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Construction organizations must maintain a productive workforce without sacrificing their health and safety. The global construction sector loses billions of dollars yearly to poor health and safety practices. This study aims to investigate benefits derivable from using wearable technologies to improve construction health and safety. The study also reports the challenges associated with adopting wearable technologies.

The study adopted a quantitative design, administering close-ended questions to professionals in the Nigerian construction industry. The research data were analysed using descriptive and inferential statistics.

The study found that the critical areas construction organizations can benefit from using WSDs include slips and trips, sensing environmental concerns, collision avoidance, falling from a high level and electrocution. However, key barriers preventing the organizations from adopting wearable technologies are related to cost, technology and human factors.

The time and cost lost to H&S incidents in the Nigerian construction sector can be reduced by implementing the report of this study.

Studies on WSDs have continued to increase in developed countries, but Nigeria is yet to experience a leap in the research area. This study provides insights into the Nigerian reality to provide directions for practice and theory.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

The purpose of this paper is to review the prevalence and risk factors of work-related musculoskeletal disorders (WMSDs) among healthcare workers (HCWs) in order to ascertain the occupation with the highest susceptibility to WMSD in the health sector. This paper will also review the effective interventions which have been used to prevent WMSDs among HCWs.

This study is a literature review of 11 papers related to the prevalence and risk factors of WMSDs and 12 papers about the interventions being used to prevent WMSDs among HCWs. The papers were retrieved from respectable databases such as PubMed, Science Direct, Google Scholar and E-Thesis.

Nurses belong to the major group of HCWs who had the highest prevalence of WMSDs compared with other health professionals and other hospital workers. Although there are several interventions being commonly used to prevent WMSD risk factors, some interventions were unsuccessful in the prevention of WMSDs in healthcare tasks. Therefore, it is necessary that future research focuses on the tasks of HCWs that are WMSD risk factors and tries to innovate or redesign ergonomic workstations to prevent those risk factors.

The expected benefit of this study is to motivate ergonomists to provide appropriate and innovative interventions to ensure health and safety for nurses and other HCWs.

To compare the electromyography (EMG) features during physical and imagined standing up in healthy young adults.

Twenty-two participants (ages ranged from 20–29 years old) were recruited to participate in this study. Electrodes were attached to the rectus femoris, biceps femoris, tibialis anterior and the medial gastrocnemius muscles of both sides to monitor the EMG features during physical and imagined standing up. The %maximal voluntary contraction (%MVC), onset and duration were calculated.

The onset and duration of each muscle of both sides had no statistically significant differences between physical and imagined standing up (p > 0.05). The %MVC of all four muscles during physical standing up was statistically significantly higher than during imagined standing up (p < 0.05) on both sides. Moreover, the tibialis anterior muscle of both sides showed a statistically significant contraction before the other muscles (p < 0.05) during physical and imagined standing up.

Muscles can be activated during imagined movement, and the patterns of muscle activity during physical and imagined standing up were similar. Imagined movement may be used in rehabilitation as an alternative or additional technique combined with other techniques to enhance the STS skill.

The purpose of this study is to describe the design and validation of a three-dimensional (3D)-printed phantom of a uterus to support the development of uterine balloon tamponade devices conceived to stop post-partum haemorrhages (PPHs).

The phantom 3D model is generated by analysing the main requirements for validating uterine balloon tamponade devices. A modular approach is implemented to guarantee that the phantom allows testing these devices under multiple working conditions. Once finalised the design, the phantom effectiveness is validated experimentally.

The modular phantom allows performing the required measurements for testing the performance of devices designed to stop PPH.

PPH is the leading obstetric cause of maternal death worldwide, mainly in low- and middle-income countries. The proposed phantom could speed up and optimise the design and validation of devices for PPH treatment, reducing the maternal mortality ratio.

To the best of the authors’ knowledge, the 3D-printed phantom represents the first example of a modular, flexible and transparent uterus model. It can be used to validate and perform usability tests of medical devices.

The purpose of this paper is to expand current knowledge about the recent trend of wearable technology to assess both its potential in the work environment and the challenges concerning the utilisation of wearables in the workplace.

After establishing exclusion and inclusion criteria, an independent systematic search of the ACM Digital Library, IEEE Xplore, ScienceDirect and Web of Science databases for relevant studies was performed. Out of a total of 359 articles, 34 met the selection criteria.

This review identifies 23 categories of wearable devices. Further categorisation of the devices based on their utilisation shows they can be used in the work environment for activities including monitoring, augmenting, assisting, delivering and tracking. The review reveals that wearable technology has the potential to increase work efficiency among employees, improve workers’ physical well-being and reduce work-related injuries. However, the review also reveals that technological, social, policy and economic challenges related to the use of wearable devices remain.

Many studies have investigated the benefits of wearable devices for personal use, but information about the use of wearables in the work environment is limited. Further research is required in the fields of technology, social challenges, organisation strategies, policies and economics to enhance the adoption rate of wearable devices in work environments.

Previous studies indicate that occupational stress and injuries are detrimental to employees’ health; this paper analyses the use of wearable devices as an intervention method to monitor or prevent these problems. Introducing a categorisation framework during implementation may help identify which types of device categories are suitable and could be beneficial for specific utilisation purposes, facilitating the adoption of wearable devices in the workplace.

Most support surfaces in comfort applications and sporting equipment are made from pressure-relieving foam such as viscoelastic polyurethane. However, for some users, foam is not the best material as it acts as a thermal insulator and it may not offer adequate postural support. The additive manufacturing of such surfaces and equipment may alleviate these issues, but material and design investigation is needed to optimize the printing parameters for use in pressure relief applications. This study aims to assess the ability of an additive manufactured flexible polymer to perform similarly to a viscoelastic foam for use in comfort applications.

Three-dimensional (3D) printed samples of thermoplastic polyurethane (TPU) are tested in uniaxial compression with four different infill patterns and varying infill percentage. The behaviours of the samples are compared to a viscoelastic polyurethane foam used in various comfort applications.

Results indicate that TPU experiences an increase in strength with an increasing infill percentage. Findings from the study suggest that infill pattern impacts the compressive response of 3D printed material, with two-dimensional patterns inducing an elasto-plastic buckling of the cell walls in TPU depending on infill percentage. Such buckling may not be a beneficial property for comfort applications. Based on the results, the authors suggest printing from TPU with a low-density 3D infill, such as 5% gyroid.

Several common infill patterns are characterised in compression in this work, suggesting the importance of infill choices when 3D printing end-use products and design for manufacturing.

Exoskeletons are mechanical structures that humans can wear to increase their strength and endurance. The purpose of this paper is to explain how exoskeletons can be used to improve performance across five phases of manufacturing.

Multivocal literature review, encompassing scientific literature and the grey literature of online reports, etc., to inform comprehensive, comparative and critical analyses of the potential of exoskeletons to improve manufacturing performance.

There are at least eight different types of exoskeletons that can be used to improve human strength and endurance in manual work during different phases of production. However, exoskeletons can have the unintended negative consequence of reducing human flexibility leading to new sources of musculoskeletal disorders (MSD) and accidents.

Findings are relevant to function allocation research concerned with manual production work. In particular, exoskeletons could exacerbate the traditional trade-off between human flexibility and robot consistency by making human workers less flexible.

The introduction of exoskeletons requires careful health and safety planning if exoskeletons are to improve human strength and endurance without introducing new sources of MSD and accidents.

The originality of this paper is that it provides detailed information about a new manufacturing technology: exoskeletons. The value of this paper is that it provides information that is comprehensive, comparative and critical about exoskeletons as a potential alternative to robotics across five phases of manufacturing.

In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system.

To achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop.

The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems.

The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.