A Semi-Supervised Learning-Aided Evolutionary Approach to Occupational Safety Improvement

Worldwide, four people die every minute as a consequence of illnesses and accidents at work. This considerable number makes occupational safety an important research area aimed at obtaining safer and safer workplaces. This paper presents a semi-supervised learning-aided evolutionary approach to improve occupational safety by classifying workers depending on their own risk perception for the task assigned. More in detail, a semi-supervised learning phase is carried out to initialize a good population of a non-dominated sorting genetic algorithm (NSGA-II). Each chromosome of the population represents a pair of classifiers: one determines a worker's risk perception with respect to a task, the other determines the level of caution of the same worker for the same task. Learning from constraints reinforces the initial training performance. The best Pareto-optimal solution to the problem is selected by means of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The proposed framework was tested on real-world data gathered through a website purposely developed. Results showed a good performance of the obtained classifiers, thus validating the effectiveness of the proposed approach in supporting the decision-maker in critical job assignment problems, where risks are a serious threat to the workers' health

An Integrated Optimization System for Safe Job Assignment Based on Human Factors and Behavior

Industrial safety has been deeply improved in the past years, thanks to increasingly sophisticated technologies. Nevertheless, 2.3 million people yearly die worldwide due to occupational illnesses and accidents at work. Human factors can be profitably used for safety improvement because of their influence on the workers’ behavior. This paper presents an integrated optimization system to help companies assign each task to the most suitable worker, minimizing cost, while maximizing expertise and safety. The system is made of three modules. A neural module computes each worker’s caution for every task on the basis of some human factors and the worker’s behavior. To solve the multiobjective job assignment problem, an evolutionary module approximates the Pareto front through the nondominated sorting genetic algorithm II. Pareto-optimal solutions then form the alternatives of a multicriteria decision-making problem, and the best is selected by a decision module jointly based on the analytic hierarchy process and the technique for order of preference by similarity to ideal solution. Validation was carried out involving two footwear companies, where personnel was recruited and reassigned to tasks, respectively. Comparing the worker-task assignment proposed by the system to the one suggested/used by the management, noteworthy low-cost improvement in safety is shown in both scenarios, with low or no decrease in expertise. The proposed system can, thus, contribute to get safer workplaces where risks are less likely and/or less harmful

Artificial bee colony optimization to reallocate personnel to tasks improving workplace safety

Worldwide, just under 5,800 people go to work every day and do not return because they die on the job. The groundbreaking Industry 4.0 paradigm includes innovative approaches to improve the safety in the workplace, but Small and Medium Enterprises (SMEs)—which represent 99% of the companies in the EU—are often unprepared to the high costs for safety. A cost-effective way to improve the level of safety in SMEs may be to just reassign employees to tasks, and assign hazardous tasks to the more cautious employees. This paper presents a multi-objective approach to reallocate the personnel of a company to the tasks in order to maximize the workplace safety, while minimizing the cost, and the time to learn the new tasks assigned. Pareto-optimal reallocations are first generated using the Non-dominated Sorting artificial Bee Colony (NSBC) algorithm, and the best one is then selected using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The approach was tested in two SMEs with 11 and 25 employees, respectively

Human Factors-Based Many-Objective Personnel Recruitment for Safety-Critical Work Environments

In spite of many improvements in industrial safety of the last decades, nowadays four people per minute die in the world for occupational illnesses and accidents at work. Besides equipping machines with the most advanced technologies, industrial safety has become more and more interested in human factors in recent years, since many accidents at work are proven to be blamed on dangerous behaviours of workers. Recruiting workers with proper risk perception and caution can increase how safely they will deal with the task assigned, thus reducing devastating events. This paper presents a many-objective optimization framework for personnel recruitment in safety-critical work environments. Four objectives are considered: cost and learning time (which are minimized), and risk perception and caution (which are maximized). A neural network-based module computes each candidate’s risk perception and caution for every single task he/she applies for. Pareto optimal solutions are generated using the Multi-Objective Particle Swarm Optimizer based on hypervolume (MOPSOhv). The best personnel recruitment is selected by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The effectiveness of the proposed framework was validated on two real-world recruitment processes involving 100 and 300 candidates, respectively

Brain informed transfer learning for categorizing construction hazards

A transfer learning paradigm is proposed for "knowledge" transfer between the human brain and convolutional neural network (CNN) for a construction hazard categorization task. Participants' brain activities are recorded using electroencephalogram (EEG) measurements when viewing the same images (target dataset) as the CNN. The CNN is pretrained on the EEG data and then fine-tuned on the construction scene images. The results reveal that the EEG-pretrained CNN achieves a 9 % higher accuracy compared with a network with same architecture but randomly initialized parameters on a three-class classification task. Brain activity from the left frontal cortex exhibits the highest performance gains, thus indicating high-level cognitive processing during hazard recognition. This work is a step toward improving machine learning algorithms by learning from human-brain signals recorded via a commercially available brain-computer interface. More generalized visual recognition systems can be effectively developed based on this approach of "keep human in the loop"

Critical thinking and clinical reasoning in new graduate occupational therapists: a phenomenological study.

The aim of this study was to examine, understand and conceptualise the critical thinking and clinical reasoning adopted by new graduate occupational therapists as they enter the workforce to become newly autonomous practitioners. The study obtained the perspectives of new graduates, their supervisors and service managers on the means by which critical thinking and clinical reasoning develop to meet the expectations of employers. Factors which impeded the transition between new graduate and autonomous practitioner were identified and explored. Ethical approval was obtained to conduct the study. The study adopted a qualitative phenomenological research approach; Interpretative Phenomenological Analysis (IPA), which informed framing, data gathering and analysis. Semi-structured interviews were conducted with new graduates (n=6), supervisors (n=7) and managers (n=7) from multiple sites within one National Health Service Board. Interviews were transcribed verbatim from audio-recordings. The findings indicate that new graduates are expected to develop critical thinking and clinical reasoning in a manner that might challenge traditional conceptualisations of the transitioning process. A phenomenon, historically named the shock of practice, was reflected on by therapists in each phase of the study and adaptive and mal-adaptive responses to this in the thinking and behaviour of new graduates was identified. The clinical supervisor-supervisee relationship appeared to be the key source of support, and the supervisor the most significant knowledge resource, for new graduates. This relationship was supplemented by both peer support and Preceptorship. Discharge planning was a significant source of anxiety and development of an algorithm to support this process is proposed. Recommendations for further research and theoretical implications for practice and undergraduate education are discussed

Undergraduate and Graduate Course Descriptions, 2016 Fall

Wright State University undergraduate and graduate course descriptions from Fall 2016