako Working Assessment System (OWAS) method, Quick Exposure Checklist (QEC), Rapid Entire Body Assessment (REBA), Postural Ergonomics Risk Assessment (PERA); and (3) determine the best method to assess harvesters work posture. An observation was conducted to nine of healthy workers in Bantul and Sleman districts, Daerah Istimewa Yogyakarta (DIY) province. The questionnaire was set to collect respondent demographics data. Data for harvester body posture (neck, trunk, leg, wrist, lifting load, shoulder) repetition, duration and force were collected by observation in the field. A sickle was used to cut rice straw, while a manual gepyok and mobile hand thresher was used to threshing rice panicles. Four methods were applied to assess the work posture, i.e., OWAS, QEC, REBA, PERA methods. Based on observation, five workstations were identified: (1) cutting rice straw, (2) transporting rice straw, (3) threshing of panicles, (4) sorting, and (5) packaging and transporting. REBA and PERA showed a similar trend of the ergonomic risk, high to low risk took place in workstation transporting rice straw, packaging and transporting, cutting, and threshing. The assessment of work posture that is most suitable for non-mechanical harvesting methods was the REBA method with an accuracy of 92.9%

인간공학적 자세 평가를 위한 비디오 기반의 작업 자세 입력 시스템 개발

학위논문(석사) -- 서울대학교대학원 : 공과대학 산업공학과, 2022. 8. 윤명환.Work-related musculoskeletal disorders are a crucial problem for the worker’s safety and productivity of the workplace. The purpose of this study is to propose and develop a video-based work pose entry system for ergonomic postural assessment methods, Rapid Upper Limb Assessment(RULA) and Rapid Entire Body Assessment(REBA). This study developed a work pose entry system using the YOLOv3 algorithm for human tracking and the SPIN approach for 3D human pose estimation. The work pose entry system takes in a 2D video and scores of few evaluation items as input and outputs a final RULA or REBA score and the corresponding action level. An experiment for validation was conducted to 20 evaluators which were classified into two groups, experienced and novice, based on their level of knowledge or experience on ergonomics and musculoskeletal disorders. Participants were asked to manually evaluate working postures of 20 working videos taken at an automobile assembly plant, recording their scores on an Excel worksheet. Scores were generated by the work pose entry system based on individual items that need to be inputted, and the results of manual evaluation and results from the work pose entry system were compared. Descriptive statistics and Mann-Whitney U test showed that using the proposed work pose entry system decreased the difference and standard deviation between the groups. Also, findings showed that experienced evaluators tend to score higher than novice evaluators. Fisher’s exact test was also conducted on evaluation items that are inputted into the work pose entry system, and results have shown that some items that may seem apparent can be perceived differently between groups as well. The work pose entry system developed in this study can contribute to increasing consistency of ergonomic risk assessment and reducing time and effort of ergonomic practitioners during the process. Directions for future research on developing work pose entry systems for ergonomic posture assessment using computer vision are also suggested in the current study.작업 관련 근골격계 질환은 근로자의 안전과 작업장의 생산성 향상에 중요한 문제다. 본 연구의 목적은 인간공학적 자세 분석에 사용되는 대표적인 방법인 Rapid Upper Limb Assessment(RULA) 및 Rapid Entire Body Assessment(REBA)를 위한 비디오 기반의 작업 자세 입력 시스템을 제안하는 것이다. 본 연구는 영상 내 사람 탐지 및 추적을 위한 YOLOv3 알고리즘과 3차원 사람 자세 추정을 위한 SPIN 접근법을 사용하는 시스템을 개발했다. 해당 작업 자세 입력 시스템은 2차원 영상과 몇 개의 평가 항목 점수를 입력으로 받아 최종 RULA 또는 REBA 점수와 해당 조치수준(Action level)을 출력한다. 본 연구에서 제안하는 작업 자세 입력 시스템이 일관적인 결과를 산출하는지 알아보기 위해 인간공학 및 근골격계 질환에 대한 지식이나 경험을 기준으로 숙련된 평가자와 초보 평가자의 두 그룹으로 분류된 평가자 20명을 대상으로 검증 실험을 진행했다. 참가자들은 국내 자동차 조립 공장에서 찍은 20개의 작업 영상의 작업 자세를 수동으로 평가하여 Excel 워크시트에 점수를 기록하였다. 시스템 사용 시 입력해야 하는 개별 항목을 기준으로 시스템을 통한 점수를 생성하고 기존의 전통적인 방법으로 평가한 결과와 시스템에서 얻은 결과를 비교하였으며, 기술 통계와 Mann-Whitney U test는 제안된 시스템을 사용하면 그룹 간의 차이와 표준편차가 감소한다는 것을 보여주었다. 또한, 경험이 많은 평가자들이 초보 평가자들보다 더 높은 점수를 받는 경향이 있다는 것을 보여주었다. 시스템에 입력되는 평가 항목과 경험 정도와의 관계를 확인하기 위해 Fisher’s exact test를 수행하였으며, 결과는 명백해 보일 수 있는 일부 항목도 그룹 간에 다르게 인식될 수 있음을 보여주었다. 이 도구에서 개발된 작업 자세 입력 시스템은 인간공학적 자세 평가의 일관성을 높이고 평가 과정 중 중에 인간공학적 평가자의 시간과 노력을 줄이는 데 기여할 수 있다. 또한 컴퓨터 비전을 활용한 인간공학적 자세 평가를 위한 작업 자세 입력 시스템 개발에 대한 향후 연구 방향도 이번 연구에서 제시된다.Chapter 1 Introduction 1 1.1 Background 1 1.2 Research Objectives 4 1.3 Organization of the Thesis 5 Chapter 2 Literature Review 6 2.1 Overview 6 2.2 Work-related Musculoskeletal Disorders 6 2.3 Ergonomic Posture Analysis 7 2.3.1 Self-reports 7 2.3.2 Observational Methods 7 2.3.3 Direct Methods 15 2.3.4 Vision-based Methods 17 2.4 3D Human Pose Estimation 19 2.4.1 Model-free Approaches 20 2.4.2 Model-based Approaches 21 Chapter 3 Proposed System Design 23 3.1 Overview 23 3.2 Human Tracking 24 3.3 3D Human Pose Estimation 24 3.4 Score Calculation 26 3.4.1 Posture Score Calculation 26 3.4.2 Output of the Proposed System 31 Chapter 4 Validation Experiment 32 4.1 Hypotheses 32 4.2 Methods 32 4.2.1 Participants 32 4.2.2 Apparatus 33 4.2.3 Procedure 33 4.2.4 Data Analysis 37 4.3 Results 38 4.3.1 RULA 38 4.3.2 REBA 46 4.3.3 Evaluation Items for Manual Input 54 Chapter 5 Discussion 56 5.1 Group Difference 56 5.1.1 RULA 57 5.1.2 REBA 57 5.2 Evaluation Items for Manual Input 58 5.3 Proposed Work Pose Entry System 59 Chapter 6 Conclusion 62 6.1 Conclusion 62 6.2 Limitation, Contribution, and Future Direction 62 Bibliography 65 국문초록 77석

A Parameterized Design Optimization Framework for Worker-Friendly Workplaces in Modular Construction

Workers in modular construction suffer frequent exposure to ergonomic risks that lead to work-related musculoskeletal disorders (WMSDs). Addressing ergonomic risk factors is thus critical to enhance the productivity of production lines and reduce social expenses for workers’ recovery. Towards this goal, an ergonomic-driven workplace design approach is essential to not only prevent risks through design changes proactively but also accommodate medical restrictions for workers getting back on the job during the health recovery period. However, a lack of methods to identify root causes of ergonomic risks among various workplace design parameters (WDPs) and design optimal workplace settings for complex and multiple tasks leads to difficulties in adopting this twofold design approach. To address this limitation, this thesis proposes a parameterized workplace design optimization framework that involves four procedures: (i) performing design initiation to identify WDPs and accordingly create design alternatives using the definitive screening design (DSD) method; (ii) building interactive worker-workplace simulation models to acquire workers’ body posture data and assess ergonomic risks among the different design alternatives; (iii) developing predictive surrogate models of the tasks using DSD statistical analysis; and (iv) optimizing workplace settings using the genetic algorithm to minimize ergonomic risk scores. The proposed framework is demonstrated through a case study to design a drywall preparation workplace in a real modular construction plant

Ergonomic Risk Assessment of Maintenance Workers in Educational Institute

Risks associated with ergonomics are pervasive groups that have crept into people\u27s daily lives. Ergonomic risks have been linked to long-term health effects like musculoskeletal disorders, cumulative trauma disorders, and lower back pain. Due to the limitations of their jobs, workers are most impacted by these repetitive, continuous labor activities. The main objective of this project is to provide a solution to the ergonomic problems that construction workers in the educational maintenance industry encounter. Based on the conditions, only a few jobs were identified, and samples were obtained by watching and asking people about their jobs. For tasks like plantation, rebar bending, material transportation, etc., the RULA and REBA method is used in this project. This study aims to investigate the ergonomic hazards experienced by educational maintenance construction workers

Ergonomic risk analysis inherent in neonate bathing activity performed by nurses using the REBA methodology through kinect depth sensors

The absence of quantitative parameters to determine the mobility of the body segments required by functional assessment scales such as Rapid Entire Body Assessment (REBA) reduces its reliability by identifying risks based only on postural observation. This work measures the ergonomic risk associated with the neonatal bathing activity performed by nurses, the influence of the introduction of Kinect sensors, and their marker-less skeleton tracking function in conjunction with the postural analysis tool REBA to reduce the inter-observer variability and the subjectivity of the results. Many people without injuries reproduced the sequence of movements of a baby's body wash task, selected as the most critical within the activity. The use of a reference motion capture system, such as photogrammetry, was used to check the validity of Kinect as a measurement instrument and its precision. Variables such as the recording frequency of the sensors, and their location to the participants, influence the detection of body positions. This paper demonstrates the need for improving the nurses’ posture because it is associated with an intermediate level of ergonomic risk and requires intervention

Risk assessment for musculoskeletal disorders in forestry: A comparison between RULA and REBA in the manual feeding of a wood-chipper

The analysis of the postural attitude of workers during the interaction with workstation\u2019s elements and working environment is essential in the evaluation and prevention of biomechanical overload risk in workplaces. RULA (Rapid Upper Limb Assessment) and REBA (Rapid Entire Body Assessment) are the two easiest methods for postural risk assessment in the workplace. Few studies investigated postural risk in forestry sector with regard to human\u2013machine interaction, in particular manually fed wood-chippers. The aim of this study was to evaluate the postures assumed by an operator during the manual feeding of a wood-chipper, and to compare RULA and REBA, in order to identify the more effective and appropriate method for the assessment of the risk of biomechanical postural overload. The results pointed out several postural issues of the upper limbs, and showed that RULA is a more precautionary method to protect operator\u2019s health during the targeted tasks. Implications to improve the human\u2013wood-chipper interaction are discussed

The development of fully automated RULA assessment system based on Computer Vision

The purpose of this study was to develop an automated, RULA-based posture assessment system using a deep learning algorithm to estimate RULA scores, including scores for wrist posture, based on images of workplace postures. The proposed posture estimation system reported a mean absolute error (MAE) of 2.86 on the validation dataset obtained by randomly splitting 20% of the original training dataset before data augmentation. The results of the proposed system were compared with those of two experts’ manual evaluation by computing the Intraclass correlation coefficient (ICC), which yielded index values greater than 0.75, thereby confirming good agreement between manual raters and the proposed system. This system will reduce the time required for postural evaluation while producing highly reliable RULA scores that are consistent with those generated by manual approach. Thus, we expect that this study will aid ergonomic experts in conducting RULA-based surveys of occupational postures in workplace conditions