Envisioning Physical Learning, Lightning-Fast

 

Recently I saw something pretty cool. It’s a motion capture device, but it doesn’t use cameras. As you might’ve already guessed, it uses some well-placed (and talkative) silicon instead. Imagine a suit you put on that will record your every movement in three dimensions, via 17 sensors and a wireless data feed.

This has some pretty cool features. Whereas the traditional motion capture systems we are familiar with require things like a nice camera setup, a well-lit space, and a suit covered in little balls, the Motionwerx suit just needs your movement, and a computer. Further, if you think about it there are some obvious places motion capture as-we-know-it has a very hard time indeed, such as picking up movements that are hidden by the mover’s body position, or by a partner, when more than one person is moving together. And underwater, for example? I haven’t seen anything yet that can do that. Until now, that is.

 

Okay, so that’s all very great. But here’s a thought about a potential killer app from the oh-my-god-this-could-change-everything department. What about the field of motion emulation, which is to say, learning to do things that require movement, like play sports? Or learning to dance? Imagine for a moment, once a motion capture is acquired, you could put on a Motionwerx suit, and receive rapid feedback as you attempt to emulate the movements you want to learn. Want to swing a golf club exactly like tiger woods? Learn the Rumba? Swim like Michael Phelps? Grapple like Marcelo Garcia? Now, theoretically, you could, and with a minimum of overhead.

I imagine it would look something like this: A model of the motion profile captured from the athlete whose movement is to be emulated is loaded into the learning console.

As you move with the suit on, you receive feedback indicating how closely you match the target, perhaps via tiny vibration packs near each of the sensors, as well as an audible tone that rates an assignable metric, and a visual color coded display which can be observed in real time and reviewed after each attempt. The feedback system is optimized to coach you to improve incrementally with each attempt, setting a new, achievable goal each time, helping you home in closer to target each time. And because the sensors are in so many distinct locations, it is possible to work on different aspects sequentially, e.g. legs, then arms, then head, and then focusing on left elbow, right elbow, etc…

 

Naturally, you may not be physically identical to the person from whom your target motion is captured. Therefore there are some interesting calibrations to be done to  your own baseline profile, determined by your size and range of movement. It could actually be quite fascinating to experience firsthand in your own body the best match to the movements of several different successful athletes and interpret how these different styles of movement feel in your body and then develop your own, perhaps changeable, modes of performance. Babe Ruth, for example, had a highly unconventional, yet highly successful style of hitting a baseball–rocked back on his heels, and with an endomorphic body type to boot–which would be possible to mimic in your own body and then contrast with the hitting style of Jose Canseco or BarryBonds, again, in your own body.

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