Using big data to tune the fusion of human physiology and machine physics
Staying fit is increasingly an engagement between you and the machines that tune and tone your physiology. If you're like me, you have your own favorite machines for burning fat, building strength and toning muscle. When I use my fitness club, I become one with my pet mechanisms: the treadmill, elliptical, guided weights and so forth. I adjust them to my preferences and set them to challenge me just enough but not so much that I strain and tear something.
If you're serious about machine-enhanced fitness, you soon learn to play the numbers. You learn how to adjust each mechanism to your preferred settings (duration, speed, incline, resistance, weight and more), align the settings to your age and other vitals, and monitor the impacts of utilization on your heart rate and pulse. And, if you're a geek like me, you also ponder the myriad design, engineering and calibration decisions that went into producing these marvelous contraptions. The more intimate you become with any particular machine, the more you notice when its performance begins to degrade from some operating condition that you may regard as utter perfection.
Bicycling is one of the purest and most intimate examples of human-machine engagement. Many people around the world use bicycling as a primary fitness activity, or, even more often, as a primary transportation mode that also helps them stay lean and toned. It's a bit like jogging—if you did it on a wheeled walker. And it's a bit like swimming—if you did it on a wheeled flotation device. It is flowing, aerodynamic and cardiovascular to its very core.
As I was reading this recent article on big data's use in pro cycling, I kept thinking about the conjoined performance of a human and a bike as a fused unit of motion. The activity relies on human physiology operating in concert with vehicular physics. For most people, traditional bike riding is not a terribly analytical activity, other than, say, trying to choose the right bike for your physique, figuring out which of the gears is best suited to the incline you're on or trying to recall what your lock combination might be.
But pro cyclists are a different species altogether. In order to boost their performance to the max, they must analyze every last detail of their bike's design, of their own biking apparel, of their own physiology and training, of their position and operation of the vehicles and of the biking routes upon which they compete. Where pro bike design is concerned, big data has entered the race in the form of an intensified focus on computational fluid dynamics to model every variable of this conjoined human-machine performance.
Olympic gold medals depend on this big data analysis. In addition to making the bike go faster, the article states, "the teams must push their athlete to the absolute limit of known human physical capacity. From a metabolic viewpoint, this event is considered the definitive aerobic endurance cycling test because the athlete must perform in steady state conditions at the highest possible percentage of his maximal oxygen uptake. We have to consider physiological factors—lactate acid build-up and ‘burn,’ muscle fatigue—mental stamina—pain resistance, focus—and also environmental variables (such as air density and temperature). Then there is G-forces on track cornering."
Of necessity, a computational fluid dynamics model blurs the boundaries between cyclist and cycle. The article footnotes a study where researchers modeled physiological and aerodynamic variables, including power output, estimated speed, heart rate and the onset of blood lactate accumulation, using a drag coefficient and shape coefficient as measured in a wind tunnel.
In addition, modelers must factor not just the constraints of physiology and physics, but also those laid down in the rules of pro cycling. As the article states: "You can be guaranteed that they will use every bit of cutting edge research to make the bike and wheels go faster and the riding position more aerodynamic whilst staying within the UCI (Union Cycliste International) rules."
This is all purely focused on speed and endurance, of course. But when you dial back the discussion to everyday recreational cycling, and consider the health and fitness benefits of the activity, there are important takeaways from these analytics. Aerodynamics are a fundamental determinant of two bicycling exercise metrics: resistance (of wind and pedal) and efficiency (of cardiovascular oxygen utilization). For any cyclist, these metrics spell the difference between an invigorating workout on wheels and an exhausting ordeal against wind, gravity and suboptimal road conditions.
If designers can use big data to tune bicycle performance for maximum fitness impact, these analytics can play a big role in encouraging more people to push pedals for fun and exercise.