biomotioner
A biomechanical system that analyses your running data to suggest the best for your run.
Introduction
biomotioner, a biomechanical system that analyses your running data to suggest the best for your run. Starting from these data, he’ll calculate if the values are physically possible and then he will estimate, changing some variables, an improvement based on your capabilities.
Published on nuthings, it’s completely free for everybody who wants to improve his athletic performances.
the
biomechanical
core
Input parameters
These parameters are fundamental to build a consistent and versatile physical system for every run technique.
- mass: is the mass of the runner, measured with a balance.
- pass: is the distance between the takeoff and the next foot land. Measured with the 240fps video.
- a⊥ max: is the peak value of acceleration, obtained by using the Phyphox program accelerometer on the smartphone.
- vmax: is the highest value of velocity obtained by analysing 3 different sections of the 30 metres, by knowing the distance and counting the time.
- legrelaxed: is the relaxed leg length (so the distance between the hip and the ground), measured with a metre.
- leg⊥ compressed: is the length of the leg at the land instant, meaning that is the length registered between the hip and the ground when the foot is receiving the body weight. It’s measured with the 240fps video.
- legoffset: is the distance between the landed foot and the runner vertical axe at the impact instant. Calculated from the 240fps video.
Read it nowLab report
The entire physics demonstration of every parameter is on the report, check it out!
Derivatives parameters
To validate the system based on the inserted values, the program has to do a specific list of calculations, here’s each one in order.
- alpha: it’s defined as the angle between the vertical axe and the femur of the forward projected leg, at the land instant.
alpha = asin( legoffset ÷ legrelaxed )
- F⊥ max: is the max vertical force that the runner applies to the ground during tc .
F⊥ max = m ( a⊥ max + g)
- F⊥ avg: is the average vertical force that the runner applies to the ground during tc, so the value of effective force applied.
F⊥ avg = F⊥ max ⋅ 2 ÷ π
- legcompression: is the difference between the two leg lengths.
legcompression = legrelaxed – legcompressed
- kleg: the leg has an elastic behavior that allows the runner to receive an inverse force equal to the one applied.
kleg = F⊥ max ÷ legcompression
- tc: it’s the time in which the athlete can apply force to the ground to get more acceleration and more speed.
tc = π ⋅ √ ( m ÷ kleg )
- ⊥component: this calculation is core to create the system because the runner takes advantage of this force to move forward, not above.
⊥component = F⊥ avg ⋅ cos(alpha)
- F⊥ takeoff: This parameter represents the value of the velocity during the takeoff. This fraction of time is when the runner is leaving the leg and is starting the tf (fly time).
v⊥ takeoff = [ ⊥component – (m ⋅ g) ] ÷ m ⋅ tc
- tf: this is the value of the runner’s flying time.
tf = 2 ⋅ v⊥ takeoff ÷ g
- f: by definition, the frequency is the number of iterations of a motion in a period of time.
f = 1 ÷ ( tc + tf )
- v// estimated: it’s the estimation based on the other parameters. It’ll be compared with the real value.
v// estimated = pass ⋅ f
- v⊥: it’s the velocity calculated using the motion law of the uniformly accelerated rectilinear motion, on the free fall case.
v⊥ = g ⋅ tf ÷ 2
- v//: it’s the real value based on v⊥ and vmax .
v// = √ ( vmax2 – v⊥2 )
improvements
In improvement, the program uses data calculated based on the user’s data and returns new (improved) values, indicating the new value and the percentage improvement in the relevant parameter, for the current category, i.e., the one the athlete is in and which the program has confirmed. Improvements occur within a maximum of +10%, as it would be unrealistic for an average athlete to improve further. To determine whether improvement is possible, the angle is checked; if this falls within a range of ±20% of the reference value, the improvement is made.
+6.8%
Potencial increase
+10%
kleg and alpha increase
In improvement, the program uses data calculated based on the user’s data and returns new (improved) values, indicating the new value and the percentage improvement in the relevant parameter, for the current category, i.e., the one the athlete is in and which the program has confirmed. Improvements occur within a maximum of +10%, as it would be unrealistic for an average athlete to improve further. To determine whether improvement is possible, the angle is checked; if this falls within a range of ±20% of the reference value, the improvement is made.
+7.1%
Potencial increase
+10%
kleg and alpha increase
In improvement, the program uses data calculated based on the user’s data and returns new (improved) values, indicating the new value and the percentage improvement in the relevant parameter, for the current category, i.e., the one the athlete is in and which the program has confirmed. Improvements occur within a maximum of +10%, as it would be unrealistic for an average athlete to improve further. To determine whether improvement is possible, the angle is checked; if this falls within a range of ±20% of the reference value, the improvement is made.
+5.4%
Potencial increase
+10%
kleg and alpha increase
