Using Human Body Models for Better Safety Engineering

Today’s safety challenges extend far beyond traditional crash scenarios.

Engineers must design for:

• A diverse global population with significant variation in size, age, and physiology
• New seating configurations driven by automation and interior redesign
• Pre-crash and low-gravity events that influence occupant positioning
• Interactions between human behavior and increasingly intelligent vehicle systems

Despite this complexity, many simulation approaches still rely on models that lack sufficient anatomical and biomechanical detail.

The consequences are subtle, but significant.

HBMs are not simplified representations. They are biofidelic, physics-based models. Engineers design them to copy how the human body reacts to forces, motion, and the environment. They do this with high accuracy.

These models enable engineers to evaluate safety at a deeper level by:

• Simulating anatomical structures and tissue response
• Predicting injury based on strain and stress, not just motion
• Representing variability across real-world populations
• Capturing dynamic interactions between occupant and system

This shifts simulation from structural validation to human understanding.

Engineers are no longer limited to asking whether a system performs. They can begin to understand how it affects the human body, and why.

Consider a vehicle interior designed for both traditional driving and automated modes.

In automated mode, occupants may recline, rotate, or adopt non-standard postures. These variations introduce new safety challenges that are difficult to evaluate using conventional methods. 

With high-fidelity human body models, engineers can simulate:

• Different seating positions and transitions between them
• Variations in body size, posture, and muscle engagement
• The interaction between restraint systems and diverse anatomies

This allows risks to be identified earlier and across a broader set of conditions.

Instead of designing for an "average" occupant, engineers can design for the real population.

This is where Safety Intelligence becomes actionable.

The effectiveness of any human body model depends on how closely it matches real life. It also depends on how well it fits into the broader safety ecosystem.

Humanetics’ high quality models stand apart in several critical ways.

These models do not operate in isolation. They link physical tests, sensor data, and simulation workflows. This creates a closed-loop system where insights improve over time.

Humanetics HBMs integrate with: 

• Ergonomics (with RAMSIS or Safety Studio)
• Physical ATDs
• Virtual ATDs
• Population diversity

• Automated workflows (with APT CONNECT® and INSIGHT CONNECT®)

Humanetics’ heritage in crash test dummies, sensors, and human measurement provides a unique foundation. The models are not theoretical constructs. They are informed and validated by 75+ years of real-world human response data.

Rather than relying solely on kinematic outputs, Humanetics HBM models simulate tissue-level response. Engineers can use this to evaluate how injuries occur, not just to determine whether something exceeds a threshold.

Safety is not uniform, and neither are the people it protects. Humanetics models incorporate variability across gender, size, and physiology, enabling more inclusive and accurate safety design.

This combination of integration, grounding, and fidelity, are what separates Humanetics from competitors.

It helps you find risk sooner.  It enables you to rely less on late-stage testing, and trust simulation-based decisions more.

Most organizations already use simulation widely. Teams are under pressure to accelerate development, reduce cost, and manage increasing system complexity. Digital tools are in place, but confidence in human response modeling is often limited.

This creates a problem. Existing models may not provide sufficient accuracy or representation. Engineers rely on them for early-stage decisions but must validate extensively through physical testing. This introduces inefficiency and uncertainty.

The implications become clear over time. Development cycles extend because teams discover issues later in the process. Design changes become more costly. Certain risks, particularly those affecting underrepresented populations, may go undetected until late validation stages or even post-launch.

Human body models change this dynamic. HBM enables faster iteration without sacrificing accuracy, and they support the development of systems that perform more consistently across real-world populations.

In doing so, they shift safety engineering from reactive validation to proactive design.   

The most significant impact of advanced human body models is the transition from representation to prediction.

Traditional approaches approximate human behavior. High-fidelity models enable engineers to predict it.

This predictive capability is essential in a world where:

• The number of possible scenarios exceeds what can be physically tested
• Vehicle systems are increasingly adaptive and software-driven
• Safety expectations continue to rise

By combining detailed human modeling with scalable simulation, organizations can move from asking:

• “Does this design pass?” to
• “How will this design protect humans in the real world?”