Each of us appears to have a unique way of moving, a physical “signature” that is ours alone, like our face or fingerprints, according to a remarkable new study of people and their muscles. The study, which used machine learning to find one-of-a-kind patterns in people’s muscular contractions, could have implications for our understanding of health, physical performance, personalized medicine, and whether and why people can respond so differently to the same exercise.
Intuitively, most of us probably know there is something in the way we move, and that something defines us. Most people can pick out their friends and loved ones, based solely on how they walk.
Those identifications, whether fond or creepy, rely on external cues about how we look in motion and depend on anatomical features, such as height, limb length, or how we swing our arms, which may not be stable.
So, for the new study, which was published this month in the Journal of Applied Physiology, French and Australian researchers decided to turn to algorithms to ferret out whether unique, personal muscle patterns exist.
The scientists began by recruiting 80 healthy men and women of varying physical sizes and fitness and inviting them to a human performance lab.
There, the volunteers sat on stationary bicycles while researchers adjusted the pedals, handlebars, and seats so that everyone’s riding style would be the same. The researchers also attached electrodes to eight of the muscles in the volunteers’ legs. The electrodes were designed to read and record electrical activity in those muscles while they contracted. Then the volunteers cycled for 90 seconds multiple times at a range of pedaling speeds.
Next, they moved over to treadmills and, still wearing the electrodes, walked barefoot during multiple 90-second strolls. Several days later, most of the volunteers returned to the lab and repeated the cycling, walking, and electrodes routines. Finally, the program’s algorithm was directed to differentiate the movement patterns, without names attached, and assign them to the correct volunteer.
It turned out to be quite adept, accurately recognizing anonymous movement patterns more than 99 percent of the time when using readouts from all eight muscles. Taken as a whole, these data suggest that people have distinct, detectable, and durable ways of using their muscles, said François Hug, a professor of movement science at the University of Nantes who led the study. The individual patterns remained recognizable even from one day to the next.