bmclab / bmc Goto Github PK

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Latex:

<img src="https://render.githubusercontent.com/render/math?math=\frac{\mathrm d }{\mathrm d t}\left({\frac{\partial\mathcal{L}}{\partial\dot{q}_i }}\right)-\frac{\partial \mathcal{L}}{\partial q_i }=Q_{NCi}\quad i=1,\dotsc,N">
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Image (drag and drop image file here but use html code to set its size):

<img src="https://user-images.githubusercontent.com/3271581/76967836-b1af5700-6906-11ea-8a26-ef2c89ca1460.png" height="100" >
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I have looked through your notes and they are brilliant. I have skimmed through your notes and have a shallow grasp of some of the concepts. Lets say someone has smart shoes and other wearables that can record the ground contact time, strike angle, left/right foot balance, distance, heart rate, muscle impulses, flight time, stride length, pace, cadence and so on, but not the force applied to the ground nor the ground reaction forces. Can I use inverse dynamics to determine the forces given all the other parameters? If I apply body segmentation and calculate my centre of mass and centre of pressure, can that be converted into a time series ? Does the position of the centre of mass affect posture and form ? When accounting for drag, how big is the difference in the surfaces of a cyclist and a sprinter ? Another question I have would be, if say, someone was on a wind surfing board and could accurately locate their position on a body of water using gps and also using an accelerometer on the sail, how would one apply inverse dynamics to calculate bouyancy forces, forces on an individual, and the propulsion force ?

First of all, thank you for writing such a great tutorial. It's both informative and detailed.
I'm very interested in using python to do biomechanics simulation and control.

Is there any plan for adding more tutorials on motor control? I've found it scarce not only in python control but also in biomechanics. I'm mostly interested in using control theory for biomechanics control.

Thx for all you've done!

I am trying to detect local maxima (* peaks12* used in the code below) and local minima (* peaks13*) from a vector called nrz. All peaks and valleys should be at above 0.25 and below -0.25 respectively. Since my data is per second, I am using mpd=1. I also want to have a new peak or valley only if the vector value has dropped below 0.1 or rised above -0.1. When I use the code below, it doesn’t seem to work for my case.

peak12 = detect_peaks(nrz, mph=0.25 , mpd=1, threshold=0.25, valley=False)
peak13 = detect_peaks(nrz,mph=-0.25, mpd=1, threshold=-0.25, valley=True)

Should I use another parameter when calling the function?
(I also attached a simple .xlsx file with the peaks and valleys I want to detect.
example.xlsx

Thanks for this code, it's been useful so far !

I discovered a small issue in the _plot() method. When specifying an ax object only the first reference would be plot.

The issue can be replicated as:
fig, ax = plt.subplots(ncols=3,nrows=3,figsize=(16,12))
ind1 = detectPeaks.detectPeaks(a1, show=True, ax=ax[2,0], threshold=0, kpsh=True, mpd=2, mph=5e-2)
ind2 = detectPeaks.detectPeaks(a2, show=True, ax=ax[2,1], threshold=0, kpsh=True, mpd=2, mph=5e-2)

The workaround is easy:
if ax is None:
_, ax = plt.subplots(1, 1, figsize=(8, 4))
noAx = True
else:
noAx = False
(...)

if noAx:
plt.show()