Inclusion Of Human Factors Research Articles

Factors contributing towards effective maritime simulator training: a systematic literature review

ABSTRACT Training is essential in every industry, and the maritime industry is no exception. Effective training plays a crucial role in ensuring the safety and efficiency of maritime operations. The current study is a systematic literature review examining the factors contributing to effective maritime simulator training. The study identifies key factors that influence the success of simulator training programs, including the quality of training materials, the competency of trainers, the use of realistic scenarios, and the frequency of training sessions. Other important factors include the use of feedback mechanisms, the integration of new technologies, and the inclusion of human factors in the training curriculum. Considering all these factors, the study highlights the need for a comprehensive and integrated approach to maritime simulator training. It also suggests that using advanced simulation technologies, such as virtual reality and artificial intelligence, can enhance the effectiveness of simulator training programs. Overall, this systematic literature review provides valuable insights into the factors contributing to effective maritime simulator training and offers recommendations for improving the design and implementation of such programs.

Ranking of occupational health and safety risks by a multi-criteria perspective: Inclusion of human factors and application of VIKOR

Nowadays, the Occupational Health and Safety (OHS) is more and more recognized as a crucial process to be properly managed and continuously improved by every organization. Primarily addressed to prevent workers’ injuries and diseases, it positively impacts on productivity, competitiveness and reputation as well as it contributes to cost savings in general. OHS management is grounded upon the risk assessment results, on the basis of which defining corrective measures to be taken to reduce risks to acceptable values. In this regard, the paper proposes a Multi-Criteria Decision Making (MCDM) based methodology addressed to the occupational risks prioritization. In order to overcome the shortcomings of the traditional risk matrix, the new methodology considers three evaluation criteria differently weighted by the Analytic Hierarchy Process (AHP) method. Among criteria, one specifically refers to the contribution of human factors to the occurrence of accidents, and a combination of Human Error Assessment and Reduction Technique (HEART) and Standardized Plant Analysis Risk-Human (SPAR-H) techniques is used to this aim. Afterwards, the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method is applied to rank risks. The proposed approach is implemented in a Sicilian manufacturing company that produces and commercializes wooden products, together with the sale of outdoor furniture. The comparison of the obtained results with the ones arising from previous risk analyses performed by the company highlights the higher ability of the proposed methodology to differentiate risks.

Machine Learning-Based Cognitive Position and Force Controls for Power-Assisted Human–Robot Collaborative Manipulation

Manipulation of heavy objects in industries is very necessary, but manual manipulation is tedious, adversely affects a worker’s health and safety, and reduces efficiency. On the contrary, autonomous robots are not flexible to manipulate heavy objects. Hence, we proposed human–robot systems, such as power assist systems, to manipulate heavy objects in industries. Again, the selection of appropriate control methods as well as inclusion of human factors in the controls is important to make the systems human friendly. However, existing power assist systems do not address these issues properly. Hence, we present a 1-DoF (degree of freedom) testbed power assist robotic system for lifting different objects. We also included a human factor, such as weight perception (a cognitive cue), in the robotic system dynamics and derived several position and force control strategies/methods for the system based on the human-centric dynamics. We developed a reinforcement learning method to predict the control parameters producing the best/optimal control performance. We also derived a novel adaptive control algorithm based on human characteristics. We experimentally evaluated those control methods and compared the system performance between the control methods. Results showed that both position and force controls produced satisfactory performance, but the position control produced significantly better performance than the force controls. We then proposed using the results to design control methods for power assist robotic systems for handling large and heavy materials and objects in various industries, which may improve human–robot interactions (HRIs) and system performance.

Evaluation of Instance-Based Learning and Q-Learning Algorithms in Dynamic Environments

Reinforcement learning is an unsupervised learning algorithm, where learning is based upon feedback from the environment. Prior research has proposed cognitive (e.g., Instance-based Learning or IBL) and statistical (Q-learning) reinforcement learning algorithms. However, an evaluation of these algorithms in a single dynamic environment has not been explored. In this paper, a comparison between the statistical Q-learning algorithm and the cognitive IBL algorithm is presented. A well-known environment, “Frozen Lake,” is used to train, generalize, and scale Q-learning and IBL algorithms. For generalizing, the Q-learning and IBL agents were trained on one version of the Frozen Lake and tested on a permuted version of the same environment. For scaling, the two algorithms were tested on a larger version of the Frozen Lake environment. Results revealed that the IBL algorithm used less training time and generalized better to different environment variants. The IBL algorithm was also able to show scalability by retaining its superior performance in the larger environment. These results indicate that the IBL algorithm could be proposed as an alternative to the standard reinforcement learning algorithms based on dynamic programming such as Q-learning. The inclusion of human factors (such as memory) in the IBL algorithm makes it suitable for robust learning in complex and dynamic environments.

Incorporating human factors into LCM using fuzzy TCI model

ABSTRACT Incorporation of Human Factors (HF) into the mathematical car-following (CF) models has always been the research hotspot. Ignorance of such inclusion would inevitably hinder us from acquiring a comprehensive understanding of traffic flow phenomena. This paper proposed a novel CF model in order to bridge three existing research gaps: the demand for more inclusion of HF into the Longitudinal Control Model (LCM); the requirement for a more desirable underlying CF model for the Task Capability Interface (TCI) model; the ignorance of the fuzziness of human brains when modeling Task Difficulty (TD). Specifically, in order to imitate driver’s natural or subjective uncertainty and ambiguity of his TD, the fuzzy logic approach is introduced, and the TD is then incorporated into the LCM. Thereafter, both numerical simulation and field-data validation have been performed. Results indicate that our proposed model is more capable of accommodating HF and exhibits better performance than its predecessor.

How to include User eXperience in the design of Human-Robot Interaction

• Inclusion of human factors in the design of Human-Robot collaborative interfaces • UX-based approach to include human factors in human-robot interface design and practically support user-centred design • Set of UX specific tools to collect user requirements and support the design of human-robot interfaces • Validation on an industrial case study focusing on collaborative assembly In recent years Human-Robot Collaboration (HRC) has become a strategic research field, considering the emergent need for common collaborative execution of manufacturing tasks, shared between humans and robots within the modern factories. However, the majority of the research focuses on the technological aspects and enabling technologies, mainly directing to the robotic side, and usually neglecting the human factors. This work deals with including the needs of the humans interacting with robots in the design in human-robot interaction (HRI). In particular, the paper proposes a user experience (UX)-oriented structured method to investigate the human-robot dialogue to map the interaction with robots during the execution of shared tasks, and to finally elicit the requirements for the design of valuable HRI. The research adopted the proposed method to an industrial case focused on assembly operations supported by collaborative robots and AGVs (Automated Guided Vehicles). A multidisciplinary team was created to map the HRI for the specific case with the final aim to define the requirements for the design of the system interfaces. The novelty of the proposed approach is the inclusion of typically interaction design tools focusing in the analysis of the UX into the design of the system components, without merely focusing on the technological issues. Experimental results highlighted the validity of the proposed method to identify the interaction needs and to drive the interface design.

A generic multi-scale framework for microscopic traffic simulation part II – Anticipation Reliance as compensation mechanism for potential task overload

The inclusion of human factors (HF) in mathematical models is proving crucial to allow complex driving behaviour and interactions to be explicitly considered to capture driving phenomena. An important area where such integration is required is for the role of anticipation by drivers to compensate for critical traffic situations. In this paper, we introduce the concept of Anticipation Reliance (AR), which acts as a demand lowering compensative effect for the driving task by relying more on anticipation. We implement AR in a generic multi-scale microscopic traffic modelling and simulation framework to explore and explain the effects of HF on traffic operations and safety in critical traffic situations. This concept addresses a disparity in the description of driver workload in relation to the execution of driving tasks in regard to the confidence that drivers place on tasks that are sub-consciously catered for. The crossover from HF to a mathematical description of this role of AR introduces a ground-breaking concept that explains and models the mechanisms that allow drivers to compensate and avoid accidents in many circumstances, even when driving errors or sub-optimal driving performance occurs. By and large, the HF effects can be subdivided in effects on perception and anticipation; effects on sensitivity and response to stimuli; and effects on personal attributes and characteristics. A key aspect of the framework are two intertwined driver-specific mental state variables—total workload and awareness—that bridge between classic collision-free idealized models for lane changing and car following, and HF models that explain under which conditions the performance of drivers deteriorates in terms of reaction time, sensitivity to stimuli or other parameters. In this paper, we focus on the awareness construct, as described by AR, and explore the effects. We prove the effectiveness of the approach with a case example that demonstrates the ability of the model to dissect a complex traffic situation with both longitudinal and lateral driving tasks, while endogenously considering human factors and that produces accident avoidance and occurrence within the same order of magnitude compared to real traffic accident statistics.

Improving Patient Safety through Human-factor-based Risk Management

National and international efforts under the initiative ‘patient safety’ aim for more safety and transparency within healthcare systems for both patients and professionals. Within the healthcare sector, workflows become more and more complex, while time and money become scarce. As the consequence, the risk awareness, fault management and quality aspects in general become more important. One of the most established risk assessment methods is Failure Mode and Effect Analysis (FMEA) – a reliability analysis and risk assessment tool widely used in various industries. The traditional FMEA is using a Risk Priority Number (RPN) ranking system to evaluate and identify the risk level of failures, and to prioritize actions. However, there are shortcomings in obtaining a quality estimate of the failure ratings with FMEA, especially when human factors play a role, as it is in healthcare. Thus, a new risk assessment method named HFdFMEA (Human Factor dependent FMEA) based on dependency of used parameters and observation of human factors, is proposed to address these drawbacks. The results of this paper show that the HFdFMEA does not only increase risk level of failures based on the inclusion of human-factors but also gives the possibility to reduce the risk level of failures through means of addressing human-factors via trainings, motivation, etc. Finally, we discuss the opportunity to improve patient safety as result of the proposed HFdFMEA, used as technique for Human-Factor-based Risk Management (RiDeM).

Improving integration of human resources into quality management system standards

Purpose– Little attention has been paid to how quality management systems (QMSs) are optimized by supportive employee behavior. The purpose of this paper is to provide a critical review of the literature on the inclusion of human factors in the ISO 9000 family of standards, identify deficiencies in the standard's current treatment of these issues, and offer recommendations on how human resources (HRs) can be better integrated into these business management standards.Design/methodology/approach– This concept paper presents a survey of both academic and practitioner literature on the topic of HR and its treatment in quality standards. The focus is restricted to consideration of human factors that are specifically identified in the ISO 9001:2008 and ISO 9004:2009 standards.Findings– ISO 9001 and 9004 include some HR topics, but their treatment is insufficient to meet the demands of today's business environment. The recent addition to the ISO 9000 family,ISO 10018 – Quality Management – Guidelines on People Involvement and Competence(ISO, 2012b) will help to address the deficiency if adopted by the marketplace. To improve the usefulness of ISO 9000 standards, the breadth of human factors should be enhanced both extensively to include components of workplace culture and work design and intensively to require more rigorous treatment of the HR considerations already included in the standards.Practical implications– There is a need for more comprehensive consideration of human contributions to quality if organizations are to optimize the value they receive from their HR and their investment on the ISO 9001 QMS.Originality/value– The limited references linking HR and the ISO 9000 series of standards have focussed upon how human factors contribute (or not) to the successful use of the ISO 9000 standards. In contrast, this paper offers a comprehensive and integrative examination of how the ISO 9000 QMS standards could more comprehensively and effectively incorporate HR into a firm's practices.

The cognitive approach to the creation of ontology

The paper deals with the inclusion of human factors in the development of intellectual systems based on achievements in cognitive psychology ranging from the heuristic Gestalt principle to modern perspectives of psychosemantics. It contends the importance of secondary factors in the expert’s model of the world, primarily the axiological and aesthetic models, as conveying holism and balance to the model of the world. Practical advice for designing ontology in soft object domains ensuing from the suggested approach is to maximize the use of an expert’s specific holistic view of the given domain in addition to more effective engineering applications of this ontology.

The impact of team and work characteristics on team functioning

In this article, the authors seek to strengthen the theoretical foundation of team and cell formation through the inclusion of human factors. They distinguish three types of team characteristics: global, shared, and compositional attributes. In this last category, they also deal with diversity in teams by addressing three types of variables: demographic characteristics, skills and knowledge, and personality traits. In addition, the authors address the relevance of the structure of diversity in cells. For each of the above-mentioned groups of team characteristics, their potential impact on team functioning is worked out and discussed with simultaneous consideration of the characteristics of the team task. Three industrial examples are included to illustrate the importance of including worker, team, and work characteristics when designing manufacturing cells. (c) 2006 Wiley Periodicals, Inc.