Clearest and most succinct explanation of the UCM I’ve heard so far. Thank you Andrew & Rob 🙏 Incredibly exciting to hear about active, current research that is aimed at shedding light on how motor synergies and efficiency can be understood mathematically. I feel like this must be the new frontier of coaching. Can someone reason this through with me? If it is possible to measure the effective UCM for different tasks and phenotypes, then we have a better way of measuring motor efficiency, am I understanding that correctly? ‘Just getting stronger in the weight room’ has been an outdated training strategy in a while now and surely no one listening to the Perception Action Podcast is stuck on those tracks. Action capacity training, as Frans Bosch establishes in such great detail, must be closely related to the tasks we’re aiming to improve in. If our strength training is aimed in the wrong direction, it might interfere with greater athletic performance. It appears to me that the UCM analysis is a way to more directly measure if performance increases are due to improved coordination or due to improved strength. In other words, I could do a UCMA for my athlete at the beginning of off-season. Have them go hard in the weight room. And measure their UCM performance a training cycle later. If they can throw faster while keeping the same accuracy, but they didn’t move closer to the optimal UCM zone over all recorded trials, then I can extract that my gains came from the strength training. Now I could compare that to a group of athletes who are on a minimal action capacity training regime. But they follow the CLA. Now at the end of my training cycle I can do an UCM analysis and see if they are moving more closely to the optimal zone. If they still improved in performance and can throw faster, I can attribute their gains to improved motor synergies, not just to brute strength gains. And I can now do similar tests with my group that does Frans Bosch style conditioning plus follows the CLA. In theory I will be able to effectively see, how much of my performance improvements are due to efficiency/synergy/coordination gains - and how much performance improvements are due to improved brute strength/action capacities. Am I getting that right? If in theory the above follows valid reasoning, then given that these mathematical models that take data from visual analyses can be translated into algorithms, we could track athletes improvements in the direction of greater synergy (and therefore greater efficiency) pretty much in real time, right? The 2D video to 3D model software is getting better and better, being able to take foreshortening into account accurately. That means we could have constant real-time data (for tasks for which the outcome parameters are defined) and see how closely our athlete is operating in the functional uncontrolled manifold. We could test any practice intervention with it. Surely, the pathway of learning isn’t linear, so we take momentary deviations from the UCM as necessary and useful explorations of the motor space - but we could at least detect if an athlete hasn’t moved towards greater efficiency in days, weeks or months. And EVEN IF their performance has improved (due to increased strength), if their are not moving towards the optimal UCM and the movements aren’t occurring with enough synergy, it might hint at a possible overuse injury soon to occur. (Due to increased forced demands - but if synergy doesn’t increase alongside the demands it means the force will be distributed over a smaller portion of tissues.) All in all I’m incredibly excited for the developments in the field. 🙌 If there are any obvious gaps in reasoning or misunderstandings of the subject matter, I would love to be corrected!
The elbow has 2 dimensions of movement and the wrist has 2 as well. What you percieve as wrist rotation is actually a flextion and extension, combined with the elbow’s pronation and supination.
I love this discussion 🙏. It seems like UCM analyses could be a high-leverage-point for improving skill acquisition *while simultaneously reducing overuse injury risk.* I’m hoping more UCM research is going to give us yet more evidence for skeptical “correct-only mechanics” coaches to help move toward training for variability. Or at the least, to become more tolerant of movement that’s not perfectly aligned with the One Ideal Biomechanics technique. It will also give us practitioners more support against the straw “so you’re saying any possible random movement is fine?” 🤦🏼♀️. I reckon ANYTHING that provides data showing skillful outcomes are possible within a bandwidth rather than following an ideal prescription is going to be great… and the UCM helps us define boundaries. Ofc I can see it also being used as a way to shut down exploration that falls outside the UCM, but still… its better than where we are now. I guess it could be used as just a somewhat better version of “one ideal”, and once again I’m counting on folks like Andrew to emphasize when we are looking at data that DESCRIBES but should not necessarily be mistaken for PRESCRIBES. But even if UCM *is* used as prescription, it’s still better to have “acceptable variability bandwidth” prescription than the “one idealized biomechanics”. As always, I aim for the world in which we accept that it might *sometimes* be training deliberately way outside the UCM that allows us to more quickly acquire that which puts us more often and more fully into the “good variability” zone.
Stoked to read your comment on a similar intuition about how UCM analyses could be applied in the motor learning field. With applying the UCM sensibly as a tool that can tell us if we’re ‘moving towards synergy’ or moving ‘away from synergy’ I have the hope that it will pull targeted exploration more onto the map - with us knowing through Rob’s research how beneficial exploring new solutions is to optimising movement efficiency. But I also notice within myself the tendency to see this as ‘the new IT tool’ where practically speaking I’d benefit from doing UCMA myself to understand better what kind of tasks it can accurately measure and which kind of tasks it doesn’t yet do a good job for. In either way, absolutely looking forward to seeing how this field will develop in the coming years!
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Clearest and most succinct explanation of the UCM I’ve heard so far. Thank you Andrew & Rob 🙏
Incredibly exciting to hear about active, current research that is aimed at shedding light on how motor synergies and efficiency can be understood mathematically.
I feel like this must be the new frontier of coaching. Can someone reason this through with me?
If it is possible to measure the effective UCM for different tasks and phenotypes, then we have a better way of measuring motor efficiency, am I understanding that correctly?
‘Just getting stronger in the weight room’ has been an outdated training strategy in a while now and surely no one listening to the Perception Action Podcast is stuck on those tracks.
Action capacity training, as Frans Bosch establishes in such great detail, must be closely related to the tasks we’re aiming to improve in. If our strength training is aimed in the wrong direction, it might interfere with greater athletic performance.
It appears to me that the UCM analysis is a way to more directly measure if performance increases are due to improved coordination or due to improved strength.
In other words, I could do a UCMA for my athlete at the beginning of off-season. Have them go hard in the weight room. And measure their UCM performance a training cycle later.
If they can throw faster while keeping the same accuracy, but they didn’t move closer to the optimal UCM zone over all recorded trials, then I can extract that my gains came from the strength training.
Now I could compare that to a group of athletes who are on a minimal action capacity training regime. But they follow the CLA.
Now at the end of my training cycle I can do an UCM analysis and see if they are moving more closely to the optimal zone. If they still improved in performance and can throw faster, I can attribute their gains to improved motor synergies, not just to brute strength gains.
And I can now do similar tests with my group that does Frans Bosch style conditioning plus follows the CLA. In theory I will be able to effectively see, how much of my performance improvements are due to efficiency/synergy/coordination gains - and how much performance improvements are due to improved brute strength/action capacities. Am I getting that right?
If in theory the above follows valid reasoning, then given that these mathematical models that take data from visual analyses can be translated into algorithms, we could track athletes improvements in the direction of greater synergy (and therefore greater efficiency) pretty much in real time, right?
The 2D video to 3D model software is getting better and better, being able to take foreshortening into account accurately.
That means we could have constant real-time data (for tasks for which the outcome parameters are defined) and see how closely our athlete is operating in the functional uncontrolled manifold.
We could test any practice intervention with it.
Surely, the pathway of learning isn’t linear, so we take momentary deviations from the UCM as necessary and useful explorations of the motor space - but we could at least detect if an athlete hasn’t moved towards greater efficiency in days, weeks or months. And EVEN IF their performance has improved (due to increased strength), if their are not moving towards the optimal UCM and the movements aren’t occurring with enough synergy, it might hint at a possible overuse injury soon to occur. (Due to increased forced demands - but if synergy doesn’t increase alongside the demands it means the force will be distributed over a smaller portion of tissues.)
All in all I’m incredibly excited for the developments in the field. 🙌
If there are any obvious gaps in reasoning or misunderstandings of the subject matter, I would love to be corrected!
The elbow has 2 dimensions of movement and the wrist has 2 as well. What you percieve as wrist rotation is actually a flextion and extension, combined with the elbow’s pronation and supination.
I love this discussion 🙏. It seems like UCM analyses could be a high-leverage-point for improving skill acquisition *while simultaneously reducing overuse injury risk.* I’m hoping more UCM research is going to give us yet more evidence for skeptical “correct-only mechanics” coaches to help move toward training for variability. Or at the least, to become more tolerant of movement that’s not perfectly aligned with the One Ideal Biomechanics technique.
It will also give us practitioners more support against the straw “so you’re saying any possible random movement is fine?” 🤦🏼♀️.
I reckon ANYTHING that provides data showing skillful outcomes are possible within a bandwidth rather than following an ideal prescription is going to be great… and the UCM helps us define boundaries. Ofc I can see it also being used as a way to shut down exploration that falls outside the UCM, but still… its better than where we are now.
I guess it could be used as just a somewhat better version of “one ideal”, and once again I’m counting on folks like Andrew to emphasize when we are looking at data that DESCRIBES but should not necessarily be mistaken for PRESCRIBES.
But even if UCM *is* used as prescription, it’s still better to have “acceptable variability bandwidth” prescription than the “one idealized biomechanics”.
As always, I aim for the world in which we accept that it might *sometimes* be training deliberately way outside the UCM that allows us to more quickly acquire that which puts us more often and more fully into the “good variability” zone.
Stoked to read your comment on a similar intuition about how UCM analyses could be applied in the motor learning field.
With applying the UCM sensibly as a tool that can tell us if we’re ‘moving towards synergy’ or moving ‘away from synergy’ I have the hope that it will pull targeted exploration more onto the map - with us knowing through Rob’s research how beneficial exploring new solutions is to optimising movement efficiency.
But I also notice within myself the tendency to see this as ‘the new IT tool’ where practically speaking I’d benefit from doing UCMA myself to understand better what kind of tasks it can accurately measure and which kind of tasks it doesn’t yet do a good job for.
In either way, absolutely looking forward to seeing how this field will develop in the coming years!