Workplace ergonomics is under increasing scrutiny as organisations look to reduce musculoskeletal injuries while maintaining productivity. Many still rely on single assessment tools to deliver quick insights, but growing evidence suggests this approach may not capture the full picture.

In complex environments such as manual handling, factors like force, repetition and posture interact continuously. When these are assessed in isolation, risks can be underestimated or misunderstood.

Keep reading to understand why relying on one ergonomic assessment tool can lead to incomplete conclusions and what a more effective approach looks like.

The limits of single tool assessments

A recent peer reviewed study, “An Uncertainty Aware Ergonomic Risk Assessment Framework”, highlights a critical issue in workplace ergonomics: relying on a single assessment tool can lead to incomplete or misleading conclusions.

Traditional tools such as REBA, RULA and OWAS remain widely used because they are practical, quick to apply and well understood. However, the research confirms that these methods often:

• Rely heavily on assessor interpretation
• Focus on individual risk factors in isolation
• Struggle to reflect the true complexity of manual handling tasks

As a result, organisations may reach very different conclusions about the same task, particularly in manual handling environments where force, repetition and posture interact continuously.

At Cardinus, this mirrors what we see in practice. Effective ergonomic risk management depends not on a single score, but on understanding how work is actually performed, across time, people and environments.

Ergonomic risk is rarely caused by one factor

The study reinforces an important principle: musculoskeletal risk is rarely caused by one issue alone. Instead, it emerges from a combination of:

• Force and load handling
• Repetition and frequency
• Awkward or sustained postures
• Environmental and task design factors

Crucially, the research found that force was the most significant contributor to overall risk, particularly in manual handling activities.

This is a key challenge for organisations. Many commonly used tools are posture led, yet force, speed and repetition often drive injury risk. Without a broader view, assessments may underestimate exposure or focus controls in the wrong place.

Different tools highlight different risks

The research also demonstrates that commonly used ergonomic tools:

• Measure different aspects of risk
• Can produce conflicting results for the same task

For example:

• A posture based assessment may suggest low risk
• A repetition or force focused method may indicate high exposure

This is not a failure of the tools themselves; it reflects the complexity of human movement and work design. The challenge lies in interpreting results and deciding where to act.

Combining methods improves decision making

The study shows that combining ergonomic assessment methods provides:

• Greater consistency
• Better visibility of hidden risks
• More defensible decisions

Methods such as Strain Index (SI), OWAS and RULA were found to be more reliable when evaluated together rather than in isolation.

At Cardinus, we see the greatest value when traditional ergonomic tools are supported by objective movement data, particularly for manual handling tasks that vary across shifts, people and loads.

Reducing uncertainty with motion capture and sensor technology

A key strength of the research is its acknowledgement that ergonomic assessment involves uncertainty. Assessments are typically based on brief observations and professional judgement, both essential but inherently variable.

This is where motion capture and wearable sensor technology add significant value.

When used alongside established assessment tools, these technologies can:

• Objectively measure joint angles, movement speed and repetition
• Capture how tasks vary across complete work cycles, not just snapshots
• Highlight cumulative exposure over time
• Reduce reliance on visual estimation alone

Rather than replacing traditional ergonomic assessments, motion capture strengthens them, supporting a more uncertainty aware, evidence led approach, exactly as recommended in the research.

Why this matters for manual handling

Manual handling risk is rarely static. Tasks often involve:

• Changing loads and techniques
• Repeated lifts across a shift
• Compensatory movements due to fatigue or workspace constraints

Observational tools alone may struggle to reflect this variability.

By integrating ergonomic assessment tools with motion capture analysis, organisations can:

• Identify high risk postures that occur repeatedly, not occasionally
• Understand how force, speed and posture interact
• Evaluate whether control measures genuinely reduce exposure

This leads to better targeted interventions and more sustainable risk reduction.

Practical takeaways for organisations

To improve manual handling risk management, organisations should:

• Avoid relying on a single ergonomic assessment tool
• Combine complementary methods where appropriate
• Recognise and manage uncertainty in ergonomic decision making
• Use objective data, such as motion capture, to support observations
• Focus on task design and control effectiveness, not just scores

At Cardinus, we help organisations apply this blended approach, combining expert ergonomic consultancy with advanced motion analysis to create clear, defensible and actionable outcomes.

Summary

This research reinforces an essential message: Ergonomic risk assessment is a decision making process, not a checklist exercise.

For manual handling environments, the most effective approach combines:

• Established ergonomic tools
• Professional expertise
• Objective movement data

By recognising uncertainty, using multiple methods and focusing on real world work patterns, organisations can design more effective controls and reduce musculoskeletal injury risk with confidence.

References

Zenginoğlu, A. and Kuvvetli, Y., 2026.
An uncertainty aware ergonomic risk assessment framework using Interval Valued Fermatean Fuzzy Sets and hybrid MCDM.
Scientific Reports.

Abbreviations

• OWAS – Ovako Working Posture Analysis System
• REBA – Rapid Entire Body Assessment
• RULA – Rapid Upper Limb Assessment
• SI – Strain Index