Search Results (45)

In the field of high-altitude operations, the frequent occurrence of fall accidents is usually closely related to safety measures such as the incorrect use of safety locks and the wrong installation of safety belts. At present, the manual inspection method cannot achieve real-time monitoring of the safety status of the operators and is prone to serious consequences due to human negligence. This paper designs a new type of high-altitude operation safety device based on the STM32F103 microcontroller. This device integrates ultra-wideband (UWB) ranging technology, thin-film piezoresistive stress sensors, Beidou positioning, intelligent voice alarm, and intelligent safety lock. By fusing five modes, it realizes the functions of safety status detection and precise positioning. It can provide precise geographical coordinate positioning and vertical ground distance for the workers, ensuring the safety and standardization of the operation process. This safety device adopts multi-modal fusion high-altitude operation safety monitoring technology. The UWB module adopts a bidirectional ranging algorithm to achieve centimeter-level ranging accuracy. It can accurately determine dangerous heights of 2 m or more even in non-line-of-sight environments. The vertical ranging upper limit can reach 50 m, which can meet the maintenance height requirements of most transmission and distribution line towers. It uses a silicon carbide MEMS piezoresistive sensor innovatively, which is sensitive to stress detection and resistant to high temperatures and radiation. It builds a Beidou and Bluetooth cooperative positioning system, which can achieve centimeter-level positioning accuracy and an identification accuracy rate of over 99%. It can maintain meter-level positioning accuracy of geographical coordinates in complex environments. The development of this safety device can build a comprehensive and intelligent safety protection barrier for workers engaged in high-altitude operations. Full article

Background/Objectives: The aging of workers with intellectual and developmental disabilities is an emerging reality attributed to the rise in life expectancy and improved labor market access. In this study, “workers” is used as an inclusive, neutral term covering all individuals engaged in paid labor—whether employees, self-employed, freelancers, or those performing manual or non-manual tasks. It encompasses every form of work. It is crucial to comprehend the reality of aging workers from the perspectives of the primary individuals involved: the workers, their families, and supporting professionals. Methods: A qualitative study was developed, involving 12 focus groups and 107 participants, using NVivo 12 Pro for analysis; we used a phenomenological methodology and grounded theory. Results: A set of concrete needs was highlighted: among them, 33 were related to declining work ability due to aging and disability (WADAD), and 30 to transition to retirement. These needs were grouped into categories: workplace accommodations, coordination and collaboration, personal and family support, counseling and training, and other types of needs. Conclusions: This study establishes an empirical basis tailored to the needs of this group, enabling the development of prevention and intervention protocols that address WADAD and the transition to retirement. Full article

Shift workers are at increased risk of insomnia. The standard treatment (cognitive behavioral therapy for insomnia) poses significant challenges for this demographic due to irregular work and sleep schedules. New approaches are still considered insufficient due to high attrition or insufficient effectiveness. Our preliminary study identified sleep-relevant state and trait factors (see secondary outcomes) for incorporation into an innovative manual that addresses sleep in an implicit manner. The objective was to reduce the focus on insomnia and to replace regularity-based interventions. With a sample of 55 insomniacs (67.74% male, mean age 41.62 years), standard and customized treatments were compared using pre-treatment, post-treatment, and three-month follow-up measurements (RCT, self-assessment data). Our linear mixed models revealed the main significant effects of the measurement point for the primary (insomnia severity, sleep quality, sleep onset latency, total sleep time, daytime sleepiness) and the secondary outcomes (selection: anxiety/depression, dysfunctional beliefs, arousal, emotional stability, concern). No main effects of the condition or interaction effects were identified. Non-inferiority and equivalence tests demonstrated that the customized treatment is equivalent to standard therapy, which is a favorable outcome in light of the implicit approach. Consequently, this innovative approach warrants further exploration, incorporating the present results. Full article

Railroad construction sites are high-risk environments where monitoring personnel safety is critical for preventing accidents and enhancing construction efficiency. Traditional manual monitoring and image processing methods exhibit deficiencies in real-time performance and accuracy. This paper proposes a railway worker detection method based on improved support vector machines (ISVM), while using non-local mean noise reduction and histogram equalisation pre-processing techniques to optimise image quality to improve detection efficiency and accuracy. Multiscale features are then extracted with Inception v3 and combined with principal component analysis (PCA) for dimensionality reduction. Finally, an SVM classification algorithm is employed for personnel detection. To process small sample categories, data enhancement techniques (e.g., random flip and rotation) and K-fold cross-validation are applied to optimize the model parameters. The experimental results demonstrate that the ISVM method significantly improves accuracy and real-time performance compared to traditional detection methods and single deep learning models. This method provides technical support for railroad construction safety monitoring and effectively addresses personnel detection tasks in complex construction environments. Full article

Timber harvesting operations, including manual and motor-manual activities, require workers who are in good health to be able to work effectively. The aim of our paper was to introduce a simplified index methodology for workload assessment. Generally available wearable technology, namely Garmin, Biostrap, and Whoop devices, were used. The dependence of the heart rate (HR) on physical workload was examined to calculate the Heart Rate Index. The case study was performed with several variations of chainsaw devices cutting the poplar wood. It was proved that the use of a heavier work tool, MS 500i/90 cm 9.3 kg, contributes both to the creation of a non-ergonomic working position and to an increase in the energy required to perform work, which was represented by an increase in heart rate. With a lighter work tool and a shorter cutting blade, both a decrease in heart rate and a reduction in the working time performed in a non-ergonomic position were achieved. The results can be used in common practice for workers’ self-assessment to increase safety and health protection at work or work productivity, not only in forestry-related professions. Full article

Objectives: The association between food insecurity and metabolically unhealthy obesity (MUO) in the population with obesity is unclear. We evaluated factors associated with MUO and the relationship between food insecurity and MUO in individuals with obesity. Methods: We analyzed data from 5191 adults with obesity (body mass index ≥ 25 kg/m²) from the 8th Korea National Health and Nutrition Examination Survey 2019–2021. MUO was defined when participants with obesity had any of the following: (1) triglycerides ≥ 150 mg/dL, (2) High-density lipoprotein-cholesterol < 40 mg/dL (men), <50 mg/dL (women), (3) systolic blood pressure ≥ 135 mmHg, diastolic blood pressure ≥85 mmHg or on treatment for hypertension, (4) fasting glucose ≥ 100 mg/dL, or on treatment for diabetes. The odds ratios (ORs) and 95% confidence intervals (CIs) for MUO according to food security status, sociodemographic characteristics, and lifestyle factors were calculated using multivariate logistic regression analysis. Results: The prevalence of MUO and metabolically healthy obesity (MHO) among the participants was 85.4% and 14.6%, respectively. In the multivariate model, the OR (95% CIs) for MUO in the food insecurity group was 1.87 (1.03–3.43). The odds for MUO were higher among participants with older age, higher BMI, <12 years of education, lower fat intake, non-manual work, and moderated and low physical activity than among their counterparts. Conclusions: Food insecurity, older age, higher BMI, lower educational level, lower fat intake, non-manual workers, and lower physical activity were associated with MUO. Therefore, targeted interventions and policies are needed for vulnerable groups. Full article

Introduction: Cardiopulmonary resuscitation with mechanical devices (MCPR) was developed to provide high-quality cardiopulmonary resuscitation (CPR) for patients with cardiac arrest. However, the effect of this procedure on treatment outcomes remains controversial. Nevertheless, during the coronavirus disease-19 (COVID-19) pandemic, in-hospital MCPR gained attention, owing to its advantages such as saving medical staff and preventing infection. This study compared the treatment outcomes of in-hospital MCPR and manual CPR for out-of-hospital cardiac arrest (OHCA) patients during the COVID-19 pandemic. Materials and Methods: This retrospective nationwide population-based study was conducted in South Korea. Data were collected from the Out-of-Hospital Cardiac Arrest surveillance database managed by the Korea Disease Control and Prevention Agency. We included adult OHCA patients transported by emergency medical services from 2016 to 2021. The study compared outcomes during the COVID-19 pandemic years (2020–2021) with the preceding non-pandemic years (2018–2019). The primary outcome was survival to hospital discharge, and the secondary outcomes were good neurological outcome and sustained return of spontaneous circulation (ROSC). Results: The entire study included 72,050 patients with OHCA and, in the multivariable analyses, MCPR was associated with lower survival rates compared to manual CPR (AOR 0.63; 95% CI 0.51–0.77; p < 0.001). Interestingly, during the COVID-19 pandemic, while MCPR use increased, the survival rate did not differ significantly between the MCPR and manual-CPR groups. Conclusion: Our study findings suggest that while MCPR may offer potential benefits, such as decreased infection risk for healthcare workers, it did not demonstrate superior outcomes compared to manual CPR in our study population. Full article

Musculoskeletal disorders are prevalent among agricultural workers, increasing the risk of work-related diseases due to manual labor, repetitive tasks, and prolonged postures. This study evaluates the association between physical behaviors during work and non-work, measured objectively, and musculoskeletal pain in the neck, upper back, and lower back in viticulture workers. A cross-sectional quantitative study was conducted with 75 viticulturists of both sexes aged 18 years or older. An accelerometer measured physical behaviors (lying down, sitting, standing, moving, walking, and sleeping) during work and non-work periods. Pain intensity was quantified using a 0–10 scale and categorized as “Low” and “High” pain intensity. Binary logistic regression tested the association between pain and time spent on physical behaviors. Results indicated a high prevalence of pain: 46.7% cervical, 52% upper back, and 60% lower back. Standing was the most common behavior during work, while lying and sitting were predominant during non-work. An increased sleeping time was associated with a decreased probability of experiencing high-intensity neck pain. Increased time spent lying down during non-work hours was associated with an increased probability of experiencing high-intensity upper back pain. No physical behavior was associated with high-intensity lower back pain. In conclusion, sedentary behaviors worsen upper back pain, and sleep reduces neck pain in viticulture workers. Full article

Background: Many COVID-19 survivors still experience long-term effects of an acute infection, most often characterised by neurological, cognitive and psychiatric sequelae. The treatment of this condition is challenging, and many hypotheses have been proposed. Non-invasive vagus nerve stimulation using slow-paced breathing (SPB) could stimulate both central nervous system areas and parasympathetic autonomic pathways, leading to neuromodulation and a reduction in inflammation. The aim of the present study was to evaluate physical, cognitive, emotional symptoms, executive functions and autonomic cardiac modulation after one month of at-home slow breathing intervention. Methods: 6655 healthcare workers (HCWs) were contacted via a company email in November 2022, of which N = 58 HCWs were enrolled as long COVID (cases) and N = 53 HCWs as controls. A baseline comparison of the two groups was performed. Subsequently each case was instructed on how to perform a resonant SPB using visual heart rate variability (HRV) biofeedback. They were then given a mobile video tutorial breathing protocol and asked to perform it three times a day (morning, early afternoon and before sleep). N = 33 cases completed the FU. At T0 and T1, each subject underwent COVID-related, psychosomatic and dysfunctional breathing questionnaires coupled with heart rate variability and manual dexterity assessments. Results: After one month of home intervention, an overall improvement in long-COVID symptoms was observed: confusion/cognitive impairment, chest pain, asthenia, headache and dizziness decreased significantly, while only a small increase in manual dexterity was found, and no relevant changes in cardiac parasympathetic modulation were observed. Full article

Agriculture is being transformed through automation and robotics to improve efficiency and reduce production costs. However, this transformation poses risks of job loss, particularly for low-skilled workers, as automation decreases the need for human labor. To adapt, the workforce must acquire new qualifications to collaborate with automated systems or shift to roles that leverage their unique human abilities. In this study, 15 agricultural occupations were methodically mapped in a cognitive/manual versus routine/non-routine two-dimensional space. Subsequently, each occupation’s susceptibility to robotization was assessed based on the readiness level of existing technologies that can automate specific tasks and the relative importance of these tasks in the occupation’s execution. The qualifications required for occupations less impacted by robotization were summarized, detailing the specific knowledge, skills, and work styles required to effectively integrate the emerging technologies. It was deduced that occupations involving primary manual routine tasks exhibited the highest susceptibility rate, whereas occupations with non-routine tasks showed lower susceptibility. To thrive in this evolving landscape, a strategic combination of STEM (science, technology, engineering, and mathematics) skills with essential management, soft skills, and interdisciplinary competences is imperative. Finally, this research stresses the importance of strategic preparation by policymakers and educational systems to cultivate key competencies, including digital literacy, that foster resilience, inclusivity, and sustainability in the sector. Full article

Despite advancements in technology, safety equipment, and training within the construction industry over recent decades, the prevalence of fatal and nonfatal injuries and accidents remains a significant concern among construction workers. Hard hats and safety vests are crucial safety gear known to mitigate severe head trauma and other injuries. However, adherence to safety protocols, including the use of such gear, is often inadequate, posing potential risks to workers. Moreover, current manual safety monitoring systems are laborious and time-consuming. To address these challenges and enhance workplace safety, there is a pressing need to automate safety monitoring processes economically, with reduced processing times. This research proposes a deep learning-based pipeline for real-time identification of non-compliance with wearing hard hats and safety vests, enabling safety officers to preempt hazards and mitigate risks at construction sites. We evaluate various neural networks for edge deployment and find that the Single Shot Multibox Detector (SSD) MobileNet V2 model excels in computational efficiency, making it particularly suitable for this application-oriented task. The experiments and comparative analyses demonstrate the pipeline’s effectiveness in accurately identifying instances of non-compliance across different scenarios, underscoring its potential for improving safety outcomes. Full article

Physical activity (PA) and sedentary behavior (SB) changed during the COVID-19 pandemic; hence, this study examined PA and SB at four time points between December 2019 and December 2022. The participants’ PA decreased during the pandemic and did not recover afterwards. Among women, PA increased slightly in 2022 but not at all in men. From 2019 to 2020, SB increased and then decreased to near the pre-pandemic level in both sexes. Regarding age, PA decreased in the oldest age group (65–79 years) across all time points, while SB increased in all age groups during 2019–2020 and then returned close to pre-pandemic levels among the two middle age groups (30–64 years), but not among the youngest and oldest groups. Considering occupation, PA decreased from 2020 to December 2022 among retired and “other” participants, while SB decreased among nonmanual workers and retired participants. The regression models associated better self-reported health, male sex, and those born overseas with higher PA. Higher age, better self-reported health, poor education, and later survey time points were associated with lower SB. These findings highlight the need to return PA and SB to at least pre-pandemic levels and that subgroups may need different interventions. Full article

Personal protective equipment (PPE) can increase the safety of the worker for sure by reducing the probability and severity of injury or fatal incidents at construction, chemical, and hazardous sites. PPE is widely required to offer a satisfiable safety level not only for protection against the accidents at the aforementioned sites but also for chemical hazards. However, for several reasons or negligence, workers may not commit to and comply with the regulations of wearing the equipment, occasionally. Since manual monitoring is laborious and erroneous, the situation demands the development of intelligent monitoring systems to offer the automated real-time and accurate detection of PPE compliance. As a solution, in this study, Deep Learning and Computer Vision are investigated to offer near real-time and accurate PPE detection. The four colored hardhats, vest, safety glass (CHVG) dataset was utilized to train and evaluate the performance of the proposed model. It is noteworthy that the solution can detect eight variate classes of the PPE, namely red, blue, white, yellow helmets, head, person, vest, and glass. A two-stage detector based on the Fast-Region-based Convolutional Neural Network (RCNN) was trained on 1699 annotated images. The proposed model accomplished an acceptable mean average precision (mAP) of 96% in contrast to the state-of-the-art studies in literature. The proposed study is a potential contribution towards the avoidance and prevention of fatal/non-fatal industrial incidents by means of PPE detection in real-time. Full article

The harvest phase plays an important role in the whole process of production of tomato fruit. Therefore, it is necessary to ensure a technological process that will not damage biological materials. The harvest phase plays an important role in the whole process. Many growers use special machines for harvesting, but there are fruits and vegetables that should be harvested manually to avoid damaging the surface or parenchyma tissue of the harvested objects. In addition to maintaining the quality of biological materials, work comfort, and ergonomic conditions for pickers should be ensured because inadequate working conditions do not encourage employees to undertake manual work in horticulture. Therefore, there have been shortages of workers on Polish plantations in the past year. Based on manual tomato harvesting, the authors conducted a matched qualitative research study on biological materials and work ergonomics. For this purpose, the Grip System was used to investigate tomato quality by assessing the impact of picking hand pressure (in three different picker’s body positions) on the harvested objects. Simultaneously, for the picker’s ergonomic analysis, a non-invasive surface electromyography method was used to precisely measure changes in muscle motor unit action in the picker’s wrist and lumbar spine while in three characteristic picker’s positions. The tests found that the poorest body position was when the body was deeply inclined and simultaneously twisted. No significant effect was shown of the body position of the tomato picker on the deterioration of the picked fruit quality. However, body positions significantly affect the level of physical load and work comfort. Full article

The article concerns the possibility of the automation and robotization of the process of deburring jet engine components. The paper presents the construction of a laboratory stand enabling the automation of selected production operations of typical low-pressure turbine blades. The work identifies important parameters and results of the technological process related to the removal of burrs that affect the exactness of the process. The results of the analysis of the impact of individual process parameters on the magnitude of forces and moments occurring during deburring were carried out and presented. The results of initial and detailed tests were presented. Based on the results obtained, it was noticed that doubling the rotational speed of the brush results in a linear increase in torque and an increase in the engagement of the detail in the disc brush, leading to a non-linear increase in torque. It has also been shown that with tool wear, the value of the torque generated by the rotating tool decreases. Based on the results of a comparison of manual and automated process and histogram analysis, results from an automated stand are centered more correctly inside of the required radius range. This means that the repeatability of the process is higher for an automated test stand, which is one of the key aspects of large-scale aviation component manufacturing. Additionally, it was confirmed by visual inspection that all burs had been removed correctly—the deburring operation for all tested work pieces was successful. Based on the results obtained, it was proven that introduction of an automated stand can improve working conditions (by the elimination of the progressive fatigue of employees and the possibility for injury) and allows for the elimination of the negative impact of the machining process on workers. Further areas in which the optimization of the process parameters of the edge deburring can be developed in order to reduce unit costs have also been indicated. Full article