What can Garmin RunDynamics and Stryd data tell you about biomechanical training load? - Running Writings

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I just published a new scientific study—this paper was basically the centerpiece of my PhD dissertation, and the title more or less gives away the premise: “Predicting Achilles tendon and patellofemoral joint forces during running with consumer-grade wearable sensor data.”

In this study, “consumer-grade wearable sensor data” means RunDynamics from a chest-worn Garmin heart rate monitor and similar data from a Stryd foot pod. If you aren’t familiar with these devices, they use on-board sensors to estimate biomechanical parameters like your cadence / stride length, vertical oscillation, ground contact time, and so on.[1]

There are a few issues with the gait metrics you get from these kinds of devices, though. First, are they accurate? And second…do they actually tell you anything about what’s going on inside your body?

The main driver of the tissue damage that causes running injuries is internal biomechanical loading—the high-magnitude forces inside your Achilles, knee, shin, hip, etc., which cause microscopic amounts of damage with every step you take while running. So, if we want to go beyond just tracking mileage as a way to monitor your injury risk, we should be looking at these internal biomechanical forces.

Estimating biomechanical forces during running in the lab

There’s just one problem: estimating these biomechanical forces is really hard! You can’t just measure the force going into the ground (e.g. with a pressure-sensing insole) because that “external loading” can be very different from the internal loading your tissues actually experience, and the two are not even very well-correlated.

So, figuring out those internal biomechanical forces is tricky. Here's one straightforward way to do it: surgically implanting a force gauge into a tendon or bone (raise your hand if you want to volunteer for that study! Any takers?).

Barring that, the second-best option we have is musculoskeletal modeling —creating a “digital twin” of a runner, then running a computer simulation that’s consistent with the laws of physics and the principles of muscle physiology. Here’s a sketch of what that looks like:

A runner comes into a motion capture lab

We outfit the runner with reflective markers at key anatomic landmarks and take a “snapshot” of their body while standing still

They run on a force-sensing treadmill while we record the force data + motion capture data from the reflective markers

We create a “digital twin” of the runner by scaling a generic model of the musculoskeletal system to match the runner’s height, weight, limb length, approximate muscular strength, and so on

In a computer simulation, we make the digital twin of the runner move the same way and encounter the same forces that the real runner encountered, while also constrained by the laws of physics and the principles of biomechanics

This computer simulation estimates the actual muscular forces the real runner produced, which are consistent with the actual motion and forces we measured

Summing up these muscle forces acting on a particular tissue gives us the internal force we’re interested in (e.g. adding up the calf muscle forces gives us the tensile force in the Achilles tendon)

This procedure does result in pretty accurate results, but in case it isn’t obvious, this modeling pipeline is incredibly laborious and time-consuming.[2] Not to mention expensive. And then there’s the fact that it requires all your running to be done in a gait lab! Real runners do their training in the real world, so we need a better solution.

Maybe wearable sensors can shortcut around biomechanical modeling

The rationale for this project was pretty straightforward: the gait metrics you record from a Garmin or Stryd sensor may not capture everything about how you move, but they do tell you a lot—for example, “a 70 kg athlete is running at 4:40/km with a cadence of 175 spm, a ground contact time of 290 ms, and a vertical oscillation of 12.1 cm.”

Maybe that gait data contains enough information about the runner’s movement pattern to accurately predict what kind of internal forces we would measure if the athlete was running in the lab, and we went through that whole seven-step procedure above.

If we could build a predictive model that could take wearable sensor data as input, and predict internal forces (at, say, the Achilles tendon) as an output, we could avoid the entire modeling pipeline and just immediately go from sensor data to internal force estimates. Whether or not this procedure will work depends on (a) how powerful our predictive model is, and (b) whether the wearable sensor data contain enough information about gait to make an accurate prediction.

My paper set out to find answers on both of these points.

Getting the input data and the output data for a predictive model

To gather the data to build a predictive model, I made...