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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.

Construction companies are increasingly adopting sensing technologies like laser scanners, making it necessary to upskill the future workforce in this area. However, limited jobsite access hinders experiential learning of laser scanning, necessitating the need for an alternative learning environment. Previously, the authors explored mixed reality (MR) as an alternative learning environment for laser scanning, but to promote seamless learning, such learning environments must be proactive and intelligent. Toward this, the potentials of classification models for detecting user difficulties and learning stages in the MR environment were investigated in this study.

The study adopted machine learning classifiers on eye-tracking data and think-aloud data for detecting learning stages and interaction difficulties during the usability study of laser scanning in the MR environment.

The classification models demonstrated high performance, with neural network classifier showing superior performance (accuracy of 99.9%) during the detection of learning stages and an ensemble showing the highest accuracy of 84.6% for detecting interaction difficulty during laser scanning.

The findings of this study revealed that eye movement data possess significant information about learning stages and interaction difficulties and provide evidence of the potentials of smart MR environments for improved learning experiences in construction education. The research implication further lies in the potential of an intelligent learning environment for providing personalized learning experiences that often culminate in improved learning outcomes. This study further highlights the potential of such an intelligent learning environment in promoting inclusive learning, whereby students with different cognitive capabilities can experience learning tailored to their specific needs irrespective of their individual differences.

The classification models will help detect learners requiring additional support to acquire the necessary technical skills for deploying laser scanners in the construction industry and inform the specific training needs of users to enhance seamless interaction with the learning environment.

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 have the potential to alleviate musculoskeletal disorders (MSDs), increase productivity and ultimately reduce construction project costs, but the concerns about their ethical, social and psychological risks for the construction industry are unknown. This paper investigates these risks and their implications for exoskeleton acceptance.

Participants performed masonry tasks without an exoskeleton and with an active and passive exoskeleton. Using descriptive and inferential statistics, ethical, social and psychological risks associated with exoskeletons, as well as their trust levels, were assessed. Objective data were procured to determine stress and productivity levels with and without these exoskeletons, while subjective data included trust and the ethical and social risks of the exoskeletons.

The findings show that lack of informed consent and procuring sensitive health data is an important ethical consideration when using active and passive exoskeletons. Regarding social risks, unequal access to exoskeletons, exoskeleton sharing and exoskeleton costs as major concerns. Furthermore, the findings revealed statistical differences between active and passive exoskeletons in terms of certain social risks. The findings show that participants believed in passive exoskeletons more than active exoskeletons. The results also revealed a strong positive relationship between ethical and social risks, and trust levels. The results also indicated that both exoskeletons induce relatively moderate stress levels and enhance productivity, compared to the no exoskeleton condition.

This study is one of the few empirical investigations in the construction industry on the ethical and social risks associated with exoskeletons, which can facilitate the adoption of exoskeletons for mitigating MSDs in the construction industry.