Co-designing an AI-based ergonomic risk assessment system with rehabilitation professionals: A review on participatory framework

Abstract

Background

Work-related musculoskeletal disorders remain a major source of disability and productivity loss across healthcare and rehabilitation workforces. Although ergonomic risk assessment is central to prevention strategies, traditional assessment approaches are often constrained by time pressures, workload demands, and variability in clinical interpretation. Recent advances in artificial intelligence offer new opportunities to support ergonomic risk assessment through automated and data-driven methods; however, many AI-based workplace tools fail to achieve sustained real-world adoption.

Objective

This paper proposes a participatory co-design framework to guide the development and implementation of artificial intelligence–based ergonomic risk assessment systems in rehabilitation and workplace settings.

Methods

Drawing on principles from participatory ergonomics, occupational health, and ethical artificial intelligence, this conceptual paper outlines a structured co-design process that actively involves rehabilitation professionals and other stakeholders across all stages of system development. The framework integrates stakeholder engagement, iterative design, ethical and governance considerations, and alignment with workplace realities.

Results

The proposed framework emphasises worker-centred design, transparency of AI-supported decision outputs, integration with rehabilitation workflows, and support for professional judgment. It provides practical guidance for translating AI-enabled ergonomic assessment technologies into realworld rehabilitation and occupational contexts.

Conclusion

Participatory co-design offers a critical pathway for aligning AI innovation with professional practice, ethical standards, and sustainable adoption in ergonomic risk assessment at work.

Keywords

artificial intelligence participatory ergonomics occupational health ergonomics in design work rehabilitation

Introduction

Work-related musculoskeletal disorders (WRMSDs) are injuries or disorders affecting muscles, tendons, ligaments, joints, nerves, and related soft tissues that are caused, aggravated, or exacerbated by occupational activities and workplace exposures.¹ Globally, work-related musculoskeletal disorders are among the most prevalent occupational health conditions and contribute substantially to disability, absenteeism, and productivity loss.¹ Previous studies have reported that the prevalence of WRMSDs among healthcare professionals ranges from 40% to 90%, with rehabilitation professionals demonstrating particularly high prevalence rates due to repetitive movements, prolonged static postures, manual patient handling, and physically demanding work activities.² Similar trends have been reported in Saudi Arabia, highlighting the continuing need for effective ergonomic risk assessment and preventive interventions within healthcare settings.³ In daily clinical practice, rehabilitation professionals are frequently exposed to physically demanding tasks performed under time and organisational constraints. Although ergonomic risk assessment tools, such as observational checklists and posture-based scoring systems, are widely used, their routine application is often limited by high workload demands, time pressures, and inter-rater variability.⁴

Recent advances in artificial intelligence (AI) have created new opportunities to automate and augment ergonomic risk assessment through computer vision, wearable sensing technologies, and intelligent decision-support systems.⁵ In principle, these approaches offer the potential to support more consistent and timely identification of ergonomic risks. However, many AIdriven workplace solutions struggle to achieve sustained adoption because they are developed with limited engagement of the workers and professionals who are expected to use them. This disconnect raises important concerns related to usability, ethical acceptability, trust, and implementation feasibility, particularly in rehabilitation settings where professional judgment, autonomy, and seamless workflow integration are essential.⁶

Despite the rapid growth of artificial intelligence applications in ergonomics and occupational health, existing research has predominantly focused on technical accuracy, automation potential, and algorithmic performance.⁷ Comparatively little attention has been given to the design, introduction, and governance of AI-based ergonomic risk assessment systems within real-world rehabilitation and workplace contexts.⁸ In particular, there remains a lack of participatory frameworks that actively involve rehabilitation professionals in shaping system functionality, workflow integration, ethical safeguards, and decision-support outputs.

This absence of worker-centred and participatory approaches represents a critical gap. Rehabilitation professionals operate within complex clinical and occupational environments where contextual judgment, accountability, and adaptation to workplace realities are fundamental to effective ergonomic assessment. AI systems developed without such engagement risk misalignment with clinical workflows, reduced trust, ethical concerns related to transparency and data use, and ultimately poor adoption or abandonment. Addressing this gap requires a structured participatory co-design approach that integrates ergonomic science, artificial intelligence, and stakeholder engagement to support ethical, feasible, and sustainable AI implementation in rehabilitation work settings.

Methods: Conceptual framework development

This conceptual paper was developed using an integrative narrative approach to synthesise literature from participatory ergonomics, artificial intelligence in occupational health, rehabilitation practice, and ethical AI governance. The aim was not to conduct a systematic review or statistical analysis, but to develop a theoretically grounded framework for the codesign and implementation of AI-based ergonomic risk assessment systems.

The framework development followed four sequential stages informed by principles of integrative review methodology, participatory ergonomics, human-centred design, implementation science, and ethical artificial intelligence (AI) governance. In the first stage, relevant literature was identified through searches of PubMed, Scopus, Web of Science, and Google Scholar using combinations of terms related to artificial intelligence, ergonomic risk assessment, participatory ergonomics, rehabilitation, occupational health, human-centred design, implementation science, and ethical AI. The identified literature was reviewed to explore key concepts, opportunities, barriers, and implementation challenges associated with AI-supported ergonomic assessment systems.^8,9

The second stage involved thematic mapping of the literature. Recurring concepts and implementation factors were extracted and grouped into broader thematic categories, including stakeholder engagement, workflow integration, usability, transparency, accountability, professional oversight, ethical governance, and implementation readiness. These themes consistently emerged across the reviewed literature and were considered essential for successful adoption of AI-supported technologies in workplace and rehabilitation settings.¹⁰

In the third stage, the identified themes were synthesised into a preliminary participatory co- design framework tailored to rehabilitation and occupational ergonomics contexts. Relationships among themes were examined to determine how stakeholder engagement, collaborative design processes, governance considerations, workflow integration, and implementation activities interact throughout the lifecycle of AI-based ergonomic risk assessment systems.^11,12 These relationships informed the structure of the proposed framework and the directional links illustrated in Figure 1.

In the final stage, the framework underwent iterative conceptual refinement to improve clarity, practical applicability, and alignment with rehabilitation practice. Particular emphasis was placed on ensuring that AI functions as a decision-support tool that complements rather than replaces professional judgement.¹³ The resulting framework integrates technical, organisational, ethical, and human factors considerations within a single participatory model designed to support future implementation, evaluation, and empirical validation.

Traditional ergonomic risk assessment relies on several validated observational tools, including the Rapid Upper Limb Assessment (RULA), Rapid Entire Body Assessment (REBA), Ovako Working Posture Analysis System (OWAS), and the National Institute for Occupational Safety and Health (NIOSH) Lifting Equation.¹⁴ These instruments are widely used to evaluate postural loads, biomechanical exposures, and ergonomic risk factors associated with workrelated musculoskeletal disorders.¹⁵ Previous studies have demonstrated acceptable reliability and validity of these tools for identifying workplace ergonomic hazards and informing preventive interventions.¹⁶ However, their application often requires trained assessors, substantial observation time, and may be affected by inter-rater variability and subjective interpretation. These limitations have contributed to increasing interest in artificial intelligence (AI)-supported ergonomic assessment systems capable of providing more objective, scalable, and real-time evaluations of ergonomic risk in occupational settings.

Artificial intelligence has the potential to enhance ergonomic risk assessment by addressing several limitations associated with traditional observational methods.¹⁷ Conventional ergonomic tools rely heavily on expert judgment and manual observation, processes that are time-intensive and susceptible to inter-rater variability. AI-enabled approaches, including computer vision, wearable sensing, and intelligent decision-support systems, offer opportunities to automate posture analysis, movement tracking, and exposure estimation with greater consistency and efficiency.¹⁸

In occupational and rehabilitation settings, AI-based ergonomic assessment systems may support continuous or task-specific monitoring without disrupting routine work processes. These technologies can assist rehabilitation professionals in identifying high-risk tasks, tracking cumulative biomechanical exposure, and informing preventive interventions.¹⁹ From the perspective of clinicians engaged in routine workplace assessment, the value of AI lies not only in technical accuracy but also in the ability of systems to deliver timely, interpretable insights that complement professional judgment in complex work environments.

Despite these opportunities, significant challenges limit the real-world adoption of AI in ergonomics. Many systems are developed and evaluated in controlled environments, with limited consideration of workplace variability, clinical workflows, and contextual constraints.²⁰ As a result, AI-generated outputs may lack transparency, interpretability, or relevance to professional practice. For rehabilitation professionals, whose assessments are grounded in contextual reasoning and ethical accountability, poorly integrated AI tools can undermine trust and reduce practical utility.¹³

Ethical and organisational concerns further complicate implementation. Issues related to data privacy, worker surveillance, algorithmic bias, and professional autonomy are particularly salient in workplace applications. Without clear governance structures, appropriate training, and meaningful user involvement, AI-based ergonomic systems risk resistance, misuse, or abandonment. These limitations underscore the importance of worker-centred and participatory approaches that align technological innovation with professional values, ethical standards, and real-world work practices.²¹

Results: Proposed participatory co-design framework for AI-based ergonomic risk assessment

This co-design provides a structured approach for developing artificial intelligence–based ergonomic risk assessment systems that are aligned with real-world rehabilitation and workplace contexts.¹¹ Rather than relying on technology-led development, this framework positions rehabilitation professionals and other stakeholders as active partners throughout the design and implementation process. This approach reflects the realities of rehabilitation ergonomics, where professional judgment, ethical responsibility, and workflow integration are central to effective practice.²² Based on the integrative synthesis of literature from participatory ergonomics, human-centred artificial intelligence, ethical AI governance, and rehabilitation practice, several key concepts were identified that informed the development of the proposed framework. These concepts and their relevance to AI-based ergonomic risk assessment are summarised in Table 1.

Table 1.

Key concepts from the literature informing the proposed participatory co-design framework.

Key concept	Relevance to AI-based ergonomic risk assessment	Application in the proposed framework
Participatory ergonomics	Involves workers and professionals in identifying ergonomic risks and solutions	Stakeholder engagement and shared decision-making
Human-centred AI	Ensures AI systems are usable, understandable, and aligned with user needs	User-centred interface design and interpretable outputs
Workflow integration	Supports adoption by fitting technology into existing rehabilitation and workplace routines	Integration with clinical and occupational assessment processes
Ethical AI governance	Addresses privacy, accountability, transparency, and bias	Governance principles embedded across all framework stages
Professional judgement	Recognises that AI should support, not replace, rehabilitation expertise	AI outputs are positioned as decision-support tools
Implementation readiness	Considers training, organisational support, and scalability	Capacity building, pilot testing, and iterative refinement

The identified concepts highlight the importance of stakeholder engagement, workflow integration, ethical governance, professional oversight, and implementation readiness in the successful adoption of AI-supported ergonomic assessment systems. These elements were integrated into the proposed participatory co-design framework illustrated in Figure 1.

To further highlight the novelty of the proposed framework, Table 2 compares its key characteristics with existing human-centred AI, participatory design, AI governance, and technology implementation approaches commonly reported in the literature.^13,23

Table 2.

Comparison of the proposed participatory co-design framework with existing AI implementation approaches.

Framework type	Primary focus	Key limitation	Contribution of the proposed framework
Human-centred AI frameworks	User experience, usability, transparency, explainability	Limited emphasis on rehabilitation workflows and ergonomic risk assessment processes	Integrates human-centred AI principles with rehabilitation-specific ergonomic assessment and workplace implementation
Participatory design frameworks	Stakeholder involvement and co-design activities	Often provide limited guidance regarding AI governance, accountability, and implementation readiness	Combines stakeholder engagement with ethical governance and implementation planning
AI governance frameworks	Transparency, accountability, privacy, and ethical AI use	Limited attention to workflow integration, rehabilitation practice, and ergonomic assessment requirements	Integrates governance principles with real-world rehabilitation and workplace applications
Technology implementation frameworks	Organisational readiness, adoption, and scalability	Limited focus on professional judgement and ergonomic decision-making processes	Emphasises rehabilitation professional oversight and AI-supported ergonomic risk assessment
Proposed participatory co-design framework	Stakeholder engagement, workflow integration, ethical governance, professional oversight, and implementation readiness	Specifically developed for AI-supported ergonomic risk assessment in rehabilitation and occupational settings	Provides an integrated participatory model addressing technical, organisational, ethical, and clinical considerations within a single framework

Framework type primary focus key limitation contribution of the proposed framework

Human-centred AI frameworks User experience, usability, transparency, explainability Often provide limited guidance regarding AI governance, accountability, and implementation readiness combines stakeholder engagement with ethical governance and implementation planning.

Technology implementation frameworks adoption and organisational readiness limited focus on professional judgement and ergonomic assessment processes emphasises rehabilitation professional oversight and ergonomic decision support.

Proposed framework Stakeholder engagement, workflow integration, ethical governance, professional oversight, and implementation readiness Specifically tailored for AI-supported ergonomic risk assessment in rehabilitation settings Provides an integrated participatory model addressing technical, organisational, ethical, and clinical considerations.

The concepts presented in Tables 1 and 2 were used to inform the development of the framework domains illustrated in Figure 1. Participatory ergonomics and human-centred AI informed the stakeholder engagement and collaborative system design domains, while ethical AI governance informed the governance and oversight domain. Workflow integration, professional judgement, and implementation readiness informed the implementation and evaluation domain.

The relationships among these domains were derived from recurring interactions identified in the literature, indicating that successful AI adoption depends on continuous engagement between stakeholders, system design activities, governance processes, and implementation efforts. Consequently, the directional arrows in Figure 1 represent iterative and bidirectional interactions rather than a strictly linear process, reflecting the dynamic nature of participatory co-design and technology implementation.

Consequently, the directional arrows in Figure 1 represent iterative and bidirectional interactions rather than a strictly linear process, reflecting the dynamic nature of participatory co-design and technology implementation.

The proposed framework comprises four interconnected domains: stakeholder engagement, collaborative system design, ethical and governance oversight, and implementation and evaluation. Stakeholder engagement involves rehabilitation professionals, workers, ergonomists, and organisational leaders in identifying needs and contextual challenges. Collaborative system design focuses on defining AI functionality, workflow integration, and interpretability requirements. Ethical and governance oversight addresses privacy, transparency, accountability, and worker autonomy. Implementation and evaluation include training, organisational readiness, scalability, and future empirical assessment of usability and effectiveness.

The framework begins with the identification and engagement of key stakeholders, including rehabilitation professionals, workers, ergonomists, organisational leaders, and technical specialists.¹² Early involvement supports a shared understanding of ergonomic challenges, contextual constraints, and expectations of AI-supported assessment tools. This stage helps establish trust and reinforces the role of AI as a decision-support resource rather than a substitute for professional expertise.

Subsequent co-design activities focus on collaboratively defining system objectives, functionality, and integration within existing workflows. Rehabilitation professionals contribute practical insights into assessment routines, time demands, and interpretability requirements, ensuring that AI-generated outputs are meaningful, actionable, and compatible with clinical reasoning.²⁴ Iterative feedback allows system concepts to be refined in response to user needs and workplace realities.

Ethical, legal, and governance considerations are embedded throughout the co-design process. Stakeholder participation enables transparent discussion of data privacy, consent, accountability, and worker autonomy, helping to mitigate concerns related to surveillance or inappropriate use of AI technologies.²⁵ Co-developed governance principles strengthen ethical adoption and support trust among users and organisations.

Finally, the framework incorporates capacity building and implementation planning to support sustainable integration. Training strategies, organisational readiness, and scalability across diverse work settings are addressed collaboratively. The participatory process is inherently iterative, enabling continuous refinement and providing a foundation for future empirical evaluation of usability, acceptability, and impact on ergonomic risk management.

Discussion

This paper presents a participatory co-design framework to guide the development and implementation of artificial intelligence based ergonomic risk assessment systems in rehabilitation and workplace settings. By integrating principles from participatory ergonomics, ethical AI, and rehabilitation practice, the framework addresses a critical gap between technological innovation and real-world adoption of AI at work. Rather than prioritising algorithmic performance alone, the framework emphasises worker-centred design, professional judgment, and contextual integration priorities that are familiar to rehabilitation professionals translating ergonomic assessment into practical workplace interventions.

Unlike many existing AI implementation and evaluation frameworks, which primarily focus on technical performance, algorithmic accuracy, usability, trust, or organisational adoption, the proposed framework adopts a broader participatory perspective that integrates rehabilitation practice, ergonomic assessment, ethical governance, and stakeholder engagement throughout the development lifecycle.²⁶ Previous studies have highlighted the importance of transparency, trust, and usability for successful AI adoption; however, these factors are frequently examined independently rather than within an integrated participatory framework.²⁷ The present framework extends existing approaches by explicitly incorporating stakeholder co-design,²⁸ professional oversight, workflow integration,²⁹ ethical accountability, and implementation readiness as interconnected components of AI-supported ergonomic risk assessment systems.³⁰ This integrated perspective is particularly relevant for rehabilitation professionals, whose decision-making processes require contextual judgement, ethical responsibility, and close alignment with workplace realities.

The framework responds to growing concerns that AI-driven workplace technologies often fail due to misalignment with professional workflows, ethical expectations, and organisational realities.³¹ In rehabilitation contexts, ergonomic assessment is closely linked to clinical reasoning, accountability, and direct interaction with workers. AI systems developed without meaningful user involvement may undermine trust, reduce usability, and limit acceptance.³² By positioning rehabilitation professionals and workers as active contributors throughout the design and implementation process, the framework operationalises transparency through explainable AI outputs, supports workflow integration through collaborative system configuration, and promotes shared ownership through continuous stakeholder feedback and governance participation.

Ethical and governance dimensions influence the perception, adoption, and long-term sustainability of AI-based ergonomic systems in work environments.³³ In occupational ergonomics, concerns extend beyond technical performance to include accountability for AIinformed decisions, clarity of responsibility among stakeholders, and alignment with organisational policies.³⁴ By embedding governance considerations within the participatory co-design process, the proposed framework enables shared ownership of ethical standards, explicit role delineation, and context-sensitive decision-making. This approach supports the integration of AI as a professional support resource that reinforces, rather than constrains, clinical judgment and worker agency.³⁵

In practical terms, ethical governance may be operationalised through transparent data management policies, clearly defined stakeholder responsibilities, regular system audits, user training programmes, and ongoing monitoring of algorithmic performance. Such measures may strengthen trust, accountability, and long-term sustainability of AI-supported ergonomic assessment systems.

A critical consideration in AI-based ergonomic risk assessment is the allocation of responsibility when AI-generated outputs are inaccurate, biased, or misinterpreted.³⁶ Recent literature on AI governance emphasises that accountability in AI-supported decision-making should be viewed as a shared responsibility involving technology developers, organisational leadership, and end-users rather than being assigned to a single stakeholder.³⁷ Developers are responsible for ensuring transparency, validation, explainability, and mitigation of algorithmic bias, whereas organisations should establish governance structures, provide adequate training, and oversee implementation processes. Rehabilitation professionals remain responsible for applying clinical reasoning, contextual judgement, and professional expertise when interpreting AI-generated recommendations. Adopting a shared accountability approach may reduce ethical and legal ambiguity while promoting safe, transparent, and responsible use of AI technologies in workplace and rehabilitation settings.

Although this paper does not present empirical findings, it offers a structured foundation for future research and practice. The framework can inform the design of pilot studies, feasibility assessments, and effectiveness evaluations of AI-based ergonomic risk assessment systems. It may also support organisational decision-making related to training, implementation planning, and policy development. Future research should examine how participatory co-design influences usability, acceptability, trust, and ergonomic risk outcomes across diverse rehabilitation and work environments.

Limitations

This paper presents a conceptual framework and does not include empirical validation or field testing of the proposed model. The framework was developed through an integrative narrative synthesis of the literature rather than a systematic review, which may have resulted in the omission of some relevant studies. Furthermore, the framework has not yet been evaluated across different occupational sectors or rehabilitation settings. Future studies should assess its feasibility, usability, and effectiveness in real-world workplace environments.

Clinical implications

The proposed framework provides practical guidance for rehabilitation professionals, ergonomists, and organisational stakeholders involved in the development and implementation of AI-supported ergonomic risk assessment systems. By emphasising stakeholder engagement, workflow integration, ethical governance, and professional oversight, the framework may facilitate the development of AI tools that are more acceptable, trustworthy, and aligned with rehabilitation practice. The framework also reinforces the role of AI as a decision-support resource that complements professional judgement rather than replacing clinical expertise.

Conclusion

Artificial intelligence holds considerable promise for enhancing ergonomic risk assessment in rehabilitation and workplace settings; however, technological capability alone is insufficient to ensure meaningful and sustainable adoption. This paper presents a participatory co-design framework that positions rehabilitation professionals and workers at the centre of AI system development, implementation, and governance. By integrating ergonomic science, ethical considerations, and real-world work practices, the framework supports worker-centred, trustworthy, and context-sensitive AI adoption. The proposed approach provides practical guidance for aligning AI innovation with professional values and occupational health goals, while establishing a strong foundation for future empirical research on AI-enabled ergonomics at work.

Footnotes

Acknowledgments

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Groups Program under grant number RGP2/468/47.

ORCID iDs

Paul Silvian Samuel

Lalitha Kakaraparthi

Ethical approval

Not applicable.

Informed consent

Not applicable.

Funding

Declaration of conflicting interests

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

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