OBJECTIVES: This paper discusses activity detection and analysis (ADA) using security robots in workplaces. The application scenario of this method relies on processing image and sensor data for event and activity detection. The events that are detected are classified for its abnormality based on the analysis performed using the sensor and image data operated using a convolution neural network. This method aims to improve the accuracy of detection by mitigating the deviations that are classified in different levels of the convolution process.
RESULTS: The differences are identified based on independent data correlation and information processing. The performance of the proposed method is verified for the three human activities, such as standing, walking, and running, as detected using the images and sensor dataset.
CONCLUSION: The results are compared with the existing method for metrics accuracy, classification time, and recall.
Methods: A multicentre cross-sectional study was conducted in 12 government hospitals accredited for housemanship training within the central zone of Malaysia. The study included a total of 1,074 house officers who had been working for at least 6 months in various housemanship rotations. The Negative Acts Questionnaire-Revised (NAQ-R) was used to examine workplace bullying.
Results: The 6-month prevalence of workplace bullying among study participants was 13%. Work-related bullying such as 'being ordered to do work below your level of competence', person-related bullying such as 'being humiliated or ridiculed in connection with your work', and physically intimidating bullying such as 'being shouted at or being the target of spontaneous anger' were commonly reported by study participants. Medical officers were reported to be the commonest perpetrators of negative actions at the workplace. Study participants who graduated from Eastern European medical schools (adjusted odds ratio [AOR] 2.27; 95% confidence interval [CI]: 1.27, 4.07) and worked in surgical-based rotation (AOR 1.83; 95% CI: 1.13, 2.97) had higher odds of bullying compared to those who graduated from local medical schools and worked in medical-based rotation, whereas study participants with good English proficiency (AOR 0.14; 95% CI: 0.02, 0.94) had lower odds of bullying compared to those with poor English proficiency.
Conclusion: The present study shows that workplace bullying is prevalent among Malaysian junior doctors. Considering the gravity of its consequences, impactful strategies should be developed and implemented promptly in order to tackle this serious occupational hazard.
OBJECTIVES: In this manuscript, the Robotic Facial Recognition System using the Compound Classifier (RERS-CC) is introduced to improve the recognition rate of human faces. The process is differentiated into classification, detection, and recognition phases that employ principal component analysis based learning. In this learning process, the errors in image processing based on the extracted different features are used for error classification and accuracy improvements.
RESULTS: The performance of the proposed RERS-CC is validated experimentally using the input image dataset in MATLAB tool. The performance results show that the proposed method improves detection and recognition accuracy with fewer errors and processing time.
CONCLUSION: The input image is processed with the knowledge of the features and errors that are observed with different orientations and time instances. With the help of matching dataset and the similarity index verification, the proposed method identifies precise human face with augmented true positives and recognition rate.
METHODS: Administrative and field workers from different divisions across Malaysia's six regions were randomly sampled to collect data for this study. The workers were given a total of 500 questionnaires, of which 345 were returned to the team of researchers. Based on the data analysis, there is an effective interaction between the factors tested toward safety performance.
RESULTS: According to findings, psychological capital positively and significantly affected workers' work engagement. Also, work engagement greatly impacted both workers' safety performance outcomes. Also, as expected, worker pressure significantly and negatively affected workers' safety performance.
DISCUSSION: Insights gained from this research have helped us better organize work and involve employees in safety activities/policies to boost workplace safety performance. The study also suggested that firms should reduce their employees' workloads because doing so would not lower their Psychological Capital but would instead fortify them to better carry out their duties in a risk-free manner.
OBJECTIVE: The purpose of this study is to understand the relationship between the different variables associated with fatal falls from heights, which will help identify potential areas to work on to prevent these types of injuries.
METHODS: The study analyzed 3,321 fatal falls from height accidents from 2010 to 2020 DOSH data. Data were cleaned and normalized to extract relevant information for analysis, with agreement on variables and reliability achieved through independent sampling.
RESULTS: This study found that general workers were the most vulnerable category to fatal falls, with a 32% yearly average, whereas supervisors were the least vulnerable, with 4%. Roofers recorded a yearly fatal falls average of 15.5%, followed by electricians with 12%. Cramer's V results ranged from negligible, weak, and strong correlations; strong to moderate correlation between the dates of injuries and the factors used in this study, whereas the direct and root causes recorded a weak to negligible correlation with the rest of the variables.
CONCLUSIONS: This study provided a better understanding of the working conditions of the Malaysian construction industry. By analyzing fall injury patterns and uncovering the factors, direct and root causes relationship with other variables, it was clear how severe the Malaysian workplace conditions were.
PRACTICAL APPLICATIONS: This study will help better understand fatal fall injuries in the Malaysian construction industry and help develop prevention measures based on the uncovered patterns and associations.
Methods: This project designed a safety vest incorporated with metal detectors which can provide immediate warning to the field workers when there is metal hazard around. This product has greater freedom of design via smart manufacturing as it involves the assembly of few commercially available parts into a single entity. Briefly, the metal detector is a do it yourself (DIY) kit, and the safety vest is purchasable from any local market. The DIY kit was connected to a copper coil and being sewed into the safety vest.
Results: The metal detector induces beeping sound when there is metal hazard around. A total of 121 engineering students were introduced to the prototype before being requested to answer a survey associated with the design. Respondents have rated >3.00/5.00 for the design simplicity, ease of usage, and light weight. Meanwhile, respondents suggested that the design should be further improved by increasing the metal detection range.
Conclusion: It is envisaged that the introduction of this smart safety vest will allow the workers to carry out their duties securely by reducing the accident rates. Particularly, such design is expected to reduce workplace accident especially during night time at construction sites where the visibility is low.
OBJECTIVES: In this paper, the Advanced Human-Robot Collaboration Model (AHRCM) approach is to enhance the risk assessment and to make the workplace involving security robots. The robots use perception cameras and generate scene diagrams for semantic depictions of their environment. Furthermore, Artificial Intelligence (AI) and Information and Communication Technology (ICT) have utilized to develop a highly protected security robot based risk management system in the workplace.
RESULTS: The experimental results show that the proposed AHRCM method achieves high performance in human-robot mutual adaption and reduce the risk.
CONCLUSION: Through an experiment in the field of human subjects, demonstrated that policies based on the proposed model improved the efficiency of the human-robot team significantly compared with policies assuming complete human-robot adaptation.
OBJECTIVES: This paper discusses RISAPI of our original work in the field, which shows how probabilistic planning and system theory algorithms in workplace robotic systems that work with people can allow for that reasoning using a security robot system. The problem is a general way as an incomplete knowledge 2-player game.
RESULTS: In this general framework, the various hypotheses and these contribute to thrilling and complex robot behavior through real-time interaction, which transforms actual human subjects into a spectrum of production systems, robots, and care facilities.
CONCLUSION: The models of the internal human situation, in which robots can be designed efficiently, are limited, and achieve optimal computational intractability in large, high-dimensional spaces. To achieve this, versatile, lightweight portrayals of the human inner state and modern algorithms offer great hope for reasoning.