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Low back disorder is one of the most prevalent and costly injuries in the construction industry. Back-support exoskeletons are increasingly perceived as promising solutions. However, the intended benefits of exoskeletons may not be realized if intention-to-use the device is low. Social influence could increase intention-to-use exoskeletons. This study aims to evaluate the impact of social influence on construction workers' intention-to-use back-support exoskeletons.

A field study involving 37 construction workers was conducted, with workers who used exoskeleton for one week, and their peers and supervisors. Data were collected using questionnaires and semi-structured interviews, and analyzed using descriptive statistics and thematic analysis, respectively.

The workers felt that the exoskeleton is easy to use and the functions are well integrated. Workers' intention-to-use exoskeleton was mainly influenced by employers providing and requiring the use of the device. The attitude of the workers and the perception of peers and supervisors did not have a significant impact on workers' intention-to-use exoskeleton, whereas the subjective norm of construction workers had a positive impact on the intention-to-use exoskeletons.

The study involved only 37 workers, including 15 workers who used the exoskeleton, and 14 peers and 8 supervisors of the workers.

This study contributes to existing knowledge on the influence of social influence on intention-to-use exoskeletons. The study also highlights how exoskeleton designs and the construction workplace can influence behavioral intention-to-use exoskeletons.

Recognizing construction workers' activities is critical for on-site performance and safety management. Thus, this study presents the potential of automatically recognizing construction workers' actions from activations of the erector spinae muscles.

A lab study was conducted wherein the participants (n = 10) performed rebar task, which involved placing and tying subtasks, with and without a wearable robot (exoskeleton). Trunk muscle activations for both conditions were trained with nine well-established supervised machine learning algorithms. Hold-out validation was carried out, and the performance of the models was evaluated using accuracy, precision, recall and F1 score.

Results indicate that classification models performed well for both experimental conditions with support vector machine, achieving the highest accuracy of 83.8% for the “exoskeleton” condition and 74.1% for the “without exoskeleton” condition.

The study paves the way for the development of smart wearable robotic technology which can augment itself based on the tasks performed by the construction workers.

This study contributes to the research on construction workers' action recognition using trunk muscle activity. Most of the human actions are largely performed with hands, and the advancements in ergonomic research have provided evidence for relationship between trunk muscles and the movements of hands. This relationship has not been explored for action recognition of construction workers, which is a gap in literature that this study attempts to address.

Exoskeletons are moving into industries with the potential to reduce muscle strains and prevent occupational injuries. Although exoskeletons have been designed and tested in laboratory settings, rare empirical studies of their application in construction have been reported. Therefore, the purpose of this study is on in a real-life setting testing the applicability of adopting exoskeletons in the construction industry.

A feasibility study of exoskeletons in construction is conducted by testing a passive exoskeleton, designed for shoulder support. Five bricklayers tested in a two-month period the exoskeleton, each wearing it for a three-day period while carrying out normal work activities. Test data in terms of interviews were collected and analyzed using qualitative content analysis.

The application of exoskeletons in construction revealed several limitations, where the two primary ones are the exoskeleton is not designed while considering the tasks of a bricklayer causing several challenges and the exoskeleton only supports a single upward motion while limiting other movements and even counteracted when a downward movement was necessary.

The identified challenges could easily have been revealed by coupling the design and testing of exoskeletons to actual application. Thus, the design approach needs to be reversed. Instead of designing an exoskeleton to support a specific body part or motion and then identifying where it is applicable, it should target specific industries and focus on the actual work and movements and the necessary support. As part of the change, the design metrics should be reevaluated to reflect the work to support.

Concrete workers perform physically demanding work in awkward postures, exposing their backs to musculoskeletal disorders. Back-support exoskeletons are promising ergonomic interventions designed to reduce the risks of back disorders. However, the suitability of exoskeletons for enhancing performance of concrete workers has not been largely explored. This study aims to assess a passive back-support exoskeleton for concrete work in terms of the impact on the body, usability and benefits of the exoskeleton, and potential design modifications.

Concrete workers performed work with a passive back-support exoskeleton. Subjective and qualitative measures were employed to capture their perception of the exoskeleton, at the middle and end of the work, in terms of discomfort to their body parts, ease of use, comfort, performance and safety of the exoskeleton, and their experience using the exoskeleton. These were analyzed using descriptive statistics and thematic analysis.

The exoskeleton reduced stress on the lower back but caused discomfort to other body parts. Significant correlations were observed between perceived discomfort and usability measures. Design modifications are needed to improve the compatibility of the exoskeleton with the existing safety gears, reduce discomfort at chest and thigh, and improve ease of use of the exoskeleton.

The study was conducted with eight concrete workers who used the exoskeleton for four hours.

This study contributes to existing knowledge on human-wearable robot interaction and provides suggestions for adapting exoskeleton designs for construction work.

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.

Low back disorders are more predominant among construction trade workers than their counterparts in other industry sectors. Floor layers are among the top artisans that are severely affected by low back disorders. Exoskeletons are increasingly being perceived as ergonomic solutions. This study aims to compare the efficacy of passive and active back-support exoskeletons by measuring range of motion, perceived discomfort, usability, perceived rate of exertion and cognitive load during a simulated flooring task experiment.

In this study eight participants were engaged in a repetitive timber flooring task performed with passive and active back-support exoskeletons. Subjective and objective data were collected to assess the risks associated with using both exoskeletons. Descriptive statistics were used for analysis. Scheirer-Ray-Hare test and Wilcoxon signed-rank test were adopted to compare the exoskeleton conditions.

The results show no significant differences in the range of motion (except for a lifting cycle), perceived level of discomfort and perceived level of exertion between the two exoskeletons. Significant difference in overall cognitive load was observed. The usability results show that the active back-support exoskeleton made task execution easier with less restriction on movement.

The flooring task is simulated in a laboratory environment with only eight male participants.

This study contributes to the scarce body of knowledge on the usage comparison of passive and active exoskeletons for construction work.

The importance of robotics and automation (R&A) in delivering a safe built environment cannot be overemphasised. This is because R&A systems can execute a hazardous job function that the construction workforce may not execute. Based on this knowledge, this study aims to present the result of an assessment of the impediments to the deployment of R&A for a safe and healthy construction environment.

This study adopted a post-positivist philosophical stance, using a quantitative research approach and a questionnaire administered to construction professionals in South Africa. The data gathered were analysed using frequency, percentage, mean item score, Kruskal–Wallis H-test, exploratory factor analysis and partial least square structural equation modelling (SEM).

This study revealed that the impediments to the deployment of R&A could be grouped into: industry, technology, human and cost-related factors. However, SEM assessment showed that only the industry, human and cost-related factors would significantly impact attaining specific health and safety-related outcomes.

The findings offer valuable benefits to construction organisations as the careful understanding of the identified impeding factors can help lead to better deployment of R&A and the attainment of its inherent safety benefits.

This study attempts to fill the gap in the shortage of literature exploring the deployment of R&A for a safe construction environment, particularly in developing countries like South Africa, where such studies are non-existent. This paper, therefore, offers a theoretical backdrop for future works on R&A deployment, particularly in developing countries where such a study has not been explored.

The study aims to equip robots with the ability to precisely maintain interaction forces, which is crucial for tasks such as polishing in highly dynamic environments with unknown and varying stiffness and geometry, including those found in airplane wings or thin, soft materials. The purpose of this study is to develop a novel adaptive force-tracking admittance control scheme aimed at achieving a faster response rate with higher tracking accuracy for robot force control.

In the proposed method, the traditional admittance model is improved by introducing a pre-proportional-derivative controller to accelerate parameter convergence. Subsequently, the authors design an adaptive law based on fuzzy logic systems (FLS) to compensate for uncertainties in the unknown environment. Stability conditions are established for the proposed method through Lyapunov analysis, which ensures the force tracking accuracy and the stability of the coupled system consisting of the robot and the interaction environment. Furthermore, the effectiveness and robustness of the proposed control algorithm are demonstrated by simulation and experiment.

A variety of unstructured simulations and experimental scenarios are designed to validate the effectiveness of the proposed algorithm in force control. The outcomes demonstrate that this control strategy excels in providing fast response, precise tracking accuracy and robust performance.

In real-world applications spanning industrial, service and medical fields where accurate force control by robots is essential, the proposed method stands out as both practical and straightforward, delivering consistently satisfactory performance across various scenarios.

This research introduces a novel adaptive force-tracking admittance controller based on FLS and validated through both simulations and experiments. The proposed controller demonstrates exceptional performance in force control within environments characterized by unknown and varying.

Devices for step estimation are body-worn devices used to compute steps taken and/or distance covered by the user. Even though textiles or clothing are foremost to come to mind in terms of articles meant to be worn, their prominence among devices and systems meant for cadence is overshadowed by electronic products such as accelerometers, wristbands and smart phones. Athletes and sports enthusiasts using knee sleeves should be able to track their performances and monitor workout progress without the need to carry other devices with no direct sport utility, such as wristbands and wearable accelerometers. The purpose of this study thus is to contribute to the broad area of wearable devices for cadence application by developing a cheap but effective and efficient stride measurement system based on a knee sleeve.

A textile strain sensor is designed by weft knitting silver-plated nylon yarn together with nylon DTY and covered elastic yarn using a 1 × 1 rib structure. The area occupied by the silver-plated yarn within the structure served as the strain sensor. It worked such that, upon being subjected to stress, the electrical resistance of the sensor increases and in turn, is restored when the stress is removed. The strip with the sensor is knitted separately and subsequently sewn to the knee sleeve. The knee sleeve is then connected to a custom-made signal acquisition and processing system. A volunteer was employed for a wearer trial.

Experimental results establish that the number of strides taken by the wearer can easily be correlated to the knee flexion and extension cycles of the wearer. The number of peaks computed by the signal acquisition and processing system is therefore counted to represent stride per minute. Therefore, the sensor is able to effectively count the number of strides taken by the user per minute. The coefficient of variation of over-ground test results yielded 0.03%, and stair climbing also obtained 0.14%, an indication of very high sensor repeatability.

The study was conducted using limited number of volunteers for the wearer trials.

By embedding textile piezoresistive sensors in some specific garments and or accessories, physical activity such as gait and its related data can be effectively measured.

To the best of our knowledge, this is the first application of piezoresistive sensing in the knee sleeve for stride estimation. Also, this study establishes that it is possible to attach (sew) already-knit textile strain sensors to apparel to effectuate smart functionality.

This study aims to identify and evaluate the key strategies to promote the implementation of automation techniques with reference to the Nigerian construction industry.

Pragmatic philosophical thinking using a mixed-method approach (a combination of qualitative and quantitative) was adopted for this study. The qualitative strand of this research was achieved using a Delphi technique while a well-structured questionnaire conducted among 191 construction professionals was adopted to attain the quantitative strand. Obtained data were analyzed using frequencies, percentages, mean item scores, Kruskal–Wallis H test and exploratory factor analysis (FA).

Results revealed that the “provision of funding and subsidies for automation techniques” “mandatory automation policies and regulations,” “creating incentives for adoption,” “formulation of programs to promote awareness” and “deploying gamification to boost employee performance” were the top five strategies to promote the adoption of automation techniques. FA revealed four principal clusters, namely, awareness and publicity programs, government regulations and standards, provision of education and training and awards and recognition.

This study provided a solid theoretical and empirical foundation that can be useful to construction industry stakeholders, decision-makers, policymakers and the government in mapping out strategies to promote the incorporation and deployment of automation and robotics in the construction industry.

To the best of the authors’ knowledge, this study is one of the first in developing countries and Nigeria to establish an ordered grouping structure of the strategies to promote the adoption of automation techniques.