Running, Alex Hutchinson notes, is surprisingly complicated:
The physiologist and coach Jack Daniels once filmed a bunch of runners in stride, then showed the footage to coaches and biomechanists to see if they could eyeball who was the most efficient. “They couldn’t tell,” Daniels later recalled. “No way at all.” Famously awkward-looking runners like Paula Radcliffe and Alberto Salazar sometimes turn out to be extraordinarily efficient. Smooth-striding beauties sometimes finish at the back of the pack.
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One solution to this problem is to slow it all down. Film a runner and watch the footage in slow motion. Or better yet, attach a bunch of markers to key joints, feed the data into a computer, and create a three-dimensional model of the runner’s stride, so that you can analyze every joint angle and acceleration at your leisure. That’s what biomechanics researchers have been doing for years now, trying to link certain gait characteristics — a knee that rotates inward more than usual, say — with particular injuries like patellofemoral pain or IT band syndrome. They’ve had hints of success, but overall the results have been somewhat muddled and hard to interpret.
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They ran the data from 3D gait analysis of a bunch of runners, some injured and some healthy, through a form of artificial intelligence called unsupervised machine learning, to see if it could group the runners into categories based on their strides, and whether those categories would reflect the types of injuries the runners were subject to. The answers — yes to the first question, no to the second — are both worth thinking about.
Before “motion pictures” were a thing, Muybridge made a pack of photographic flipcards called “Horse in Motion.”
https://en.wikipedia.org/wiki/The_Horse_in_Motion
It was commissioned to help analyze the gait of race horse.