An Evaluation of Posture Recognition Based on Intelligent Rapid Entire Body Assessment System for Determining Musculoskeletal Disorders

Compensation Method for Missing and Misidentified Skeletons in Nursing Care Action Assessment by Improving Spatial Temporal Graph Convolutional Networks

With the increasing aging population, nursing care providers have been facing a substantial risk of work-related musculoskeletal disorders (WMSDs). Visual-based pose estimation methods, like OpenPose, are commonly used for ergonomic posture risk assessment. However, these methods face difficulty when identifying overlapping and interactive nursing tasks, resulting in missing and misidentified skeletons. To address this, we propose a skeleton compensation method using improved spatial temporal graph convolutional networks (ST-GCN), which integrates kinematic chain and action features to assess skeleton integrity and compensate for it. The results verified the effectiveness of our approach in optimizing skeletal loss and misidentification in nursing care tasks, leading to improved accuracy in calculating both skeleton joint angles and REBA scores. Moreover, comparative analysis against other skeleton compensation methods demonstrated the superior performance of our approach, achieving an 87.34% REBA accuracy score. Collectively, our method might hold promising potential for optimizing the skeleton loss and misidentification in nursing care tasks.

A Feasibility Study on the Conversion from Manual to Semi-Automatic Material Handling in an Oil and Gas Service Company

In manufacturing companies, manual material handling (MMH) involves lifting, pushing, pulling, carrying, moving, and lowering objects, which can lead to musculoskeletal disorders (MSDs) among workers, resulting in high labor costs due to excessive overtime incurred for manual product preparation. The aim of this study was to show how ergonomic measures were used to reduce the risk of MSDs and to reduce operating costs in the warehouse department of an oil and gas service company. A preliminary study using the Nordic Body Map survey showed that the workers experienced pain in various parts of the body, indicating the presence of MSDs. The researchers then used methods such as the Rapid Upper Limb Assessment (RULA), Rapid Entire Body Assessment (REBA), and National Institute for Occupational Safety and Health (NIOSH) assessments to verify whether the MMH activities had an acceptable level of risk. The results revealed that certain manual material handling (MMH) activities were assessed as low–very high risk, with RULA scores ranging from 3 to 7 and REBA scores ranging from 4 to 11. An immediate solution was to replace the manual process with a semi-automatic process using a vacuum lifter. A feasibility study was conducted using the net present value (NPV), internal rate of return (IRR), and payback period to justify the economic viability of the solution. The analysis indicated that implementing the vacuum lifter not only mitigated the risk of MSDs but also reduced the operating costs, demonstrating its viability and profitability. Overall, this study suggests that implementing a vacuum lifter as an assistive device in the warehouse would be a beneficial investment for both the workers and the company, improving both well-being and finances.

Analysis of the Reduction of Ergonomic Risks through the Implementation of an Automatic Tape Packaging Machine

Many industrial sectors still lack automation resources to optimize their production processes, aiming to make manufacturing leaner and offer better working conditions to operators. Without these improvements, workers can suffer physical and even psychological damage from the ergonomic risks of the activities performed. Thus, the aim of this paper is to present the ergonomic evaluation of packaging tapes workstation before and after the implementation of an automatic packaging machine, called Guzzetti. In the Guzzetti context, the paper shows the implementation of an electrical system based on controlling a mechanical device powered by servomotors and controlled by a PLC is necessary. For ergonomic evaluation, the paper presents the application of three methods: Suzanne Rodger, Strain Index, called Moore and Garg and REBA (Rapid Entire Body Assessment). With the results collection, was possible to obtain improvements in ergonomic risks that changed from the intermediate level to low level in all methods.

Validation of two-dimensional video-based inference of finger kinematics with pose estimation

Accurate capture finger of movements for biomechanical assessments has typically been achieved within laboratory environments through the use of physical markers attached to a participant’s hands. However, such requirements can narrow the broader adoption of movement tracking for kinematic assessment outside these laboratory settings, such as in the home. Thus, there is the need for markerless hand motion capture techniques that are easy to use and accurate enough to evaluate the complex movements of the human hand. Several recent studies have validated lower-limb kinematics obtained with a marker-free technique, OpenPose. This investigation examines the accuracy of OpenPose, when applied to images from single RGB cameras, against a ‘gold standard’ marker-based optical motion capture system that is commonly used for hand kinematics estimation. Participants completed four single-handed activities with right and left hands, including hand abduction and adduction, radial walking, metacarpophalangeal (MCP) joint flexion, and thumb opposition. The accuracy of finger kinematics was assessed using the root mean square error. Mean total active flexion was compared using the Bland–Altman approach, and the coefficient of determination of linear regression. Results showed good agreement for abduction and adduction and thumb opposition activities. Lower agreement between the two methods was observed for radial walking (mean difference between the methods of 5.03°) and MCP flexion (mean difference of 6.82°) activities, due to occlusion. This investigation demonstrated that OpenPose, applied to videos captured with monocular cameras, can be used for markerless motion capture for finger tracking with an error below 11° and on the order of that which is accepted clinically.

A System for a Real-Time Electronic Component Detection and Classification on a Conveyor Belt

The presented research addresses the real-time object detection problem with small and moving objects, specifically the surface-mount component on a conveyor. Detecting and counting small moving objects on the assembly line is a challenge. In order to meet the requirements of real-time applications, state-of-the-art electronic component detection and classification algorithms are implemented into powerful hardware systems. This work proposes a low-cost system with an embedded microcomputer to detect surface-mount components on a conveyor belt in real time. The system detects moving, packed, and unpacked surface-mount components. The system’s performance was experimentally investigated by implementing several object-detection algorithms. The system’s performance with different algorithm implementations was compared using mean average precision and inference time. The results of four different surface-mount components showed average precision scores of 97.3% and 97.7% for capacitor and resistor detection. The findings suggest that the system with the implemented YOLOv4-tiny algorithm on the Jetson Nano 4 GB microcomputer achieves a mean average precision score of 88.03% with an inference time of 56.4 ms and 87.98% mean average precision with 11.2 ms inference time on the Tesla P100 16 GB platform.

Musculoskeletal symptoms and associated factors among manual porcelain workers at different workstations: a cross-sectional study

OBJECTIVES

To investigate the prevalence of work-related musculoskeletal disorders (WMSDs) symptoms and to identify the associated factors (individual, and work-related) among manual porcelain workers at different workstations. The risk level of each workstation was also assessed based on the working postures for the purpose of improving occupational health.

METHODS

In total, 349 workers were recruited for this cross-sectional study. The Nordic Musculoskeletal Questionnaire (NMQ) was used to collect data on WMSDs symptoms in nine body regions. The relationship between individual/occupational factors and WMSDs symptoms was determined using multiple logistic regression analysis. The Rapid Entire Body Assessment (REBA) method was applied to classify the risk level of working postures at the five workstations (shaping, trimming, glazing, painting, and burning).

RESULTS

The prevalence of musculoskeletal discomfort in at least one body region within the past 12 months was 69.1% among the participants: the neck (49.3%), lower back (43.8%), and shoulders (27.5%). Sex, work experience, daily working hours, perceived work fatigue, and workstation, were significantly associated with WMSDs symptoms in different body regions. The REBA indicated that 57.8% and 32.5% of the working postures were in the medium- and above high-risk levels, respectively.

CONCLUSION

The findings of this study showed a high occurrence of WMSDs symptoms among manual porcelain workers and suggested that both individual and work-related characteristics should be considered to improve occupational health. Furthermore, urgent ergonomic intervention is needed to avoid awkward working postures that cause WMSDs symptoms in porcelain workers, particularly at the shaping and burning workstations.

Return Strategy and Machine Learning Optimization of Tennis Sports Robot for Human Motion Recognition

At present, there are many kinds of intelligent training equipment in tennis sports, but they all need human control. If a single tennis player uses the robot to return the ball, it will save some human resources. This study aims to improve the recognition rate of tennis sports robots in the return action and the return strategy. The human-oriented motion recognition of the tennis sports robot is taken as the starting point to recognize and analyze the return action of the tennis sports robot. The OpenPose traversal dataset is used to recognize and extract human motion features of tennis sports robots under different classifications. According to the return characteristics of the tennis sports robot, the method of tennis return strategy based on the support vector machine (SVM) is established, and the SVM algorithm in machine learning is optimized. Finally, the return strategy of tennis sports robots under eight return actions is analyzed and studied. The results reveal that the tennis sports robot based on the SVM-Optimization (SVM-O) algorithm has the highest return recognition rate, and the average return recognition rate is 88.61%. The error rates of the backswing, forward swing, and volatilization are high in the return strategy of tennis sports robots. The preparation action, backswing, and volatilization can achieve more objective results in the analysis of the return strategy, which is more than 90%. With the increase of iteration times, the effect of the model simulation experiment based on SVM-O is the best. It suggests that the algorithm proposed has a reliable accuracy of the return strategy of tennis sports robots, which meets the research requirements. Human motion recognition is integrated with the return motion of tennis sports robots. The application of the SVM-O algorithm to the return action recognition of tennis sports robots has good practicability in the return action recognition of tennis sports robot and solves the problem that the optimization algorithm cannot be applied to the real-time requirements. It has important research significance for the application of an optimized SVM algorithm in sports action recognition.

Accuracy Assessment of Joint Angles Estimated from 2D and 3D Camera Measurements

To automatically evaluate the ergonomics of workers, 3D skeletons are needed. Most ergonomic assessment methods, like REBA, are based on the different 3D joint angles. Thanks to the huge amount of training data, 2D skeleton detectors have become very accurate. In this work, we test three methods to calculate 3D skeletons from 2D detections: using the depth from a single RealSense range camera, triangulating the joints using multiple cameras, and combining the triangulation of multiple camera pairs. We tested the methods using recordings of a person doing different assembly tasks. We compared the resulting joint angles to the ground truth of a VICON marker-based tracking system. The resulting RMS angle error for the triangulation methods is between 12° and 16°, showing that they are accurate enough to calculate a useful ergonomic score from.

Automatic real-time occupational posture evaluation and select corresponding ergonomic assessments

The objective is to develop a system to automatically select the corresponding assessment scales and calculate the score of the risk based on the joint angle information obtained from the imaged process (OpenPose) via image-based motion capture technology. Current occupational assessments, for example, REBA, RULA, and OWAS were used to evaluate the risk of musculoskeletal disorders. However, the assessment result would not be reported immediately. Introducing real-time occupational assessments in different working environments will be helpful for occupational injury prevention. In this study, the decision tree was developed to select the most appropriate assessment method according to the joint angles derived by OpenPose image process. Fifteen operation videos were tested and these videos can be classified into six types including maintenance, handling, assembly, cleaning, office work, and driving. The selected ergonomic assessment method by our developed decision tree in each condition are consistent with the recommendation of the Labour Research Institute. Moreover, the high-risk posture could be identified immediately and provide to the inspector for further evaluation on this posture rather than the whole operation period. This approach provides a quick inspection of the operation movements to prevent musculoskeletal injuries and enhances the application of the scale assessment method in different industrial environments.

Ergonomic Task Analysis for Prioritization of Work-Related Musculoskeletal Disorders among Mango-Harvesting Farmers

This paper proposes a mixed ergonomic tool analysis algorithm to prioritize work-related musculoskeletal problems. This study is a cross-sectional study assessing the prevalence of work-related musculoskeletal disorders (WMSDs) with associated risk factors among 14 male mango-harvesting farmers (all right-handed) with the mean age of 52.28 ± 7.75 years. Four tasks following mango-harvesting processes were analyzed: (1) mango harvesting, (2) mango transporting, (3) mango size sorting, and (4) mango weighing and transporting to the truck. The perceived physical exertion while working on a mango-harvesting farm was based on the Borg CR-10 with a modified Standardized Nordic Questionnaire. Physical risk level due to awkward posture was evaluated by the Rapid Upper Limb Assessment (RULA), and risk due to whole-body posture in association with the level of WMSDs risk was evaluated by the Rapid Entire Body Assessment (REBA) score sheets. The subjective feelings of fatigue and posture analysis were normalized and combined using the theorem of power superposition to establish the fatigue effective index (FEI) for determining priorities to solve ergonomics-based task problems. This study indicated clearly that WMSDs are highly prevalent in mango-harvesting farmers, whereas the highest prevalence of WMSDs was reported in the right shoulder, right upper arm and lower back. The result provided the FEI of mango-harvesting farmers, ranked as follows: (1) size-sorting task, (2) weight-lifting task, (3) harvesting task, and (4) transporting task. The authors concluded that mango size sorting should be the first task to be improved to resolve the muscle fatigue problems among male mango-harvesting farmers.

Prototype System for Measuring and Analyzing Movements of the Upper Limb for the Detection of Occupational Hazards

In the work environment, there are usually different pathologies that are related to Repetitive Efforts and Movements (REM) that tend to predominantly affect the upper limbs. To determine whether a worker is at risk of suffering some type of pathology, observation techniques are usually used by qualified technical personnel. In order to define from quantitative data if there is a risk of suffering a pathology due to movements and repetitive efforts in the upper limb, a prototype of a movement measurement system has been designed and manufactured. This system interferes minimally with the activity studied, maintaining a reduced cost of manufacture and use. The system allows the study of the movements made by the subject in the work environment by determining the origin of the Musculoskeletal Disorder (MSD) from the movements of the elbow and wrist, collecting data on the position and accelerations of the arm, forearm and hand, and taking into account the risk factors established for suffering from an MSD: high repetition of movements, the use of a high force in a repetitive manner, or the adoption of forced positions. The data obtained with this system can be analyzed by qualified personnel from tables, graphs, and 3D animations at the time of execution, or stored for later analysis.