Smart All-Hazard Responses Framework (SARF): Human Factors and Ergonomics Approach

Abstract

The integration of Human Factors and Ergonomics (HFE) into emergency responses is crucial due to frequent human errors and inadequate human-system interactions which impede effective emergency and disaster management. Therefore, this research aims to develop and validate the Smart All-hazards Response Framework (SARF) that systematically incorporates HFE principles across all aspects of disaster management, from planning through post-response analysis. The framework focuses on reducing human errors, enhancing human-system interactions, and boosting system resilience. The research methodology includes a detailed literature review, the development of the HFE-integrated framework, and rigorous testing through experimental studies and simulations across various scenarios. Key questions address the integration of HFE to optimize system performance and the impact of ergonomic interventions on responder safety in high-risk environments. By advancing a holistic, human-centered approach to emergency and disaster management, this study aims to significantly enhance the effectiveness, safety, and adaptability of responses to all kinds of hazards.

Keywords

emergency disaster smart technology safety AI

Introduction

In the evolving landscape of today’s emergencies and disasters, the integration of Human Factors and Ergonomics (HFE) into management of all kinds of incidents, or “all-hazards approach” is of paramount importance. The necessity of the all-hazards arises from frequent observations that human errors, underutilization of ergonomic principles, and inadequate consideration of human-system interactions significantly hamper the effectiveness of emergency and disaster management. Thus, these observations underscore the need for a comprehensive approach that embeds HFE into the design and implementation of emergency and disaster management systems to improve safety, efficiency, and resilience.

The relevant background for this research derives from the acknowledgment that various hazards—ranging from natural to anthropogenic and technological—pose complex challenges that require sophisticated, adaptable, and human-centered strategies. Studies such as Sasangohar et al. (2020) highlighted the significance of incorporating ergonomic considerations in COVID-19 pandemic response efforts. Similarly, Nicoletti and Padovano (2019) underscored the critical role of ergonomic principles in managing emergency responses in industry, thereby emphasizing the broad applicability of HFE across different types of hazards. Recently, a recent study provided the “Disaster Ergonomics” framework that explains the roles of HFE along the lifecycle of disaster management (Son, 2023).

Although there have been several frameworks for the emergency and disaster management domain, several limitations still exist. These frameworks lack specificity for individual and team level implementation. Especially, the design and use of smart technologies (e.g., artificial intelligence [AI], unmanned aerial vehicle [UAV]) was not adequately considered in the human-system integration. To address this knowledge gap, the objective of the current study is to develop a detailed framework that systematically integrates HFE principles into all-hazards incident management, from planning and training to execution and post-event analysis. Our framework aims to address the current gaps in emergency and disaster management strategies by focusing on optimizing human-system interactions, minimizing human errors, and enhancing overall system resilience.

To achieve these desired outcomes, this research follows a clear roadmap that includes a literature review to establish a theoretical foundation, followed by the development of the HFE-integrated framework. Subsequent phases involve the design of experimental studies and simulations to test the framework under various incident scenarios, and the analysis of these studies to refine and validate the framework. Key research questions (research aims) guiding this investigation include:

How can HFE principles be effectively integrated into all-hazards strategies to minimize human errors and optimize system performance?

What are the impacts of HFE interventions on the effectiveness and safety of emergency and disaster management systems in high-stress and high-risk environments?

How do human-system interactions in disaster management influence the resilience and adaptability of emergency and disaster management strategies in the face of various kinds of hazards?

Current Frameworks and Issues

Our literature review revealed a nuanced approach toward integrating HFE principles in managing hazards. The analysis identifies several key characteristics, strengths, and weaknesses associated with the emergency and disaster management frameworks.

It was found that recent studies tried to have integrated technology, adaptability, flexibility, and automatic risk assessment feature (Sasangohar et al., 2020; Son, 2023; Son et al., 2020). In these works, the incorporation of technologies to simulate, analyze, and improve human-system interactions was emphasized for proactive and preventive management of hazards. There is a growing focus on creating adaptable and flexible systems that can be reconfigured according to human ergonomic needs in recent disasters (Moon et al., 2024; Son et al., 2023). These frameworks are becoming more tailored to specific environments, such as offshore installations, highlighting the need for industry-specific hazard management strategies.

In detail, there were four types of frameworks. The first type focused on the overall flow to have safety and improved effectiveness of the emergency and disaster management. The framework proposed by Gucci et al. (2019) focused on macro-ergonomic factors to identify potential hazards in industries, emphasizing a holistic view of human-work system interactions. Petrosoniak et al. (2017) offered a framework based on human factors for improving patient safety in trauma resuscitation, showcasing the application of HFE in healthcare emergency responses. Son (2023) also emphasized the overall flow of the framework with preparedness, response, recovery, and mitigation. The second type of the frameworks emphasized more on the importance of human components in the emergency and disaster management. Demirel (2020) introduced a framework in which digital human models are incorporated in the system design and assessment, aiming to enhance the interaction between humans and complex systems in virtual environments. This approach allows for the early identification of potential ergonomic and safety issues. As a participatory framework, Morag and Luria (2013) proposed another ergonomics approach for workplace hazard and risk analysis, stressing employee involvement in identifying and mitigating risks. The third type of emergency and disaster management frameworks were focused on hazards of specific industries such as healthcare and manufacturing. Bortolini et al. (2020) discussed the inclusion of safety, ergonomics, and human factors in designing reconfigurable manufacturing systems. This framework highlights the adaptability of manufacturing environments to human needs, ensuring safety and efficiency. Norazahar and Ahmad (2018) developed a framework assessing risks associated with human responses during emergency escapes on offshore installations, addressing the unique challenges of evacuation in high-risk environments. Nickel et al. (2020) emphasized designing machinery and systems for improved safety, focusing on human-system interaction requirements to prevent accidents. Lastly, there was a framework focused on regulatory exercises. Wilkinson (2016)’s research outlines the current regulatory and governmental focus on integrating HFE into safety standards and practices, highlighting the importance of legal and procedural adherence.

There are several limitations on current frameworks. First, they have implementation complexity and lack the specificity. The complexity of integrating HFE principles into existing systems can be a significant barrier, requiring substantial time and resource investment. Some frameworks (Petrosoniak et al., 2017; Son, 2023) lack the specificity needed to address unique hazards in certain industries, leading to generic solutions that may not be optimally effective. Secondly, they did not involve a plan of having advanced technologies, especially for AI, with training and adaptiveness issues (Carayon et al., 2014; Duffy, 2016; Endsley, 2017; Morag & Luria, 2013). Effective implementation often requires extensive training and awareness programs, which can be challenging to execute across large organizations. While adaptable systems offer significant benefits, their complexity poses challenges in implementation and requires extensive training.

Proposed Framework

Smart All-Hazard Response Framework (SARF)

Based on the limitations in the current frameworks and insights gathered from the literature review, a new framework is proposed: Smart All-hazards Response Framework (SARF) (Figure 1). The SARF aims to leverage the strengths of previous approaches while addressing their limitations through the integration of advanced and smart technologies.

Figure 1.

Smart all-hazard responses framework (SARF).

AI-Driven Risk Assessment

The SARF utilizes AI to continuously analyze data from various sources (sensors, user inputs, environmental conditions) to identify potential hazards in real-time (Carayon et al., 2014). It also incorporates predictive analytics to forecast potential ergonomic and safety issues before they manifest, allowing for proactive measures. There are two types of the risk assessments. The first one is “narrow,” which has augmented reality (AR)-goggle based specific point-risk assessment (e.g., a room), to be able to provide experience of having real world and virtual world (Park et al., 2023). The second one is “broad,” which entails digital-twin (e.g., a building) concept to have team training.

Digital Human Modeling (DHM)

DHM employs simulations to evaluate human-system interactions in virtual environments, assessing ergonomic impacts of proposed solutions, which quantify and predict metrics such as physical and cognitive workload (Park et al., 2020), situation awareness (Endsley, 2017), and task performance (Park & Zahabi, 2022). Furthermore, the SARF utilizes the established models and formulations in human performance modeling (HPM) domain, to enable top-down or theory-based approach (Park, 2023; Park et al., 2020; Park & Zahabi, 2024). That is, a hybrid modeling approach is generated with the data-driven approach with AI and theory-driven approach with HPM. This is the core component of the SARF, as it incorporates HFE principles with bottom-up and top-down approaches. Integrating DHM with AI provides potential capability to optimize design and layout of workplaces and emergency and disaster management plans (Duffy, 2016).

Adaptive Response Mechanisms

To be able to have life-long performance, it is required to develop AI-based systems that are capable of adapting to the changing nature of hazards and human ergonomic needs (Endsley, 2017; Son et al., 2020). This includes mechanisms for reconfiguring manufacturing systems or emergency evacuation plans dynamically in response to AI-driven risk assessments.

Team Dynamics Modeling

Team dynamics modeling has its origin from the individual DHM, which means the metrics in individual level and team level need to be shared. Here, the SARF uses natural language processing (NLP) to analyze feedback and suggestions for continuous improvement of the emergency and disaster management system, with participatory ergonomics approach (Burgess-Limerick, 2018). Furthermore, it is essential to implement quantifiable logics for measuring team knowledge and situation awareness level (Cooke et al., 2000; Gorman et al., 2017; Dos Santos & Son, 2024).

Integrated Training Programs

Lastly, it is necessary to incorporate VR and AR training modules powered by AI to simulate emergency scenarios and best ergonomic practices. In detail, it is required to customize training contents based on AI analysis of individual learning needs and ergonomic risk factors (Iván Aguilar Reyes et al., 2022; Zahabi & Abdul Razak, 2020).

Discussion

The SARF was meticulously designed to address the three pivotal research aims (RAs) previously mentioned in the introduction, each focusing on integrating HFE with smart technologies in all-hazard emergency and disaster management efforts.

RA1-The SARF integrates HFE principles primarily through digital human modeling (DHM) and participative ergonomic Input components (Team Dynamics Modeling). Hybrid modeling based DHM uses advanced simulations to understand and optimize human-system interactions, ensuring ergonomic considerations are integrated from the design phase. Participative ergonomic approaches also allow for the incorporation of feedback and insights from end-users and stakeholders, ensuring the system is aligned with human needs and capabilities. This holistic approach ensures that HFE principles are embedded in the emergency and disaster management system, minimizing human errors, and optimizing performance.

RA2-The SARF emphasizes “Adaptive Response Mechanisms” and “Integrated Training Programs” to directly address this question. “Adaptive Response Mechanisms” ensure that emergency and disaster management strategies are dynamically adjusted based on real-time risk assessments and ergonomic considerations, enhancing safety and efficiency even in high-stress environments. “Integrated Training Programs,” tailored based on insights from AI-driven risk assessments and DHM, equip emergency response teams with the knowledge and skills needed to effectively navigate high-risk situations, thereby improving overall safety and efficiency.

RA3-The core of the SARF, powered by AI-driven risk assessment, encapsulates the essence of enhancing resilience and adaptability in emergency and disaster management. By continuously analyzing data from various sources for potential hazards and predicting future risks, the system ensures that human-system interactions are optimized for current conditions, thereby enhancing the resilience of response strategies. Furthermore, the adaptive nature of the response mechanisms, informed by ongoing DHM analyses and participative ergonomic feedback, ensures that the system can evolve in response to new information or changing hazard landscapes, maintaining its effectiveness over time.

In sum, not only does the SARF optimize human-system interactions for improved safety and efficiency, but also it ensures that. the system remains adaptable and resilient in the face of evolving hazards and challenges, with advanced and smart technologies. The successful and realistic implementation and execution of the SARF depends on the cohesiveness and timeliness of the data flow and quality of the AI algorithms. As the current study provides a roadmap for the next studies, more efforts are needed to implement individual goals for each component in SARF.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Junho Park

References

Bortolini

Botti

Galizia

Mora

(2020). Safety, ergonomics and human factors in reconfigurable manufacturing systems. Reconfigurable Manufacturing Systems: From Design to Implementation, 10, 123–138.

Burgess-Limerick

(2018). Participatory ergonomics: Evidence and implementation lessons. Applied ergonomics, 68, 289–293.

Carayon

Wetterneck

T. B.

Rivera-Rodriguez

A. J.

Hundt

A. S.

Hoonakker

Holden

Gurses

A. P.

(2014). Human factors systems approach to healthcare quality and patient safety. Applied Ergonomics, 45(1), 14–25.

Cooke

N. J.

Salas

Cannon-Bowers

J. A.

Stout

R. J.

(2000). Measuring team knowledge. Human Factors, 42(1), 151–173.

Demirel

H. O

. (2020). Digital human-in-the-loop framework. In digital human modeling and applications in health, safety, ergonomics and risk management. Posture, motion and health: 11th international conference, DHM 2020, held as part of the 22nd HCI international conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part I 22 (pp. 18–32). Springer International Publishing.

Dos Santos

Son

. (2024). Identifying firefighters’ situation awareness requirements for fire and non-fire emergencies using a goal-directed task analysis. Applied Ergonomics, 114, 104136. https://doi.org/10.1016/j.apergo.2023.104136

Duffy

V. G.

(2016). Handbook of digital human modeling: research for applied ergonomics and human factors engineering. CRC press.

Endsley

M. R.

(2017). From here to autonomy: Lessons learned from human–automation research. Human Factors, 59(1), 5–27.

Gorman

J. C.

Cooke

N. J.

Winner

J. L.

(2017). Measuring team situation awareness in decentralized command and control environments. In Situational awareness (pp. 183–196). Routledge.

10.

Gucci

D. O. D. R.

Zadry

H. R.

Jumeno

(2019). Framework assessment of the potential hazards in the industry using macroergonomics. International Journal of Progressive Sciences and Technologies, 15(2), 209–216.

11.

Iván Aguilar Reyes

Wozniak

Ham

Zahabi

. (2022, September). An adaptive virtual reality-based training system for pilots. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 66, No. 1, pp. 1962–1966). Sage CA: Los Angeles, CA: SAGE Publications.

12.

Moon

Sasangohar

Peres

S. C.

Son

(2024). Naturalistic observations of multiteam interaction networks: Implications for cognition in crisis management teams. Ergonomics, 67(3), 305–326. https://doi.org/10.1080/00140139.2023.2221418

13.

Morag

Luria

(2013). A framework for performing workplace hazard and risk analysis: A participative ergonomics approach. Ergonomics, 56(7), 1086–1100.

14.

Nickel

Bärenz

Radandt

Wichtl

Kaufmann

Monica

Bischoff

H.-J.

Nellutla

. (2020). Human-system interaction design requirements to improve machinery and systems safety. In Advances in safety management and human factors: Proceedings of the AHFE 2019 international conference on safety management and human factors, July 24-28, 2019, Washington DC, USA 10 (pp. 3–13). Springer International Publishing.

15.

Nicoletti

Padovano

(2019). Human factors in occupational health and safety 4.0: A cross-sectional correlation study of workload, stress and outcomes of an industrial emergency response. International Journal of Simulation and Process Modelling, 14(2), 178–195.

16.

Norazahar

Ahmad

(2018). Developing a framework for assessing risks associated with human responses in emergency escape on offshore installations. Journal of Energy and Safety Technology (JEST), 1(1), 39–44.

17.

Park

. (2023). Human performance modeling of upper limb prosthetic devices (Doctoral dissertation, Texas A&M University).

18.

Park

McKenzie

Shahini

Zahabi

. (2020, December). Application of cognitive performance modeling for usability evaluation of emergency medical services in-vehicle technology. In Proceedings of the human factors and ergonomics society annual meeting (Vol. 64, No. 1, pp. 72-76). Sage CA: Los Angeles, CA: SAGE Publications.

19.

Park

Music

Delgado

Berman

Dodson

Liu

Ruiz

Huang

Kaber

Zahabi

. (2023, October). Cognitive workload and usability of virtual reality simulation for prosthesis training. In 2023 IEEE international conference on systems, man, and cybernetics (SMC) (pp. 1567–1572). IEEE.

20.

Park

Zahabi

. (2022). Cognitive workload assessment of prosthetic devices: A review of literature and meta-analysis. IEEE Transactions on Human-Machine Systems, 52(2), 181–195.

21.

Park

Zahabi

. (2024). A review of human performance models for prediction of driver behavior and interactions with in-vehicle technology. Human Factors, 66(4), 1249–1275.

22.

Petrosoniak

Fan

Trbovich

White

Pinkney

McGowan

Gray

Campbell

Rizoli

Hicks

(2017). P103: A human factors-based framework analysis for patient safety: The trauma resuscitation using in situ simulation team training (TRUST) experience. Canadian Journal of Emergency Medicine, 19(S1), S113–S113.

23.

Sasangohar

Moats

Mehta

Peres

S. C.

(2020). Disaster ergonomics: Human factors in COVID-19 pandemic emergency management. Human Factors, 62(7), 1061–1068.

24.

Son

(2023). Disaster ergonomics: A human factors approach to address escalating challenges from disasters. Cognition, Technology & Work, 25(4), 325–344.

25.

Son

Sasangohar

Neville

Peres

S. C.

Moon

(2020). Investigating resilience in emergency management: An integrative review of literature. Applied Ergonomics, 87, 103114.

26.

Son

Sasangohar

Peres

S. C.

Moon

(2023). Analyzing work-as-imagined and work-as-done of incident management teams using interaction episode analysis. Theoretical Issues in Ergonomics Science, 24(6), 729–757. https://doi.org/10.1080/1463922X.2022.2153495

27.

Wilkinson

(2016). Current regulatory and government focus on human factors. In Human factors in the chemical and process industries (pp. 39–57). Elsevier.

28.

Zahabi

Abdul Razak

A. M.

(2020). Adaptive virtual reality-based training: A systematic literature review and framework. Virtual Reality, 24(4), 725–752.