Unsafe Behavior Research Articles

ACT method for safety training: An approach to improve on-site safety performance

Unsafe behavior is one of the main causes limiting the improvement of on-site safety performance. Safety training is an important means of preventing unsafe behavior. With changes in the scale of operations and employee characteristics, traditional training methods have largely failed to meet the practical demands. Therefore, to improve the effectiveness of safety training, this paper analyzes the shortcomings of the current training methods, demonstrates the potential value of accident cases for improving the training effect, and the advantages of 24Model for case analysis in safety training. Subsequently, this paper constructs a systematic training system with Accident-Cause-Training (ACT) as the training relationship chain, which runs the accident cases throughout the training process, 24Model is utilized to trace the unsafe behaviors that lead to accidents from the perspectives of organizational and individual level, and the training content is established according to the analysis results. This paper also conducts a case study to verify the operability of the ACT method, specifically, a training programme for limited space operations at a national-level oil and gas storage and transportation company is designed and implemented. Finally, by comparing the similarities and differences between the ACT method and other training methods from various perspectives, the universality and participation of the ACT method are proved, and the development trend of the ACT method is also discussed. It is believed that the proposed ACT method could enrich the types of safety training methods and provide an effective and scientific tool to enhance the safety performance of field operations.

(Mis-)use of standard Autopilot and Full Self-Driving (FSD) Beta: Results from interviews with users of Tesla's FSD Beta.

Tesla's Full Self-Driving Beta (FSD) program introduces technology that extends the operational design domain of standard Autopilot from highways to urban roads. This research conducted 103 in-depth semi-structured interviews with users of Tesla's FSD Beta and standard Autopilot to evaluate the impact on user behavior and perception. It was found that drivers became complacent over time with Autopilot engaged, failing to monitor the system, and engaging in safety-critical behaviors, such as hands-free driving, enabled by weights placed on the steering wheel, mind wandering, or sleeping behind the wheel. Drivers' movement of eyes, hands, and feet became more relaxed with experience with Autopilot engaged. FSD Beta required constant supervision as unfinished technology, which increased driver stress and mental and physical workload as drivers had to be constantly prepared for unsafe system behavior (doing the wrong thing at the worst time). The hands-on wheel check was not considered as being necessarily effective in driver monitoring and guaranteeing safe use. Drivers adapt to automation over time, engaging in potentially dangerous behaviors. Some behavior seems to be a knowing violation of intended use (e.g., weighting the steering wheel), and other behavior reflects a misunderstanding or lack of experience (e.g., using Autopilot on roads not designed for). As unfinished Beta technology, FSD Beta can introduce new forms of stress and can be inherently unsafe. We recommend future research to investigate to what extent these behavioral changes affect accident risk and can be alleviated through driver state monitoring and assistance.

Reducing workplace unsafe behaviour using risk classification, profiling, risk tolerance approach

Individuals’ risk tolerance capacity is one of the primary reasons for their unsafe behaviour at the workplace and is commonly identified as an important causal factor for the majority of workplace accidents. Research has exhibited the significance of individuals’ risk tolerance while dealing with risk at workplace. However, limited research is done to explore the influence of various factors on individuals’ risk tolerance. In this paper, questionnaire survey (42 questions based on the 36 factors) data were gathered from 606 miners (various category) belonging from three major coal producing subsidiary of northern India. Based on the responses received on the questionnaire survey, (1) Statistical method is used to identify the critical factors (10 critical factors) among all; (2) risk classification (Personal Protective Equipment, operational and others-3 way) system was introduced based on the nature of risk being taken; and (3) organisational risk profiling was done. The methodology of risk profiling and risk classification introduced in this paper will help the organisation identify the critical groups and the nature of the risks being taken, respectively. Further, by considering the combined effect of all three outcomes, necessary compliances can be carried out like design of training module, framing of safety policies and deployment of suitable manpower.

Drinking motives as a predictor of readiness to change alcohol use

Drinking motives have been identified as important predictors of alcohol consumption. Similarly, the degree of readiness to change (RTC) can predict behavioral changes when drinking alcohol. However, the link between drinking motives and RTC has not been explored in previous research. The aim of this study is to investigate whether the four drinking motives (coping, enhancement, social, conformity) can predict the three stages of RTC (precontemplation, contemplation and action) in relation to alcohol consumption. Two hundred and fifty-two undergraduates’ students completed an online self-assessment survey on Qualtrics that assessed motives for alcohol use, drinking behavior, and RTC. Hierarchical regressions showed that among the four specific drinking motives, coping motives significantly predicted all three stages of RTC; conformity motives positively predicted the action stage of change; social motives negatively predicted the precontemplation and action stages of change; enhancement motives were not significant in predicting RTC stages. These results indicate that the three RTC levels can be predicted by coping, social, and conformity motives, but not by enhancement motives. Additionally, given the importance of coping motives, it might be useful to address and include healthier coping mechanisms as part of clinical interventions and prevention methods to circumvent unsafe drinking behaviors independent of a single RTC stage.

Training Simulator for Manual Lathe Operation Using Motion Capture – Addition of Teaching Function and Evaluation of Training Effectiveness –

Numerous training simulators have been developed using virtual reality (VR) owing to their various advantages. Systems for training machine operations with physical movements face differences in the operational feel between actual and virtual machines. Moreover, virtual training is problematic in safety education because trainees in safe virtual environments can exhibit unsafe behavior in reality. To solve these problems, a previous study developed a virtual reality (VR) system to train a lathe operation with mixed reality using a motion capture system. This study included a function to teach the procedure and safety precautions for straight turning operations using a lathe. To evaluate the training effectiveness of this system, an experiment was conducted to compare learning using a video. Testees were divided into a simulator group, who learned with the system, and a video group, who learned with the video material. Work on the actual lathe by each testee after learning, was evaluated. Consequently, the actual work by the testees who used this system had fewer errors and shorter standstill times in which they attempted to recollect the next phase task. Although the number of trainees was small, this relationship had a statistical advantage. In the actual work, all the testees in the video group entered the danger area; however, only half of the testees in the simulator group entered the danger area. Therefore, a trainee using a simulator can remember the work process more reliably and accurately and perform it safely. Moreover, trainees who have undergone training several times should be able to perform actual work without making operational errors or engaging in unsafe behaviors.

What factors contribute to e-scooter crashes: A first look using a naturalistic riding approach

Introduction: Shared dockless electric scooters (e-scooters) are a popular shared mobility service providing an accessible last-mile transportation option in urban and campus environments. However, city and campus stakeholders may hesitate to introduce these scooters due to safety concerns. While prior e-scooter safety studies have collected injury data from hospitals or riding data under controlled or naturalistic conditions, these datasets are limited and did not identify risk factors associated with e-scooter riding safety. To address this gap in e-scooter safety research, this study collected the largest naturalistic e-scooter dataset to date and quantified the safety risks associated with behavioral, infrastructure, and environmental factors. Method: A fleet of 200 e-scooters was deployed on Virginia Tech’s campus in Blacksburg, VA for a 6-month period. Fifty were equipped with a unique onboard data acquisition system, using sensors and video to capture e-scooter trips in their entirety. The resulting dataset consisted of 3,500 hours of data spanning over 8,500 trips. Algorithms were developed to identify safety critical events (SCEs) in the dataset and analyses were conducted to determine the prevalence of various SCE risk factors and associated odds ratios. Results: Results from this study indicate that infrastructure-related factors, behavior of e-scooter riders and other actors, and environmental factors all contributed to the SCE risk for e-scooter riders in Virginia Tech’s pedestrian-dense campus environment. Conclusions: To help mitigate unsafe rider behavior, educational outreach programs should quantify the significant risks associated with infrastructure, behavioral, and environmental risk factors and provide clear recommendations to riders. Improved infrastructure maintenance and design may also improve safety for e-scooter riders. Practical Applications: The infrastructure, behavioral, and environmental risk factors quantified in this study can be applied by e-scooter service providers, municipalities, and campus administrators to develop mitigation strategies to reduce the safety risks associated with e-scooter deployments in the future.

DeepSafety: a deep neural network-based edge computing framework for detecting unsafe behaviors of construction workers

DeepSafety: a deep neural network-based edge computing framework for detecting unsafe behaviors of construction workers

Improving Lithium-Ion Battery Supply Chain Information Security by User Behavior Monitoring Algorithm Incorporated in Cloud Enterprise Resource Planning

Cloud enterprise resource planning (Cloud ERP) provides an efficient big data management solution for lithium-ion battery (LiB) enterprises. However, in the open ecological environment, Cloud ERP makes the LiB supply chain face multi-user and multi-subject interactions, which can generate sensitive data and privacy data security issues (such as user override access behavior). In this study, we take the value and information interaction into account to examine the user behaviors of the diverse stakeholders in the LiB supply chain. Therefore, a user behavior monitoring algorithm (UBMA), different from the mainstream supervised algorithms and unsupervised learning algorithms, is proposed to monitor the unsafe behaviors that may threaten data privacy in Cloud ERP. The results show that the UBMA can accurately search out the user behavior sequence where the unsafe behavior is located from a large amount of user behavior information, which reduces the complexity of directly identifying the unsafe behavior. In addition, compared with the recursive unsupervised binary classification method, the UBMA model has a lower resource consumption and higher efficiency. In addition, the UBMA has great flexibility. The UBMA can be further updated and extended by re-establishing the statistical characteristics of the standard user behavior fields to quickly adapt to user changes and function upgrades in the LiB supply chain.

Extracting Worker Unsafe Behaviors from Construction Images Using Image Captioning with Deep Learning–Based Attention Mechanism

Safety in the construction industry has always been a focus of attention. Existing methods of detecting unsafe behavior of workers relied primarily on manual detection. Not only did it consume significant time and money, but it also inevitably produced omissions. Currently, automated techniques for detecting unsafe behaviors rely only on the unsafe factors of workers’ ontology to judge their behaviors, making it difficult to understand unsafe behaviors in complex scenes. To address the presented problems, this study proposed a method to automatically extract workers’ unsafe behaviors by combining information from complex scenes—an image captioning based on an attention mechanism. First, three different sets of image captioning models were constructed using convolutional neural network (CNN), which are widely used in AI. These models could extract key information from complex scenes was constructed. Then, two datasets dedicated to the construction domain were created for method validation. Finally, three sets of experiments were conducted by combining the datasets and the three different sets of models. The results showed that the method could detect the worker’s job type and output the interaction behavior between the worker and the target (unsafe behavior) based on the environmental information in the construction images. We introduced environmental information into the determination of workers’ unsafe behaviors for the first time and not only output the worker’s job type but also determine the worker’s behavior. This allows the model output to be better for ergonomic analysis.

A Framework for Design, Verification, and Management of SoC Access Control Systems

System-on-chip (SoC) architectures are a heterogeneous mix of microprocessors, custom accelerators, memories, interfaces, peripherals, and other resources. These resources communicate using complex on-chip interconnect networks that attempt to quickly and efficiently arbitrate memory transactions whose behaviors can vary drastically depending on the current mode of operation and system operating state. Security- and safety-critical applications require access control policies that define how these resources interact to ensure that malicious and unsafe behaviors do not occur. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aker</small> is a design and verification framework for on-chip access control. The core of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aker</small> is the access control wrapper (ACW)–a high-performance yet efficient hardware module that dynamically arbitrates on-chip communications. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aker</small> distributes ACWs across the SoC and programs them to perform local access control. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aker</small> provides a firmware generation tool and a property-driven security verification methodology to ensure that the ACWs are properly integrated and configured. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aker</small> security verification confirms that the ACW behaves properly at IP level. It verifies the hardware root of trust firmware configures the ACW correctly. And it evaluates system-level security threats due to interactions between shared resources. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aker</small> is experimentally validated on a Xilinx UltraScale+ programmable SoC. Additionally, an <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aker</small> access control system is integrated into the OpenPULP multicore archtiecture that uses OpenTitan hardware root-of-trust for firmware configuration.

Job insecurity and unsafe behavior: Exploring curvilinear and moderated relationships.

Job insecurity and unsafe behavior: Exploring curvilinear and moderated relationships.

Perilaku Seksual Berisiko Pada Remaja di Kabupaten Banyumas

Background: Risky sexual behavior that is generally carried out by adolescents is an activity that is often carried out when dating, namely holding hands, kissing, and petting and having sex while dating. The increasing percentage of teenagers who have had premarital sex will also increase the negative impacts that can occur, such as teenage pregnancy, sexually transmitted infections, to social impacts in society. Aim:The purpose of this study was to identify risky sexual behavior in adolescents in Banyumas Regency. Methods: This research is a quantitative research with cross sectional research design. The sample of this research is junior high school, high school and vocational school students who attend school in Banyumas Regency. The sampling technique with probability stratified random sampling has been used and obtained 452 respondents. Results: The result show the respondents with risky sexuality were 251 respondents (55.5%). It is known that 231 teenagers with unsafe risky sexual behavior (51.1%) and 20 teenagers with unsafe behavior (4.4%). Most (251 respondents) have held hands with their partners (68.13%), hugged their partners' bodies (49.40%), masturbated (34.66%) and hugged their partners (27.09%). Conclusion: The high percentage of adolescents who have had premarital sexual intercourse will also increase the adverse effects that can occur, such as teenage pregnancy, sexually transmitted infections, to social impacts in society.

Exploring mandatory reporting in social care and social services in Sweden

AbstractIn Sweden, mandatory reporting in social services is regulated by the Social Services Act to protect service users and improve services. The aim of this article is to describe mandatory reports by staff and compare types of mistreatment, severity and actions across three areas of social services: elderly care, disability services, and individual and family care. All the reports written by staff and investigated by a designated official over a 1‐year period (n = 1105) in one of the largest regions in Sweden were retrieved, inductively coded and quantitatively analysed. The results showed that most of the reports related to unsafe behaviour and organisational issues. Of all the reports, 14% (n = 156) were deemed to be serious. Differences in assessing mistreatment are discussed in relation to incident reporting in health care. The need for new models of analysis for mandatory reporting in social care is highlighted.

Recognition of Unsafe Onboard Mooring and Unmooring Operation Behavior Based on Improved YOLO-v4 Algorithm

In the maritime industry, unsafe behaviors exhibited by crew members are a significant factor contributing to shipping and occupational accidents. Among these behaviors, unsafe operation of mooring lines is particularly prone to causing severe accidents. Video-based monitoring has been demonstrated as an effective means of detecting these unsafe behaviors in real time and providing early warning to crew members. To this end, this paper presents a dataset comprising videos of unsafe mooring line operations by crew members on the M.V. YuKun. Additionally, we propose an unsafe behavior recognition model based on the improved You Only Look Once (YOLO)-v4 network. Experimental results indicate that the proposed model, when compared to other models such as the original YOLO-v4 and YOLO-v3, demonstrates a significant improvement in recognition speed by approximately 35% while maintaining accuracy. Additionally, it also results in a reduction in computation burden. Furthermore, the proposed model was successfully applied to an actual ship test, which further verifies its effectiveness in recognizing unsafe mooring operation behaviors. Results of the actual ship test highlight that the proposed model’s recognition accuracy is on par with that of the original YOLO-v4 network but shows an improvement in processing speed by 50% and a reduction in processing complexity by about 96%. Hence, this work demonstrates that the proposed dataset and improved YOLO-v4 network can effectively detect unsafe mooring operation behaviors and potentially enhance the safety of marine operations.

건설공사 굴착기 근접 작업자의 불안전행동 기반 휴먼에러 연구

According to the latest data released by the Ministry of Employment and Labor, 259 (18.9%) of the 1,371 construction accident deaths between 2018 and 2020 were caused by construction machinery or equipment. In particular, excavators cause the most deaths among construction equipment, and are classified as the most dangerous equipment in previous studies on equipment risk index.&#x0D; Serious accidents of excavators mainly occur in the form of a crush or a collision with a nearby worker while the equipment is reversing or rotating the boom. In the meantime, studies for preventing excavator accidents have been conducted several times, but the trend is focusing on physical safety measures such as safety devices and alarms, or research for improving the safety capabilities of equipment operators. However, as most of the equipment accidents are caused by human factors, it is judged that research on this should be conducted in parallel.&#x0D; Accordingly, this study attempted to analyze why workers who were working around excavators were suffering from accidents due to unsafe behavior around dangerous equipment like it. To this end, we focused on the ‘factors’ and ‘causes’ of Human Error, and conducted a survey focusing on ‘factors’ as 4M aspects and ‘causes’ with James Reason’s Generic Error Modeling System (GEMS). The survey was analyzed using the SPSS statistical program. As a result of the study, the 'factors' of human error related to workers around excavators were analyzed as human error factors and job factors. and the 'Cause' of human error were identified as Slip, Lapse, Knowledge-based mistake, Situational violations. In order to maintain the attention of workers around excavator, some improvement measures like enhancing the effectiveness of safety education were suggested in this study.

A novel deep learning framework for detecting seafarer’s unsafe behavior

The accurate detection of seafarers’ unsafe behaviors is of great significance to improve the ship navigation safety. This study proposes an improved deep learning framework with a self-made dataset to detect the unsafe behavior of seafarers on duty. In order to increase the detection speed, the improved Cross Stage Partial connections (CSP) module is proposed to replace the original CSP module in the neck network of traditional algorithm. The efficient channel attention (ECA) module is also introduced to the backbone network of conventional algorithm as the attention mechanism network. In addition, the learning and representation capacities of the improved deep learning framework are promoted by redesigning the sizes of anchor boxes. The experiment results show that the proposed framework outperforms traditional object detection algorithms (e.g., YOLOv5s, R-CNN) in detecting seafarers’ unsafe behaviors because of the much higher detection speed and detection accuracy.

Structural equation model of the influence of noise annoyance on miners’ unsafe behavior in coal mines

Miners' unsafe behavior (UB) is the main cause of coal mine accidents. Previous research has suggested that excessive noise in tunnels impacts miners' temperaments and safe production behavior. To explore the influence of noise on UB, four different dimensions of noise annoyance were identified: cognitive, emotional, communication, and physical mechanism annoyance. The coal mine noise annoyance scale, miners' unsafe behavior intention (UBI) scale, and miners’ UB scale were developed according to different dimensions. In the first survey, a total of 200 questionnaires were distributed in the two coal mines, and 193 valid questionnaires were recovered. Exploratory factor analysis was conducted, and invalid items were removed according to the analysis results to form the final scale. Then, 500 final scales were distributed in two Chinese coal mines, and 482 valid questionnaires were collected. Confirmatory factor analysis was conducted by collating the data of the second questionnaire. Based upon the structural equation model (SEM), the SEM of the influence of noise annoyance degree on UB and the path analysis with latent variable path analysis with the latent variables (PA-LVs) mediating model of the effect of UBI on UB were established, and the path coefficients with fitting data of the model were analyzed. The results indicated that the four dimensions of coal mine noise annoyance had a strong positive impact on UB, which was reduced by less noise annoyance. In addition, the Bootstrap method was used to verify the mediating method among the four dimensions of noise annoyance, cognitive annoyance, emotional annoyance, with communication annoyance indirectly affecting UB through UBI. The research results provide theoretical support for reducing the UB of miners and improving coal mine safety levels.

Using the evidence to decision frameworks to formulate the direction and strength of recommendations for adapted guidelines of physical restraints in critical care: A Delphi study

BackgroundIt has been shown that the use of physical restraints has negative physiological and psychological impacts on critically ill patients. The development of clinical practice guidelines is necessary for minimizing the use of physical restraints. ObjectiveAs part of a guideline adaptation project, this study aimed to formulate the direction and strength of recommendations for adapted guidelines of physical restraints in critical care. MethodsLiterature retrieval of guidelines and systematic reviews was performed and guideline steering committee discussions were conducted to develop an inquiry questionnaire. From February to April 2022, a two-round study, including 27 multidisciplinary experts, was conducted using the evidence to decision frameworks and Delphi methods. ResultsThe 27 experts were from 22 of the 33 provinces/municipalities/autonomous regions of mainland China. For the first and second questionnaires, the recovery rates were 85.7% and 100%, respectively. In the first and second rounds, the average authoritative coefficients were 0.93 and 0.94, respectively. Regarding Kendall W values, the values were 0.555 and 0.120, respectively. A consensus was reached on 15 recommendations, including one strong recommendation, seven conditional recommendations and seven good practice statements. ConclusionIn the two rounds of consultations, a consensus was reached on 15 recommendations for the adapted physical restraint guidelines in critical care through the evidence to decision frameworks and Delphi methods. Recommendations in the adapted physical restraint guidelines are related to institutional and educational interventions, risk assessment, the management of patients’ unsafe behaviours, pain, mechanical ventilation, sleep, family engagement, mobility, sedation, and delirium. Implications for Clinical PracticeBased on our recommendations, we suggest nurses develop a physical restraint education bundle.

Unsafe Mining Behavior Identification Method Based on an Improved ST-GCN

Aiming to solve the problems of large environmental interference and complex types of personnel behavior that are difficult to identify in the current identification of unsafe behavior in mining areas, an improved spatial temporal graph convolutional network (ST-GCN) for miners’ unsafe behavior identification network in a transportation roadway (NP-AGCN) was proposed. First, the skeleton spatial-temporal map constructed using multi-frame human key points was used for behavior recognition to reduce the interference caused by the complex environment of the coal mine. Second, aiming to solve the problem that the original graph structure cannot learn the association relationship between the non-naturally connected nodes, which leads to the low recognition rate of climbing belts, fighting and other behaviors, the graph structure was reconstructed and the original partitioning strategy was changed to improve the recognition ability of the model for multi-joint interaction behaviors. Finally, in order to alleviate the problem that the graph convolution network has difficulty learning global information due to the small receptive field, multiple self-attention mechanisms were introduced into the graph convolution to improve the recognition ability of the model for unsafe behaviors. In order to verify the detection ability of the model regarding identifying unsafe behaviors of personnel in a coal mine belt area, our model was tested on the public datasets NTU-RGB + D and the self-built datasets of unsafe behaviors in a coal mine belt area. The recognition accuracies of the proposed model in the above datasets were 94.7% and 94.1%, respectively, which were 6.4% and 7.4% higher than the original model, which verified that the proposed model had excellent recognition accuracies.

Application of Social Media in Chemical Safety Training: A Case Study of Training GHS Standards to Students and Laboratory Staff at a University

Insufficient knowledge of chemical hazards can cause unsafe behaviors, diseases, and accidents in the laboratory, hence educational interventions are significant. Due to its unique qualities, distance learning via educational applications has grown dramatically. This study aimed to determine the efficacy of a safety training intervention via social media applications on the safety knowledge of staff and students at Shahid Beheshti University of Medical Sciences. A total of 166 staff and students at the laboratories of schools and research centers affiliated with Shahid Beheshti University of Medical Sciences were randomized to an experimental group and a control group. A training package was prepared including text, GIF, and video messages on the Globally Harmonized System of classification and labeling of Chemicals (GHS), and the experimental group received a one month training via a common messenger platform in Iran (Telegram). Participants’ chemical safety knowledge was assessed before and after the intervention. A validated questionnaire including 24 multiple-choice items in text format and 13 multiple-choice items in image format as well as demographic items was employed to collect data. Shapiro-Wilk, independent t test, and analysis of covariance were used to analyze the data. In the textual section of the questionnaire, the most frequently answered item concerned the “reason for choosing a respirator”, and the least frequently answered ones were “meaning of warning” and “meaning of hazard”. Environmental hazard and oxidation risk received the most frequent correct answers in the visual section. After the intervention, the control and experimental groups’ knowledge scores were 64.7 ± 10.1 and 79.2 ± 8.4, respectively. According to the covariance test, the training intervention improved participants’ knowledge significantly (p < 0.001). Training interventions through social media can improve GHS knowledge. Moreover, visual messages are easier to understand and transfer than textual ones.