AI Ergonomic Assessment Tools: Hype vs Reality.

Ergonomic assessment tools have been around for decades. Traditionally, these tools are used by highly trained ergonomists to evaluate injury risks during manual labor tasks.

Enter AI. It’s marketable, it’s attractive, and it promises accessibility. But is it truly effective?

Many technology companies have integrated AI with 2D motion capture (MoCap) technology, allowing users to record a video of an employee performing a task. The AI then analyzes the movement and generates a risk assessment—without the need for an ergonomics expert. The pitch is simple: “Anyone can use this technology.”

What’s that saying again? If it’s too good to be true, it probably is.

The Limits of AI-Based Ergonomic Assessments

Due to their reliance on 2D camera capture, AI-driven MoCap systems are primarily limited to two common ergonomic assessment tools: the Rapid Entire Body Assessment (REBA) and the Rapid Upper Limb Assessment (RULA). These tools are useful but have inherent limitations—especially when applied through AI.

1. REBA vs. RULA: Is One Better?

Research suggests that the REBA is less effective at predicting injury risk compared to the RULA.¹ This makes RULA the preferred choice when using MoCap AI, but even RULA has caveats.

2. RULA’s Predictive Weaknesses

A RULA assessment is most effective when evaluating static tasks that involve frequent upper-body movements.² However, its ability to predict injury risk weakens when applied to low- or medium-risk tasks—meaning that a “low-risk” rating does not necessarily indicate safety.³

3. The Accuracy Problem of 2D Motion Capture

AI-driven MoCap is limited by its two-dimensional perspective, leading to inherent distortions. A comprehensive review of independent studies shows that these systems struggle with factors like:

• Camera angles and perspective distortions

• Lighting conditions

• Distance from the subject

• Inability to capture depth-related movements accurately⁴

  
  

Most existing research on AI-driven ergonomic assessments has been conducted by the very companies selling these products—raising concerns about confirmation bias and reliability.

Key Takeaways: What You Need to Know

Before investing in AI-based MoCap ergonomic tools, it’s essential to recognize their limitations:

✅ Pros: AI-driven systems can provide quick, accessible assessments and may help screen for high-risk tasks.

❌ Cons:

• Restricted to REBA/RULA assessments, limiting their ability to analyze lifting tasks.

• RULA is only effective for specific types of movements and struggles with low/medium-risk predictions.

• 2D motion capture introduces accuracy challenges due to perspective distortions, lighting, and camera positioning.

• Lack of independent validation raises concerns about bias in AI-generated assessments.

The Bottom Line

AI-based ergonomic tools can provide useful insights—but they are not a substitute for a trained professional. Their effectiveness depends on understanding their limitations, using them as a supplement rather than a replacement, and ensuring that assessments are interpreted correctly.

For workplaces looking to reduce injuries and enhance safety, a hybrid approach—combining AI with expert oversight—remains the best path forward.

References

1. Marler, T., & Abdel-Malek, K. (2022). Comparison of REBA and RULA for risk assessment in workplace ergonomics. International Journal of Environmental Research and Public Health, 19(1), 595. https://www.mdpi.com/1660-4601/19/1/595

2. McAtamney, L., & Corlett, E. N. (1993). RULA: A survey method for the investigation of work-related upper limb disorders. Applied Ergonomics, 24(2), 91-99. https://www.physio-pedia.com/Rapid_Upper_Limb_Assessment_(RULA)

3. Kazemi, R., et al. (2020). Evaluating the effectiveness of RULA for predicting musculoskeletal disorders in occupational settings. Journal of Hygiene Engineering, 9(4), 414. https://jhygiene.muq.ac.ir/browse.php?a_id=414&slc_lang=en&sid=1&ftxt=1&html=1

4. Smith, J., & Brown, P. (2024). Limitations of AI-based 2D motion capture in ergonomic assessment: A systematic review. Scientific Reports, 14, 79373. https://www.nature.com/articles/s41598-024-79373-4#citeas