Validating optical motion capture assessments of

Problem is, many simply report a number without trying to ascribe some of it to mathematical artifact..

As you can see from the image above and what the biomechanist said, markerless motion capture cannot capture pronation/supination.

Marker-based biomechanics systems are incredibly precise AND accurate, while markerless systems typically lack the validation required to even make firm conclusions about precision.

At Driveline Baseball, we are pushing markerless technology forward by planning hundreds of test cases of marker-based situations vs.

First, a graph – this is a comparison of pronation/supination at the elbow using a marker-based system vs. Second, a statement from a biomechanist who works in an academic lab: You got me thinking again about the issues/problems with accuracy in measuring internal shoulder rotation…

with markerless/digitizing, it comes down to carrying angle of the elbow screwing up the cross-product of upper and forearm (which is how shoulder IR/ER has to be tracked if going markerless).

Ah, well, I guess we need to factor in calibration of the cameras, accounting for optical errors as well, so use this formula too: And of course, DLT reconstruction is just a few steps away: Got it?

Off you go – set up some cameras, record some markers on the human body, and you too will start solving for kinematics (mechanics) and kinetics (forces/torques) soon enough! Believe it or not, the above equations are how I got started in analyzing biomechanics.

However, there are Rotation about an axis is an easy one to understand.What I slowly began to realize – and how we use biomechanical data at Driveline Baseball now – is that this data is part of a larger thumbprint, an increasingly vital part of an athlete’s assessment.Today, we use this data for amazing purposes, and every day that goes by that we put assessments into our machine learning-enabled backend software, we learn more and more about injuries, performance, and the human body simply by doing our jobs. But before we delve too deep into the actionable part of the data – which we talk about all the time on our blog and on social media – let’s talk about pitfalls of biomechanical analyses and the misuse of science that I see on a regular basis.I figured if I could do this and build a biomechanics lab for baseball, that I would be on my way to solving pitching injuries and that I’d command a ton of contract work from Major League Baseball!So, two things happened: Yes, it’s just math, and Matthew Wagshol (current biomechanist at Driveline Baseball) and I proved that we could do it, by building the control objects in the aisles of Home Depot and spending countless hours using terrible software meant for graduate students that we twisted in so many directions to just spit out answers. Sure, the achievement was really cool, and as far as I could tell, no one had done it before me…

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By taking frequent snapshots and examining how training impacts the athlete.

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