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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 explore the prevalence and determinants of poor sleep quality among industrial workers in Thailand. Additionally, the authors assess the risk of work-related injuries associated with poor sleep quality.

A descriptive correlational research was implemented. A total of 472 workers from the northern region of Thailand contributed to this study. Sleep quality was assessed by the Thai version of the Pittsburgh Sleep Quality Index (Thai-PSQI). The score of more than five indicated poor sleep quality. Participants completed self-administered demographic, work characteristics and work-related injury questionnaires. Data were analyzed by applying descriptive and logistic regression statistical techniques.

More than one-third of the workers had reported poor sleep quality. Results from multivariable logistic regression analysis yielded male gender (OR = 2.74, 95% CI 1.46–5.17), alcohol drinking (OR=2.1, 95% CI 1.24–3.35), pain (OR=2.05, 95% CI 1.32–3.17) and rotating shift work (OR=1.94, 95% CI 1.23–3.05) increased the risk of poor sleep quality. Furthermore, poor sleep quality was statistically significantly associated with the risk of work-related injuries (OR = 3.98, 95% CI = 2.39–6.66).

Findings of this study indicate that the prevalence of poor sleep quality is high among industrial workers. Work characteristics and health behaviors were associated with poor sleep quality which increases the risk of work-related injuries. Modification of work environment and personal life style choices can improve quality of sleep among workers and consequently lower incidence of work-related injuries.

The purpose of this paper is to discuss the response of the relevant authorities to evidence that female primary schoolteachers have an elevated suicide risk in the UK. The paper situates the recent tragic death of a primary school head teacher, following an Ofsted inspection at her school, within the wider context of teacher suicide deaths and asks what, if any, action the authorities have taken to prevent avoidable suicide deaths from occurring.

The paper examines a recent case of suicide by a primary head teacher within the wider context of statistical data on suicides by primary schoolteachers and in relation to previous cases of suicide linked to a school inspection.

The paper suggests that the relevant authorities have failed to act in relation to evidence of high suicide risk amongst female primary schoolteachers and to previous suicide deaths linked to the impact of a school inspection. Without learning from suicide deaths and acting on available evidence, there is a risk that preventable suicide deaths will continue to occur.

The paper draws together case study evidence and statistical data to make the case for regulatory reform to ensure that work-related suicides are investigated, monitored and prevented.

This paper aims to identify and empirically analyze useful and applicable metrics for measuring and managing the sustainability performance of small and medium-sized enterprises (SMEs).

To achieve the objective of the paper, potential metrics were adopted from previous research related to industrial sustainability and an empirical analysis was carried to assess the applicability of the metrics by collecting empirical data from Italian footwear SMEs using a structured questionnaire. The SMEs were selected using a convenience sampling method.

The results of the within-case analysis and the cross-case analysis indicate that the majority of the metrics were found to be useful and applicable to each of the SMEs and across the SMEs, respectively. These metrics emphasized measuring industrial sustainability performance related to financial benefits, costs and market competitiveness for the economic sustainability dimension; resources for the environmental sustainability dimension; and customers, employees and the community for the social sustainability dimension.

Apart from the within-case analysis and cross-case analysis, it was not possible to conduct statistical analysis since a small number of SMEs were accessible to collect empirical data.

The findings of the paper have considerable academic, managerial and policy implications and will provide a theoretical basis for future research on measuring and managing industrial sustainability performance. By providing a set of empirically supported metrics based on the triple bottom line approach (i.e. economic, environmental and social metrics), this paper contributes to the existing knowledge in the field of industrial sustainability performance measurement.