Compensatory Coordination, Justifying your Alpha, and Kinematic Sequencing

Apologies for not providing a newsletter at the end of last month. I was focusing on defending my Ph.D. and did not have the time to organize the newsletter. I am happy to share that I successfully defended my dissertation :).

🦾 Biomechanics, motor control, and (re)training  recommendations

A distal external focus of attention facilitates compensatory coordination of body parts

I like this paper because rather than simply outlining that an external focus of attention improves outcomes, it also outlines that the processes individuals use to support those outcomes are changed depending on the focus of attention. This work, along with other exciting work, suggests modifying individuals' attention during exercise or sports tasks is crucial for motor learning.

Women with patellofemoral pain show lower motor complexity and a deficit in muscle coordination to execute gait

This research adds to the existing literature that measurements of movement variability can help extract insights regarding people's pain or injury status. However, it would also be nice for future experiments to also include in their research that the more traditional measures in biomechanics were not sensitive enough to find these differences between populations to justify more intensive methods, such as the self-organizing neural network used in this analysis.

An apparent contradiction: increasing variability to achieve greater precision?

Along the same lines of examining movement variability, this research found that people increased functional variability (i.e., the compensatory coordination of body parts) to increase the stability of their gait patterns in increasingly precarious environments.

🤖 Statistics and machine learning recommendations

Meta's Movement SDK for Unity

Meta recently released their own pose estimators using just their Occulus Headset. Their SDK can be found at the following link, but what's impressive to me is how well they seem to estimate what the lower extremity is doing using only a headset and two hand sensors:

Of course, this current iteration isn't going to provide gold-standard-level data for rigorous research applications. But, the ability to collect reasonably plausible data coupled with the potential for modifying visual information using virtual reality could be very interesting for various other applied scenarios!

Markerless vs Marker-Based vs Inertial Motion Tracking

I highlighted move.ai recently, but here is an interesting talk they provided about the future of motion capture.

Justifying your Alpha

To ensure everyone is on the same page, a p-value is the probability of observing a test result equal to, or more extreme, than you currently observed under the assumption that the null hypothesis is true. If the p-value is sufficiently low, we reject the assumption of the null hypothesis, and we say the results are "statistically significant." The alpha in a research study is the probability threshold in which we would declare a result statistically significant.

Although there is a seemingly endless supply of papers about selecting and justifying p-values, I like the approach of this paper to encourage researchers to justify their alpha for any research study instead of always selecting a default of 0.05, 0.005, etc.

🎙 Podcast recommendation

I liked this podcast as it got me thinking more about the role of the kinematic sequence in pitching (and swinging). The "kinematic sequence" in rotation-heavy sports (baseball, golf, etc.) refers to the temporal sequencing of body segments during a swing, throw, or strike. A widely considered "correct" kinematic sequencing of segments refers to a proximal-to-distal sequencing of segments whereby the pelvis first reaches its peak rotational velocity about the vertical axis, followed by the trunk, then the upper arm, then the forearm, and finally the hand.

In this podcast, Dr. Gray outlines some previous research by Scarborough and colleagues (e.g., Kinematic Sequence in the Overhead Baseball Pitch) explaining that elite athletes often deviate from the widely recommended proximal-to-distal sequencing of body segments during their pitches.

This is a relatively short podcast that doesn't get into the weeds. Still, it at least got me thinking more about how and why athletes, especially baseball pitchers, may deviate from this widely regarded "correct" strategy. The justification for sequencing segments in this manner is for the athlete to throw as hard as possible with as little strain as possible on their joints.

However, is the goal of a pitch to always throw as hard as possible, or is it to ensure the hitter cannot strike the ball? This is a straightforward question at face value, but sometimes I think we lose the forest for the trees when we do these biomechanical analyses. In most cases, I'd argue we only care about the latter and how fast someone can throw a ball is only necessary insofar as it supports the "actual" goal of ensuring a hitter cannot make good contact on the ball. Therefore, the ability of a pitcher to adjust the timing of when different segments reach peak velocity could be advantageous for masking ball release, which makes it much harder for the hitter to make good contact with the ball. Thus, although an elite pitcher could probably throw harder, and with less strain on their joints, by maintaining a "correct" sequencing of their segments, perhaps they've self-organized into a state where they constantly vary the temporal sequencing of their segments to make their pitches harder to hit.

I have no data to support these hypotheses, and the baseball literature is certainly sparse with any of these types of studies. I'm sure someone else is working on answering these questions, but these topics are undoubtedly top-of-mind for me.

🗣 Quote of the month

"If you are working on something that you really care about, you don’t have to be pushed. The vision pulls you."

- Steve Jobs