The Validity Check Every Biomechanics Lab Should Be Running

June 26, 2025
Theia
BIO
Recent Posts
Is Your Motion Capture Data Actually Reliable?
In this series, we’re unpacking the STRN Quality Framework — a set of 25 measurable features across five key pillars — to help researchers, clinicians, and technology buyers evaluate motion capture systems more effectively. Each post explores one pillar in depth, with practical examples, critical questions to ask, and insights on how markerless systems like Theia3D align with these industry standards.
Theia3D + GeForce RTX 5090: Next-Level Speed for Markerless Motion Capture
In case you missed it, we recently updated Theia3D Axiom to include support for the newest generation of NVIDIA graphics cards - the GeForce RTX 50 Series. With this exciting update, these new graphics cards can be used to unlock even greater processing performance from Theia3D, allowing you to push the limits of your markerless motion capture workflow. After our initial announcement which included benchmarks for the new RTX 5080, we’re following up to provide even more exciting results - RTX 5090 benchmarks.
Aligning the Frame: How Reference Frame Differences Impact Markerless vs. Marker-Based Comparisons
When comparing markerless and marker-based motion capture, researchers often find differences in the kinematic outputs. But how much of these discrepancies are true differences in how the movement is measured, and how much comes down to how each system defines the anatomical reference frames? A new study out of Rush University Medical Center and Aesculap AG explores this question using Theia3D - and finds that aligning coordinate systems dramatically improves agreement between systems.

Summary

In this series, we’re unpacking the STRN Quality Framework — a set of 25 measurable features across five key pillars — to help researchers, clinicians, and technology buyers evaluate motion capture systems more effectively. Each post explores one pillar in depth, with practical examples, critical questions to ask, and insights on how markerless systems like Theia3D align with these industry standards.

Evaluating Established Benefit with the STRN Quality Framework


When you’re investing motion capture technology — whether for a research lab, clinic, or field environment — specs alone don’t tell the full story. The real question is: Does the system work, in ways that actually matter?

That’s the focus of Pillar B: Established Benefit in the Sports Technology Research Network (STRN) Quality Framework. It’s about proven, validated value — not just theoretical performance. 

This post is part of our ongoing STRN blog series. We’re breaking down each of the five pillars and showing how Theia3D, our markerless motion capture system, measures up.

Download our full evaluation guide and checklist to compare systems across all 25 quality features.

What Does "Established Benefit” Actually Mean?


Pillar B evaluates whether a motion capture system:

  • Measures what it claims to (construct validity)
  • Matches known gold standards (concurrent validity)
  • Supports predictive insights over time (predictive validity)
  • Functions reliably in real-world settings (functionality)

Let’s look at how Theia3D performs against each of these:
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Construct Validity

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Does the system measure what it’s supposed to?

Construct validity refers to how well a technology captures the signals it claims to, from segment positions and joint angles to more complex metrics like gait indices or balance measures.

Theia3D has been independently validated across a wide range of biomechanical constructs, including:

  • Spatiotemporal gait parameters
  • Joint angles and moments (especially sagittal and frontal plane)
  • Balance-related quantities
  • Upper extremity motor function
  • Running metrics and ground reaction force estimates

Beyond simply matching known values, Theia3D has also shown promise in discriminating between clinical groups, including:

  • Knee osteoarthritis patients vs. healthy controls
  • Children with cerebral palsy vs. typically developing peers
  • Individual gait impairments vs. normative baselines

While more work is underway, early studies support Theia3D’s ability to both capture and differentiate relevant biomechanical signals.

This goes beyond raw measurement accuracy — it speaks to whether the system delivers clinically meaningful insight.


Concurrent Validity

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‍How does it compare to “gold standard” technology?

This type of validity looks at agreement between new outputs and known gold standards — usually marker-based motion capture or instrumented gait systems.

Theia3D has demonstrated strong alignment with previously validated systems for metrics including:

  • Lower limb alignment
  • Center of mass and whole-body angular momentum
  • Joint reaction forces via musculoskeletal modeling
  • Gait pathology indices
  • Muscle activation timing

In several cases, Theia3D’s results match or closely track those from optical motion capture, gait mats, and force plates — meaning researchers can trust Theia3D to generate comparable results using less equipment and in more flexible environments.
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Predictive Validity 


‍Can the system forecast future outcomes?

This is the holy grail for motion capture: not just observing what’s happening, but using those insights to anticipate injury risk, disease progression, or performance changes.

Predictive research is still emerging, but early studies using kinematic data from systems like Theia3D have shown promise in:

  • Identifying fall risk in older adults
  • Estimating injury likelihood in runners
  • Tracking movement trends in industrial workers

Theia3D’s automated, scalable data capture enables long-term population studies — a foundational step in building predictive models.


Functionality

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‍Does it actually work in your setting, with your equipment?

Theia3D is designed for real-world usability. No markers. No manual labeling. Just calibrated video and the flexibility to run with your existing camera setup.

It uses standard video input and supports multiple third-party camera systems. That flexibility, combined with transparent documentation around limitations and data requirements, helps ensure functionality across diverse lab and field settings.

Minimum requirements:

  • At least 6 calibrated cameras capturing high-resolution RGB video
  • Subjects visible in 3+ views at 500+ pixels in height
  • Calibration error within millimeter range

Plus, our team works directly with customers to match system setup to specific research and clinical needs — whether you're in a biomechanics lab or on a basketball court.
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Summary: What This Pillar Tells You


More than just technical performance, Established Benefit is about proving real-world value. From strong construct and concurrent validity to increasing predictive insight and robust functionality, Theia3D is built to support the goals of biomechanics labs, clinics, and researchers alike. 

Want to see how Theia3D stacks up across all five pillars?‍


We’ve created a free downloadable checklist to help you evaluate any motion capture system against the full STRN Quality Framework.

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Compare systems. Ask the right questions. Invest with confidence.

Recent Posts
Is Your Motion Capture Data Actually Reliable?
In this series, we’re unpacking the STRN Quality Framework — a set of 25 measurable features across five key pillars — to help researchers, clinicians, and technology buyers evaluate motion capture systems more effectively. Each post explores one pillar in depth, with practical examples, critical questions to ask, and insights on how markerless systems like Theia3D align with these industry standards.
Theia3D + GeForce RTX 5090: Next-Level Speed for Markerless Motion Capture
In case you missed it, we recently updated Theia3D Axiom to include support for the newest generation of NVIDIA graphics cards - the GeForce RTX 50 Series. With this exciting update, these new graphics cards can be used to unlock even greater processing performance from Theia3D, allowing you to push the limits of your markerless motion capture workflow. After our initial announcement which included benchmarks for the new RTX 5080, we’re following up to provide even more exciting results - RTX 5090 benchmarks.
Aligning the Frame: How Reference Frame Differences Impact Markerless vs. Marker-Based Comparisons
When comparing markerless and marker-based motion capture, researchers often find differences in the kinematic outputs. But how much of these discrepancies are true differences in how the movement is measured, and how much comes down to how each system defines the anatomical reference frames? A new study out of Rush University Medical Center and Aesculap AG explores this question using Theia3D - and finds that aligning coordinate systems dramatically improves agreement between systems.
In this blog
Summary