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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.

This study explores how police culture is experienced by women officers serving in positions where they are significantly underrepresented (i.e. leadership and elite specialty units) and the environmental factors that shape these experiences.

A qualitative analysis of transcripts from interviews with 71 women serving in male-dominated roles was conducted (N = 39 ranking women; N = 32 women on elite units).

Participants described five occupational, organizational and assignment-level factors that shaped their workplace experiences. While some contextual forces at play are similar for women working patrol (e.g. traditional police culture, the underrepresentation of women in law enforcement) unique position-level factors were also identified (e.g. the high-risk and consequential nature of the work).

While the cultural environment for women patrol officers has improved in the past few decades, the same cannot be said for women working in positions that are still dominated by men. More attention to this area of policing is needed to ensure gender diversity is achieved throughout organizations and not only in positions deemed suitable for women.

The study extends research on women in policing beyond the focus on patrol. Further, it explores the assignment- and rank-based perspectives of police culture, which are largely absent from the literature.

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.

Fall accidents can cause casualties and economic losses in the construction industry. Fall portents, such as loss of balance (LOB) and sudden sways, can result in fatal, nonfatal or attempted fall accidents. All of them are worthy of studying to take measures to prevent future accidents. Detecting fall portents can proactively and comprehensively help managers assess the risk to workers as well as in the construction environment and further prevent fall accidents.

This study focused on the postures of workers and aimed to directly detect fall portents using a computer vision (CV)-based noncontact approach. Firstly, a joint coordinate matrix generated from a three-dimensional pose estimation model is employed, and then the matrix is preprocessed by principal component analysis, K-means and pre-experiments. Finally, a modified fusion K-nearest neighbor-based machine learning model is built to fuse information from the x, y and z axes and output the worker's pose status into three stages.

The proposed model can output the worker's pose status into three stages (steady–unsteady–fallen) and provide corresponding confidence probabilities for each category. Experiments conducted to evaluate the approach show that the model accuracy reaches 85.02% with threshold-based postprocessing. The proposed fall-portent detection approach can extract the fall risk of workers in the both pre- and post-event phases based on noncontact approach.

First, three-dimensional (3D) pose estimation needs sufficient information, which means it may not perform well when applied in complicated environments or when the shooting distance is extremely large. Second, solely focusing on fall-related factors may not be comprehensive enough. Future studies can incorporate the results of this research as an indicator into the risk assessment system to achieve a more comprehensive and accurate evaluation of worker and site risk.

The proposed machine learning model determines whether the worker is in a status of steady, unsteady or fallen using a CV-based approach. From the perspective of construction management, when detecting fall-related actions on construction sites, the noncontact approach based on CV has irreplaceable advantages of no interruption to workers and low cost. It can make use of the surveillance cameras on construction sites to recognize both preceding events and happened accidents. The detection of fall portents can help worker risk assessment and safety management.

Existing studies using sensor-based approaches are high-cost and invasive for construction workers, and others using CV-based approaches either oversimplify by binary classification of the non-entire fall process or indirectly achieve fall-portent detection. Instead, this study aims to detect fall portents directly by worker's posture and divide the entire fall process into three stages using a CV-based noncontact approach. It can help managers carry out more comprehensive risk assessment and develop preventive measures.

Computer vision and deep learning (DL) methods have been investigated for personal protective equipment (PPE) monitoring and detection for construction workers’ safety. However, it is still challenging to implement automated safety monitoring methods in near real time or in a time-efficient manner in real construction practices. Therefore, this study developed a novel solution to enhance the time efficiency to achieve near-real-time safety glove detection and meanwhile preserve data privacy.

The developed method comprises two primary components: (1) transfer learning methods to detect safety gloves and (2) edge computing to improve time efficiency and data privacy. To compare the developed edge computing-based method with the currently widely used cloud computing-based methods, a comprehensive comparative analysis was conducted from both the implementation and theory perspectives, providing insights into the developed approach’s performance.

Three DL models achieved mean average precision (mAP) scores ranging from 74.92% to 84.31% for safety glove detection. The other two methods by combining object detection and classification achieved mAP as 89.91% for hand detection and 100% for glove classification. From both implementation and theory perspectives, the edge computing-based method detected gloves faster than the cloud computing-based method. The edge computing-based method achieved a detection latency of 36%–68% shorter than the cloud computing-based method in the implementation perspective. The findings highlight edge computing’s potential for near-real-time detection with improved data privacy.

This study implemented and evaluated DL-based safety monitoring methods on different computing infrastructures to investigate their time efficiency. This study contributes to existing knowledge by demonstrating how edge computing can be used with DL models (without sacrificing their performance) to improve PPE-glove monitoring in a time-efficient manner as well as maintain data privacy.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

This study aimed to compare the performance of sustainable shoes made with bacterial cellulosic composite and commercial leather shoes using an experimental research design. The two specific research objectives were: (1) to examine the basic material properties of multi-layered bacterial cellulosic materials (MBC), which include green tea-based cellulosic (GBC) mats, hemp fabrics, and denim fabrics, in comparison with those of two-layered leathers (MCP) consisting of calf-skin and pig-skin – commonly used in shoe manufacturing; and (2) to explore wearers’ performance in the two types of shoes by assessing quantitative kinematic and kinetic parameters of lower body movements.

This study focused on assessing the basic materials testing and performance of sustainable shoes through a biomechanical approach, in contrast to commercially available leather shoes, through human wear trials. In this study, green tea-based cellulosic (GBC) mats were developed using the optimal combination of ingredients for cellulose growth. Subsequently, the GBC, denim fabric (100% cotton), and 100% hemp fabric were combined to create multi-layered bacterial cellulosic materials (MBC) as an alternative to leather. Additionally, calf-skin and pig-skin leathers were utilized to produce a commercially available two-layered leather (MCP), commonly employed in shoe manufacturing. 37 of the 42 human subjects who participated in wear testing were collected. A paired t-test was conducted to determine whether significant mean differences existed between the two shoe types, a paired t-test was conducted.

To develop a biodegradable and compostable material that could be used as a leather alternative for the footwear industry, we proposed MBC and examined its properties compared with those of MCP, a product often used when making shoes. These findings confirmed the similar properties of MBC and MCP from the material testing and the possibility of using a men’s sustainable shoe prototype as a leather alternative, in terms of kinematics and kinetics.

The new multi-layered bacterial cellulosic materials (MBC) could be an alternative to commercial leathers such as innovative sustainable material construction, advanced design, and advanced techniques to optimize the overall performance of sustainable footwear.

Investigating the integration of smart textile technologies, ergonomic design principles, and personalized customization will contribute to developing MBC and making sustainable shoes using MBC compared with commercial leather shoes. This study provides valuable insights into further refinement and innovation in the sustainable footwear industry.

This study aims to explore gender performance in entrepreneurial pitching. Understanding pitching as a social practice, the authors argue that pitch content and body gestures contain gender-based norms and practices. The authors focus on early-stage ventures and the hegemonic masculinities and femininities that are performed in entrepreneurial pitches. The main research question is as follows: How is gender performed in entrepreneurial pitching?

The authors carried out the study with the post-structuralist feminist approach. The authors collected and analyzed nine online pitches with the reflexive thematic method to depict hegemonic masculinities and femininities performed at the pitch.

The authors found that heroic and breadwinner masculinities are dominant in pitching. Both male and female founders perform hegemonic masculinities. Entrepreneurs are expected to be assertive but empathetic people. Finally, there are connections between what entrepreneurs do and what investors ask, indicating the iteration of gender performance and expectations.

While the online setting helps the authors to collect data during the pandemic, it limits the observation of the place, space and interactions between the judges/investors and the entrepreneurs. As a result, the linguistic and gesture communication of the investors in the pitch was not discussed in full-length in this paper. Also, as the authors observed, people would come to the pitch knowing what they should perform and how they should interact. Therefore, the preparation of the pitch as a study context could provide rich details on how gender norms and stereotypes influence people's interactions and their entrepreneurial identity. Lastly, the study has a methodological limitation. The authors did not include aspects of space in the analysis. It is mainly due to the variety of settings that the pitching sessions that the data set had.

For social practices and policies, the results indicate barriers to finance for women entrepreneurs. Women entrepreneurs are rewarded when they perform entrepreneurial hegemonic masculinities with a touch of emphasized femininities. Eventually, if women entrepreneurs do not perform correctly as investors expect them to, they will face barriers to acquiring finance. It is important to acknowledge how certain gendered biases might be (re)constructed and (re)produced through entrepreneurial activities, in which pitching is one of them.

Practitioners could utilize research findings to understand how gender stereotypes exist not only on the pitch stage but also before and after the pitch, such as the choice of business idea and pitch training. In other words, it is necessary to create a more enabling environment for women entrepreneurs, such as customizing the accelerator program so that all business ideas receive relevant support from experts. On a macro level, the study has shown that seemingly gender-equal societies do not practically translate into higher participation of women in entrepreneurship.

For theoretical contributions, the study enhances the discussion that entrepreneurship is gendered; women and men entrepreneurs need to perform certain hegemonic traits to be legitimated as founders. The authors also address various pitching practices that shape pitch performance by including both textual and semiotic data in the study. This study provides social implications on the awareness of gendered norms and the design of entrepreneurial pitching.

This study aims to improve the use of digitalization in facilities management (FM) for shopping complex facilities in the post-COVID-19 era. The resumption of economic activities, especially in shopping complexes, poses challenges for FM with throngs of shoppers. To tackle these challenges, enhanced and innovative FM practices are necessary.

The study used a qualitative research approach, incorporating case studies, interviews, observations and documentation. It focused on super-regional shopping complexes in the Klang Valley, Malaysia, selecting two complexes for qualitative data collection. Supplementary data were gathered from various sources, including government policy publications, websites, books, journal papers and archival records.

The research provides valuable insights into FM innovations and the application of FM digitalization in shopping complexes after the COVID-19 pandemic. It also addresses challenges faced by FM teams during this period. Recommendations for implementing FM digitalization in super-regional shopping complexes post-COVID-19 include developing skilled personnel, defining appropriate work scopes, strategies and policies, using cost-effective software, and increasing occupant awareness. The involvement of outsourced service providers is advised, emphasizing their understanding of the organization’s business model and innovative approaches.

The findings offer new perspectives on the characteristics of FM digitalization in the commercial sector during business disruptions caused by the pandemic. The proposed strategies are grounded in real industry implementations, aiming to enhance the FM digitalization approach for improved business performance.

The purpose of this study is to reflect on and analyse the sensory experiences related to the transition to remote work during the COVID-19 pandemic. The research seeks to understand how these experiences have influenced the integration of work practices into home and family life and the subsequent adaptations and embodied learning that arise in response.

The authors' research approach incorporates autoethnographic methods to explore the sensory, affective and emotional experiences of transitioning to remote work. The authors draw on principles of embodied learning, as influenced by Gilles Deleuze, and utilise a range of ethnographic tools including note-taking, audio memos, photography, shared conversations and written reflections to gather their data.

The study illuminates the ways bodies learn to accommodate the new organisational contexts that arise when the spaces, affects and forces of home and work intersect. It demonstrates how the integration of work into the private domain resulted in new affective and material arrangements, involving novel sensory experiences and substantial embodied learning.

This study provides a distinct, sensory-oriented perspective on the challenges and transformations of remote work practices amid the pandemic. By focussing on the affective resonances and embodied learning that emerge in this context, it contributes to the emerging discourse around post-lockdown work practices and remote work in general.