Search Results (84)

Objectives: The aim of this study was to evaluate the impact of four working heights on lumbar biomechanics during wall construction tasks, focusing on work-related musculoskeletal disorders (WMSDs). Methods: Fifteen young male participants performed simulated mortar-spreading and bricklaying tasks while actual body movements were recorded using Inertial Measurement Unit (IMU) sensors. Muscle activities of the lumbar erector spinae (ES), quadratus lumborum (QL), multifidus (MF), gluteus maximus (GM), and iliopsoas (IL) were estimated using a 3D musculoskeletal (MSK) model and measured via surface electromyography (sEMG). The analysis of variance (ANOVA) test was conducted to identify the significant differences in muscle activities across four working heights (i.e., foot, knee, waist, and shoulder). Results: Findings showed that working at foot-level height resulted in the highest muscle activity (7.6% to 40.6% increase), particularly in the ES and QL muscles, indicating an increased risk of WMSDs. The activities of the ES, MF, and GM muscles were statistically significant across both tasks and all working heights (p < 0.01). Conclusions: Both MSK and sEMG analyses indicated significantly lower muscle activities at knee and waist heights, suggesting these as the best working positions (47 cm to 107 cm) for minimizing the risk of WMSDs. Conversely, working at foot and shoulder heights was identified as a significant risk factor for WMSDs. Additionally, the similar trends observed between MSK simulations and sEMG data suggest that MSK modeling can effectively substitute for sEMG in future studies. These findings provide valuable insights into ergonomic work positioning to reduce WMSD risks among wall construction workers. Full article

Work-related musculoskeletal disorders (WMSDs) are a leading concern in industrial ergonomics, often stemming from sustained non-neutral postures and repetitive tasks. This paper presents a vision-based framework for real-time, frame-level ergonomic risk classification using a lightweight Vision Transformer (ViT). The proposed system operates directly on raw RGB images without requiring skeleton reconstruction, joint angle estimation, or image segmentation. A single ViT model simultaneously classifies eight anatomical regions, enabling efficient multi-label posture assessment. Training is supervised using a multimodal dataset acquired from synchronized RGB video and full-body inertial motion capture, with ergonomic risk labels derived from RULA scores computed on joint kinematics. The system is validated on realistic, simulated industrial tasks that include common challenges such as occlusion and posture variability. Experimental results show that the ViT model achieves state-of-the-art performance, with F1-scores exceeding 0.99 and AUC values above 0.996 across all regions. Compared to previous CNN-based system, the proposed model improves classification accuracy and generalizability while reducing complexity and enabling real-time inference on edge devices. These findings demonstrate the model’s potential for unobtrusive, scalable ergonomic risk monitoring in real-world manufacturing environments. Full article

Work-related musculoskeletal disorders (WMSDs) are among the most prevalent occupational health issues, particularly affecting public transport drivers due to prolonged sitting, constrained postures, and poorly adaptable cabins. This study addresses the ergonomic risks associated with tram driving, aiming to evaluate biomechanical load and postural stress in relation to drivers’ anthropometric characteristics. A combined methodological approach was applied, integrating two standardized observational tools—RULA and REBA—with anthropometric modeling based on three representatives European morphotypes (SmallW, MidM, and TallM). ErgoFellow 3.0 software was used for digital posture evaluation, and lumbar moments at the L4/L5 vertebral level were calculated to estimate lumbar loading. The analysis was simulation-based, using digital human models, and no real subjects were involved. The results revealed uniform REBA (Rapid Entire Body Assessment) and RULA (Rapid Upper Limb Assessment) scores of 6 across all morphotypes, indicating moderate to high risk and a need for ergonomic intervention. Lumbar moments ranged from 51.35 Nm (SmallW) to 101.67 Nm (TallM), with the tallest model slightly exceeding the recommended ergonomic thresholds. These findings highlight a systemic mismatch between cabin design and user variability. In conclusion, ergonomic improvements such as adjustable seating, better control layout, and driver education are essential to reduce the risk of WMSDs. The study proposes a replicable methodology combining anthropometric, observational, and biomechanical tools for evaluating and improving transport workstation design. Full article

(1) Background: Work-related musculoskeletal disorders (WMSDs) are a major occupational health concern in the construction industry owing to the physically demanding tasks and poor ergonomic conditions present. Limited data exist on the prevalence of WMSDs and their ergonomic determinants among construction workers in India. This study investigated the prevalence of work-related musculoskeletal disorders (WMSDs) and the associated ergonomic risk factors among construction workers in India. (2) Methods: This cross-sectional study was conducted among 250 construction workers in India. Data on musculoskeletal disorders were collected using the Nordic Musculoskeletal Questionnaire (NMQ), and ergonomic risk was assessed using the Rapid Entire Body Assessment (REBA) tool. Logistic regression was used to identify factors associated with WMSDs, and Spearman’s correlation was used to assess the relationship between the REBA scores and the number of affected body regions. (3) Results: The prevalence of WMSDs was 60.4%. The most affected regions were the lower back (48%), knees (45%), shoulders (40%), and the neck (30%). The REBA scores indicated that 60% of the workers were at high or very high ergonomic risk and 30% at medium risk. Workers in the high/very high-risk category had significantly higher odds of developing WMSDs (OR = 4.5, 95% CI: 1.8–11.2, p = 0.001). Age above 40 years was also significantly associated with WMSDs (OR = 3.5, 95% CI: 1.2–10.2, p = 0.02). (4) Conclusions: This study demonstrated a high prevalence of WMSDs among Indian construction workers and established a clear association with poor ergonomic conditions. Targeted ergonomic interventions, including posture improvement, tool redesign, and safety training, are essential for reducing the risk of WMSDs in this population. Full article

Background: Work-related musculoskeletal disorders (WMSDs) are very prevalent among surgeons worldwide. The aim was to investigate the overall and body area WMSD prevalence (proportion of surgeons suffering from WMSD during their practice) by continent throughout a systematic review and meta-analysis. Methods: Three open databases were scanned without a date limit until 31 December 2024 to select relevant studies. The results were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results: Among the 20,486 items, 68 articles with a total of 17,188 surgeons were included, distributed as follows: 36 studies in America, 15 in Asia, 16 in Europe, and 1 in Oceania. Overall prevalence was 77.6% in Asia (95% CI: 67.3–87.9%), 73.1% in Europe (95% CI: 60.3–86.0%), and 62.8% in America (95% CI: 57.0−68.6%). The most exposed areas were the neck, upper and lower back, and shoulder, with prevalence ranging from 30 to 50%. The ranking differed according to continent. In America, neck/shoulder WMSD and overall prevalence were negatively correlated to years of experience (r² = 0.182 and r² = 0.240, p < 0.05) and to the number of cases treated per week prevalence (r² = 0.794, p < 0.05), respectively. A positive correlation was observed between the elbow WMSD prevalence and age (r² = 0.365, p < 0.05). In Europe, a negative correlation was highlighted between the overall WMSD prevalence and age (r² = 0.599, p < 0.05). Another positive correlation was identified between shoulder WMSD prevalence and years of experience (r² = 0.735, p < 0.05). Conclusions: To improve surgeons’ quality of work life, further research is needed to develop ergonomic programs, organizational work strategies, and assistive devices. Full article

Long-term manual material handling (MMH) work leads to the trend of the younger onset of work-related musculoskeletal disorders (WMSDs), with low back pain (LBP) being the most common, which causes great trouble for both society and patients. To effectively prevent LBP and provide support for workers engaged in MMH work, wearable lumbar assistive exoskeletons have played a key role in industrial scenarios. This paper divides wearable lumbar assistive exoskeletons into powered, unpowered, and quasi-passive types, systematically reviews the research status of each type of exoskeleton, and compares and discusses the key factors such as driving mode, mechanical structure, control strategy, performance evaluation, and human–machine interaction. It is found that many studies focus on the assistive performance, human–machine coupling coordination, and adaptability of wearable lumbar assistive exoskeletons. At the same time, the analysis results show that there are many types of performance evaluation indicators, but a unified and standardized evaluation method and system are still lacking. This paper analyzes current research findings, identifies existing issues, and provides recommendations for future research. This study provides a theoretical basis and design ideas for the development of wearable lumbar assistive exoskeleton systems. Full article

Work-related musculoskeletal disorders (WMSDs) are common in the agricultural industry due to repetitive tasks, like plant transplanting, which involve sustained bending, squatting, and awkward postures. This study uses wearable sensors to evaluate human biomechanics during simulated transplanting and assesses the potential of collaborative robot (cobot) assistance to reduce physical strain. Sixteen participants performed transplanting tasks under manual and cobot-assisted conditions. Kinematic and electromyographic (EMG) data were collected using Xsens motion capture and Trigno EMG systems. Cobot assistance significantly reduced the segment velocity and acceleration in key spinal regions (L5/S1, L1/T12, T1/C7), indicating lower dynamic spinal loading. It also altered muscle activation, decreasing biceps brachii use while increasing activation in stabilizing muscles such as the flexor carpi radialis, brachioradialis, and upper trapezius. Task duration decreased by 59.46%, suggesting improved efficiency. These findings highlight cobots’ potential to enhance ergonomic outcomes by encouraging controlled movements and reducing postural stress. However, the shift in muscle activation underscores the need for task-specific cobot tuning. This research supports the use of integrated IMU and EMG systems to inform cobot design and enable real-time biomechanical monitoring in labor-intensive settings. Full article

Aircraft mechanical maintenance involves high loads, repetitive movements, and awkward postures, significantly increasing the risk of work-related musculoskeletal disorders (WMSDs). Traditional static evaluation methods based on posture analysis fail to capture the complexity and dynamic nature of these tasks, limiting their applicability in maintenance settings. To address this limitation, this study introduces a novel quantitative WMSD risk assessment model that leverages 3D motion data collected through an optical motion capture system. The model evaluates dynamic human postures and employs an inverse trigonometric function algorithm to quantify the loading effects on working joints. Experimental validation was conducted through quasi-real-life scenarios to ensure the model’s reliability and applicability. The findings demonstrate that the proposed methodology provides both innovative and practical advantages, overcoming the constraints of conventional assessment techniques. Specifically, it enables precise quantification of physical task loads and enhances occupational injury risk assessments. The model is particularly valuable in physically demanding industries, such as aircraft maintenance, where accurate workload and fatigue monitoring are essential. By facilitating real-time ergonomic analysis, this approach allows managers to monitor worker health, optimize task schedules, and mitigate excessive fatigue and injury risks, ultimately improving both efficiency and workplace safety. Full article

(1) Background: The meatpacking industry in Brazil is vital for economic growth. It poses significant occupational health risks, particularly Work-Related Musculoskeletal Disorders (WMSDs). The present study analyzes the profile of slaughterhouse workers based on the governmental notification database. (2) Methods: The socio-demographic dataset (n = 820) was filled with worker data, and health profiles reported Repetitive Strain Injuries (RSI) and WMSDs from 2007 to 2019, available online by the Brazilian Health Service. The dataset was analyzed to identify patterns of injuries and contributing factors. Descriptive statistics outlined worker characteristics, while Analysis of Variance (ANOVA) explored associations between variables. (3) Results: The results revealed that 51.34% of workers were male, with most aged 30–45. Repetitive movements were reported by 89.63% of respondents, while 78.90% worked shifts exceeding six hours. Soft tissue disorders were the most prevalent diagnosis (40.97%). The majority (53.17%) experienced temporary disabilities, and 5.73% had permanent impairments. Key risk factors included inadequate breaks, stressful environments, and limited ergonomic adaptations. This study highlights the critical need for preventative measures, such as ergonomic interventions, adequate rest breaks, and employee education, to mitigate WMSDs. Future research should include ergonomic evaluations and consider broader organizational variables to enhance slaughterhouse occupational health and safety. Addressing these challenges is essential for improving worker welfare and maintaining industry productivity. Full article

Nurses are highly exposed to work-related musculoskeletal disorders (WMSDs). Several studies on this have been carried out in different Asian countries, but no synthesis was conducted. The aim of this study was to map the evidence of WMSDs among nurses in Asia. A systematic review and a meta-analysis with subgroups were performed during October 2024. Five open databases were scanned without a date limit. The article selection and data extraction processes were performed independently by two reviewers. The results report was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Among the 15,751 unique identified records, 40 studies were included, covering a total of 19,903 Asian nurses. A high heterogeneity (Cochran’s Q test and I² statistic) was evidenced between the studies. The meta-analysis polled an overall prevalence of 84.3% (95% CI: 81.1–87.4%). The lower back (58.4%, 95% CI: 52.9–63.8%), neck (45.7%, 95% CI: 38.1–53.2%) and shoulder (43.0%, 95% CI: 36.2–49.9%) were the three most exposed areas. Subgroup analyses have shown that the presence of WMSD is steadily increasing, and that years of practice reduce the exposure of the neck and shoulder, while the lower back becomes more exposed. Further efforts are needed to prevent WMSDs among Asian nurses, in order to improve their quality of life at work. Full article

Work-related musculoskeletal disorders (WMSDs) are a significant public health concern, particularly in professions requiring prolonged static postures. Objectives: This study examined the influence of demographic and occupational factors on the WMSD prevalence and provides evidence-based recommendations for workplace health improvements. Methods: A cross-sectional study (July–September 2024) surveyed 80 office employees in Varna, Bulgaria, using the Prevent 4 Work (P4Wq) questionnaire. ANOVA and t-tests assessed the WMSD prevalence across demographics, while Pearson’s correlations examined associations with age, BMI, and work experience. Data were analyzed in Jamovi v.2.6 (p < 0.05). Results: The sample (92.5% women, mean age 47.2 years) reported a high WMSD prevalence, with cervical pain (88.8%), lower back pain (83.8%), and shoulder pain (75.0%) being the most common. Work experience, age, and BMI were significantly correlated with WMSD severity, while gender showed no significant associations. Conclusions: WMSDs are highly prevalent among office employees, with lower back, neck, and shoulder pain being the most common complaints. Factors such as higher BMI, longer work experience, and increased workload are associated with greater symptom severity. These results emphasize the urgent need for targeted workplace interventions aimed at reducing ergonomic risks, improving posture, and enhancing employee well-being, ultimately fostering a healthier and more productive work environment. Full article

Background/Objectives: Work-related musculoskeletal disorders (WMSDs) have been found to be the leading factor in disabilities and absenteeism among workers. Despite their growing numbers, WMSD prevalence and risk factors among food delivery riders remain underexplored. Given the high WMSD prevalence among motorcyclists and the rising road traffic accidents (RTAs) among delivery riders in Malaysia, a cross-sectional study was conducted to identify the determinants of WMSDs among this neglected group. Methods: A self-reported questionnaire consisting of sociodemographic factors, work-related factors, and a validated Malay-translated Standardised Nordic Musculoskeletal Questionnaire (M-SNMQ) was used to evaluate the WMSD symptoms and related factors among 191 food delivery riders in Eastern Peninsular Malaysia. An ergonomic risk assessment using the Rapid Entire Body Assessment (REBA) method and whole-body vibration (WBV) measurement was also conducted to elicit the WMSD risk and vibration exposure among the riders. The determinants of WMSDs were analysed using multiple logistic regression using SPSS 20.4. Results: This study revealed a high prevalence of WMSDs (74.9%) among food delivery riders in Eastern Peninsular Malaysia. Furthermore, three factors were found to be significantly associated with WMSDs among riders, namely the increasing average working days (aOR = 2.00; 95% CI = 1.34, 2.98; p = 0.001), whole-body vibration (WBV) above the exposure action value (EAV) limit (aOR = 2.71; 95% CI = 1.13, 6.53; p = 0.026), and not exercising before work (aOR = 21.63; 95% CI = 7.45, 62.79; p < 0.001). Conclusions: Targeted interventions are essential to mitigate ergonomic risks and enhance rider safety. Occupational health policies should prioritise pre-work exercise and WBV exposure reduction to minimise musculoskeletal strain. Future longitudinal studies are needed to evaluate the long-term impact of these risks on riders’ health. Full article

Mechanization in timber harvesting has improved the comfort and safety of operator workstations. However, there is an imminent ergonomic risk in relation to the repetition of movements, which can cause musculoskeletal injuries. The aim of this study was to apply infrared thermography to identify musculoskeletal overload in the hand region of harvester operators. This study was conducted on wood harvesting of homogeneous Eucalyptus urophylla × Eucalyptus grandis stands using a forestry harvester tractor. Thermographic images were taken of seven operators at the beginning and end of the working day during six days of the shift. The maximum, average, and minimum temperatures were measured in 14 hand regions of interest (ROI), verifying the existence of a difference between the beginning and the end of work (p-value < 5%) by using the Mann–Whitney test. The operators presented hyperradiant temperature variation in the hand region after work, with a variation above 2.5 °C, indicating a high degree of abnormality. There was greater temperature variation in the right hand (3.7 °C) due to the greater concentration of commands on the machine’s right joystick. Infrared thermography has proven to be an important tool for functional imaging diagnosis, contributing to the reduction in risks of developing Repetitive Strain Injury/Work-Related Musculoskeletal Disorder (RSI-WMSD). Full article

Background: Occupational low back pain (LBP) is a pervasive health issue that significantly impacts productivity and contributes to work-related musculoskeletal disorders (WMSDs). Inadequate lifting postures are a primary, modifiable risk factor associated with LBP, making early detection of unsafe practices crucial to mitigating occupational injuries. Our study aims to address these limitations by developing a markerless, smartphone-based camera system integrated with a deep learning model capable of accurately classifying lifting postures. Material and Method: We recruited 50 healthy adults who participated in lifting tasks using correct and incorrect postures to build a robust dataset. Participants lifted boxes of varying sizes and weights while their movements were recorded from multiple angles and heights to ensure comprehensive data capture. We used the OpenPose algorithm to detect and extract key body points to calculate relevant biomechanical features. These extracted features served as inputs to a bidirectional long short-term memory (LSTM) model, which classified lifting postures into correct and incorrect categories. Results: Our model demonstrated high classification accuracy across all datasets, with accuracy rates of 96.9% for Tr, 95.6% for the testing set, and 94.4% for training. We observed that environmental factors, such as camera angle and height, slightly influenced the model’s accuracy, particularly in scenarios where the subject’s posture partially occluded key body points. Nonetheless, these variations were minor, confirming the robustness of our system across different conditions. Conclusions: This study demonstrates the feasibility and effectiveness of a smartphone camera and AI-based system for lifting posture classification. The system’s high accuracy, low setup cost, and ease of deployment make it a promising tool for enhancing workplace ergonomics. This approach highlights the potential of artificial intelligence to improve occupational safety and underscores the relevance of affordable, scalable solutions in the pursuit of healthier workplaces. Full article

Background/Objectives: Nurses are highly exposed to work-related musculoskeletal disorders (WMSDs). Global prevalence exceeds 80%, and several body areas are equally at risk. Numerous studies have assessed the exposure in many countries, but few have provided an overview by continent. The aim of this study was to propose a literature review and meta-analysis to investigate the overall and body area prevalence of WMSDs among nurses in Europe. Methods: A systematic review and a meta-analysis were conducted between September and November 2024. Five databases were scanned without a date limit: ScienceDirect, PubMed/Medline, Google Scholar, Science.gov, and Mendeley. The article selection, review, critical appraisal, and data extraction were performed by two authors independently. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was used for reporting the search results. Results: Among the 15,751 unique identified records, 12 studies met the inclusion criteria and were included for data extraction. The studies reported a total of 5153 nurses in Europe. High heterogeneity (Cochran’s Q test and I² statistic) was observed between studies. The results of the meta-analysis based on the random-effects model showed an overall prevalence of 87.8% (95% CI: 83.3–92.2%). The highest prevalence was found for lower back 61.4% (95% CI: 55.1–67.7%), neck 49.9% (95% CI: 42.9–56.8%), and upper back 46.3% (95% CI: 42.4–50.2%). Conclusions: Nurses are highly exposed to WMSDs, with a prevalence of over 30% for three-quarters of body areas. Due to the multitude of risk factors associated with nurses’ work, the development of recommendations is a complex multifactorial issue, requiring the exploration of different strategies to reduce the prevalence of MSDs in nurses. Full article