Search Results (15)

In forest operations, traditional ergonomic studies have been carried out by assessing body posture manually, but such assessments may suffer in terms of efficiency and reliability. Advancements in machine learning provided the opportunity to overcome many of the limitations of the manual approach. This study evaluated the intra- and inter-reliability of postural assessments in manual and motor-manual forest operations using the Ovako Working Posture Analysing System (OWAS)—which is one of the most used methods in forest operations ergonomics—by considering the predictions of a deep learning model as reference data and the rating inputs of three raters done in two replicates, over 100 images. The results indicated moderate to almost perfect intra-rater agreement (Cohen’s kappa = 0.48–1.00) and slight to substantial agreement (Cohen’s kappa = 0.02–0.64) among human raters. Inter-rater agreement between pairwise human-model datasets ranged from poor to fair (Cohen’s kappa = −0.03–0.34) and from fair to moderate when integrating all the human ratings with those of the model (Fleiss’ kappa = 0.28–0.49). The deep learning (DL) model highly outperformed human raters in assessment speed, requiring just one second per image, which, on average, was 19 to 53 times faster compared to human ratings. These findings highlight the efficiency and potential of integrating DL algorithms into OWAS assessments, offering a rapid and resource-efficient alternative while maintaining comparable reliability. However, challenges remain regarding subjective interpretations of complex postures. Future research should focus on refining algorithm parameters, enhancing human rater training, and expanding annotated datasets to improve alignment between model outputs and human assessments, advancing postural assessments in forest operations. Full article

Data on work accidents reflect the incidence of harm to workers’ health and occupational diseases, supported by studies that indicate the influence of length of service on service, age, and dominant skills as contributing factors to occupational accidents. This study aimed to assess whether the working environment conditions were favorable to workers and to determine whether gender, age, and length of service influenced the occurrence of work-related accidents. The goal was to identify and mitigate risk factors to improve worker health. Descriptive statistics techniques, including Pearson correlation, Analysis of Variance, the Tukey’s test, and Cluster Analysis were applied. Additionally, a categorical variable analysis (survey) was conducted to assess the work environment, alongside postural analysis using the OWAS (Ovako Working Posture Analyzing System) method. The results revealed noise levels exceeding recommended limits in almost all investigated sectors, as well as inadequate illuminance and temperature conditions on the production line. The clustering analysis identified three distinct groups. Group 1: Individuals aged 18 to 27 with little experience in the activity, of whom 42% reported pain or discomfort. Group 2: Older operators with 62% experiencing pain or discomfort. Group 3: Young male workers with experience in the role, a higher incident of work accidents, and alcohol consumption up to three times a week, of whom 50% reported pain or discomfort. Statistical inference allowed the identification of process deficiencies and a detailed analysis of work-related pain through self-perceived diagnosis, enabling corrective actions to similar processes and contributing to existing research. Full article

Forest operations often expose workers to physical risks, including posture-related disorders such as low back pain. The Ovako Working Posture Assessment System (OWAS) is widely used to assess postures in forest operations, but it requires expertise and significant resources. In this study, the use of image embedding and transfer learning was explored to automate OWAS classification. Over 5000 images from motor–manual cross-cutting operations were analyzed using two models: Google’s Inception V3 and SqueezeNet, both of which were integrated with neural networks via the Orange Visual Programming platform. The image vectors were fed into a locally run neural network (a multilayer perceptron with backpropagation) that was optimized for architecture and hyperparameters. The models were trained and tested using 20-fold cross-validation on the Posture and Action datasets, achieving accuracies of 84% and 89%, respectively, with Inception V3 outperforming SqueezeNet on both datasets. Predictions on unseen images yielded lower accuracies (50%–60%), highlighting the challenge of domain differences. These results demonstrate the potential of embedding-based transfer learning to automate postural classification with high accuracy, thereby reducing the need for expertise and resources. However, further research is needed to improve performance on unseen data and to explore alternative classifiers and embedding methods for better representation. Full article

Ergonomics is essential to improving workplace safety and efficiency by reducing the risks associated with physical tasks. This study presents a decision support system (DSS) aimed at enhancing production ergonomics in the construction sector through an analysis of high-risk postures. Using the Ovako Work Posture Analysis System (OWAS), the Revised NIOSH Lifting Equation (NIOSH equation) and Rapid Entire Body Assessment (REBA), the DSS identifies ergonomic risks by assessing body postures across common construction tasks. Three specific postures—X, Y and Z—were selected to represent typical construction activities, including lifting, squatting and repetitive tool use. Posture X, involving a forward-leaning stance with arms above the shoulders and a 25 kg load, was identified as critical, yielding the highest OWAS and NIOSH values, thus indicating an immediate need for corrective action to mitigate risks of musculoskeletal injuries. The DSS provides recommendations for workplace adjustments and posture improvements, demonstrating a robust framework that can be adapted to other postures and industries. Future developments may include application to other postures and sectors, as well as the use of artificial intelligence to support ongoing ergonomic assessments, offering a promising solution to enhance Occupational Safety and Health policies. Full article

Due to the complexity of the automobile manufacturing process, some flexible and delicate assembly work relies on manual operations. However, high-frequency and high-load repetitive operations make assembly workers prone to physical fatigue. This study proposes a method for evaluating human physical fatigue for the manual assembly of automobiles with methods: NIOSH (National Institute for Occupational Safety and Health), OWAS (Ovako Working Posture Analysis System) and RULA (Rapid Upper Limb Assessment). The cerebral oxygenation signal is selected as an objective physiological index reflecting the human fatigue level to verify the proposed physical fatigue evaluation method. Taking auto seat assembly and automobile manual assembly as an example, 18 group experiments were carried out with the ARE platform (Augmented Reality-based Ergonomic Platform). Furthermore, predictions of metabolic energy expenditure were performed for experiments in Tecnomatix Jack. Finally, it is concluded that the proposed physical fatigue evaluation method can reflect the human physical fatigue level and is more accurate than the evaluation of metabolic energy consumption in Tecnomatix Jack because of the immersion that comes with the AR devices and the precision that comes with motion capture devices. Full article

Recently, musculoskeletal disorders (MSDs) caused by repetitive working postures in industrial sites have emerged as one of the biggest problems in the field of industrial health. The risk of MSDs caused by the repetitive working postures of workers is quantitatively evaluated by using NLE (NIOSH Lifting Equation), OWAS (Ovako Working-posture Analysis System), RULA (Rapid Upper Limb Assessment), REBA (Rapid Entire Body Assessment), etc. Methods used for the working posture analysis include vision-based analysis and motion capture analysis. Vision-based analysis is a method where an expert with ergonomics knowledge watches and manually analyzes recorded working images. Although the analysis is inexpensive, it takes a lot of time to analyze. In addition, the analyst’s subjective opinions or mistakes may be reflected in the results, so it may be somewhat unreliable. On the other hand, motion capture analysis can obtain more accurate and consistent results, but its measurement equipment is very expensive and it requires a large space for measurement. In this paper, we propose a computer-based automated REBA system that can evaluate, automatically and consistently, working postures in order to supplement the shortcomings of these existing methods. The CREBA system uses the body detection learning model of MediaPipe to detect the worker’s area in the recorded images and sets the body area based on the position of the face, detected using the face tracking learning model. In the set area, the positions of joints are tracked using the posture tracking learning model, and the angles of joints are calculated based on the joint positions using the inverse kinematics, and then by automatically calculating the degree of load of the working posture with the REBA evaluation method. In order to verify the accuracy of the evaluation results of the CREBA system, we compared them with the experts’ vision-based REBA evaluation results. The result of the experiment showed a slight difference of about 1.0 points between the evaluation results of the expert group and those of the CREBA system. It is expected that the ergonomic analysis method for the working posture used in this study will reduce workers’ labor intensity and improve their safety and efficiency. Full article

Ergonomics is important for smooth and sustainable industrial operation. In the manufacturing industry, due to poor workstation design, workers frequently and repeatedly experience uncomfortable postures and actions (reaching above their shoulders, bending at awkward angles, bending backwards, flexing their elbows/wrists, etc.). Incorrect working postures often lead to specialized injuries, which reduce productivity and increase development costs. Therefore, examining workers’ ergonomic postures becomes the basis for recognizing, correcting, and preventing bad postures in the workplace. This paper proposes a new framework to carry out risk analysis of workers’ ergonomic postures through 3D human pose estimation from video/image sequences of their actions. The top-down network calculates human body joints when bending, and those angles are compared with the ground truth body bending data collected manually by expert observation. Here, we introduce the body angle reliability decision (BARD) method to calculate the most reliable body-bending angles to ensure safe working angles for workers that conform to ergonomic requirements in the manufacturing industry. We found a significant result with high accuracy in the score for ergonomics we used for this experiment. For good postures with high reliability, we have OWAS score 94%, REBA score 93%, and RULA score 93% accuracy. Similarly, for occluded postures we have OWAS score 83%, REBA score 82%, and RULA score 82%, compared with expert’s occluded scores. For future study, our research can be a reference for ergonomics score analysis with 3D pose estimation of workers’ postures. Full article

The purpose of this study is to investigate the effect of forest conservation activities on the physical and psychological wellbeing of participants. The experiment was conducted in a forest near an urban area and involved 61 participants (average age: 22.5 ± 1.8). The participants selected one of three activities (pruning, stacking cut branches, and removing vines) in the forest conservation program. The effects of these activities on the musculoskeletal system were assessed using the Ovako Working Posture Assessment System (OWAS); the physical intensity of the activities was evaluated using heart rate data. The psychological evaluation measurement indexes used the Positive and Negative Affect Schedule, Rosenberg Self-Esteem scale, World Health Organization Quality of Life assessment instrument, and the Perceived Restorativeness Scale. As a result of the OWAS assessment, forest conservation activities were found to be action categories 1 and 2, which were less burdensome to the musculoskeletal system. All forestry activities were found to be light levels of physical intensity. Psychological evaluation of the participants revealed that positive emotions such as self-esteem, quality of life, and perceived restorativeness increased significantly, whereas negative emotions decreased significantly. This forest conservation program, that involved low-intensity activities which were less burdensome to the musculoskeletal system, had positive physical and psychological effects on the local residents who participated. Full article

This study analyzes an existing scenario of musculoskeletal disorder (MSD) associated with the ergonomic hazard of the aluminum formwork workstation and its workers. Aluminum form-workers have increasing evidence of MSDs from repetitive tasks such as the adjustment, alignment of pins, pulling, pushing, and installation of panels, because of the cumulative exposure to ergonomic risks. Existing research indicates that this is due to insufficient expertise, form-worker awareness, and a complex construction plan. Using the Tecnomatix process simulate, this study aims to identify awkward postures during the process of lifting, assembling, and installing formwork to quantify MSDs and assess the ergonomic risk of aluminum form-workers and provide simple solutions. This simulation method makes use of input data from a random sample of 92 participants retrieved from four construction sites. The Rapid Upper Limb Assessment (RULA), Ovako Working Analysis System (OWAS) scores, and Energy Expenditure Rate (EER) for three identified awkward cases were determined to be unsatisfactory, unsafe, and acceptable with suggested alternatives. The ergonomic scores correspond to various bodily stresses, allowing workers to better understand which body parts experience major stress when performing manual jobs. The suggested integrated preventive ergonomics system reduces MSDs and improves how people interact with their surroundings. Full article

This study aimed to systematically compare three representative observational methods for assessing musculoskeletal loadings and their association with musculoskeletal disorders (MSDs): Ovako Working Posture Analysis System (OWAS), Rapid Upper Limb Assessment (RULA), and Rapid Entire Body Assessment (REBA). The comparison was based on a literature review without time limitations and was conducted on various factors related to observational methods. The comparisons showed that although it has a significant limitation of comprising only two classifications for the leg postures, (1) the RULA is the most frequently used method among the three techniques; (2) many studies adopted the RULA even in evaluation of unstable lower limb postures; (3) the RULA assessed postural loads as higher risk levels in most studies reviewed in this research; (4) the intra- and inter-reliabilities for the RULA were not low; and (5) the risk levels assessed by the RULA were more significantly associated with postural load criteria such as discomfort, MHTs and % capable at the trunk, and MSDs. Full article

The logistics sector should strive for sustainability alongside productivity by protecting its order pickers’ health and welfare. Existing storage assignment models are mainly based on the criterion of order picking time and, to a lesser extent, the human factor. In the paper, a solution to a storage assignment problem using a multi-objective model based on binary integer linear programing is presented by developing a solution that considers order picking time, energy expenditure and health risk. The Ovako Working Posture Assessment System (OWAS) method was used for health risk assessment. The downside of solely health risk-optimization is that the average order picking time increases by approximately 33% compared to solely time-optimization. Contrary to this, the developed multi-objective function emphasizing time has proven to be promising in finding a compromise between the optimal order picking time and eliminating work situations with a very-high risk for injuries. Its use increases the time by only 3.8% compared to solely time-optimization while significantly reducing health risk. Full article

Agricultural upper limb assessment (AULA), which was developed for evaluating upper limb body postures, was compared with the existing assessment tools such as rapid upper limb assessment (RULA), rapid entire body assessment (REBA), and ovako working posture analysis system (OWAS) based on the results of experts’ assessments of 196 farm tasks in this study. The expert group consisted of ergonomists, industrial medicine experts, and agricultural experts. As a result of the hit rate analysis, the hit rate (average: 48.6%) of AULA was significantly higher than those of the other assessment tools (RULA: 33.3%, REBA: 30.1%, and OWAS: 34.4%). The quadratic weighted kappa analysis also showed that the kappa value (0.718) of AULA was significantly higher than those of the other assessment tools (0.599, 0.578, and 0.538 for RULA, REBA, and OWAS, respectively). Based on the results, AULA showed a better agreement with expert evaluation results than other evaluation tools. In general, other assessment tools tended to underestimate the risk of upper limb posture in this study. AULA would be an appropriate evaluation tool to assess the risk of various upper limb postures. Full article

Occupational health and safety allows the prevention of occupational diseases and accidents. Agriculture is one of the sectors in which it is important to prevent the musculoskeletal disorders that workers usually develop. The objective of this study is the evaluation of postures adopted by courgette farmers in greenhouses of the Almeria-type. OWAS (Ovako Working Posture Assessment System), an ergonomic evaluation method, is used and applied after making observations to the postures adopted by the workers who were previously video recorded. The results concluded that the four risk levels established by OWAS appeared, with 37.14% being the highest rate and belonging to risk level 2, 33.33% to risk level 1, 28.57% to risk level 3, and 0.95% to risk level 4. Therefore, depending on the severity of the postures adopted in each task, the need for changes in a short, medium, or long period of time was concluded. Full article

Health and safety at work directly influence the development of sustainable agriculture. In the agricultural sector, many farm workers suffer musculoskeletal disorders caused by forced posture. The objective of this research is to assess working postures during melon cultivation in Almería-type greenhouses. The Ovako Working Posture Assessment System (OWAS) has been used with pictures of the tasks. The variables studied by multiple correspondence analysis were as follows: Subtask, Posture code, Back, Arms, Legs, Load, Risk, and Risk combination. The OWAS analysis showed that 47.57% of the postures were assessed as risk category 2, 14.32% as risk category 3, 0.47% as risk category 4, and the rest as risk category 1. Corrective measures should be implemented immediately, as soon as possible, or in the near future, depending on the risks detected. Full article

The study sampled six representative work sites in Northern and Central Italy, in order to assess the risk for developing musculo-skeletal disease due to poor work posture (postural risk) among the operators engaged in semi-mechanized post debarking operations. Assessment was conducted with the Ovako Working posture Analysis System (OWAS) on 1200 still frames randomly extracted from videotaped work samples. The postural risk associated with post debarking was relatively low, and varied with individual operations based on their specific set up. Postural risk was higher for the loading station compared with the unloading station, which makes a strong argument for job rotation. The study suggested that the infeed chute of small-scale debarkers might be too basic and should be further developed, in order to reduce postural risk. Obviously, better machine design should be part of an articulate strategy aimed at decreasing the postural risk and based on proper worksite organization and specific worker training. Full article