
Live classifying human movement through machine learning - janjongboom
http://blog.telenor.io/2015/10/26/machine-learning.html
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huac
You have literally 6 features; you do not need to do dimensionality reduction
here. Further, PCA is used generally before performing a linear regression
model, which assumes independence among variables. SVM's are nonlinear and so
don't care (though you specify both a linear kernel and the RBF kernel in your
code?). If the data was very high dimension you could do PCA and then SVM in
order to increase _speed_ at the expense of _accuracy_ \- there is no free
lunch. I think that's what you see in your results.

As a side note, SVM's and Random Forests are generally considered the best
'out of the box' models. So you chose a good one to experiment with!

Also, since you already wrote the analysis code in Python, why not write the
front-end in Python (rather than Node) as well?

~~~
rmz
> You have literally 6 features; you do not need to do dimensionality
> reduction here.

We do have six features, but they are not rotation invariant. I was hoping we
could find the "axis of maximal motion" (or something along those lines)
within each sample period, project the movement along that axis, and then do
FFT to find what kind of bouncing around the test subject were doing. That
didn't work out too well in practice, but it still sounds like the smart thing
to do :-) What do you think?

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vonnik
I love posts like this, but I suspect another set of algorithms is going to
win this race. Probably some combination of convnets and recurrent neural
nets.

[http://www.unnvision.com/pub/DDNN_ECCVW2014.pdf](http://www.unnvision.com/pub/DDNN_ECCVW2014.pdf)
[http://jmlr.org/proceedings/papers/v32/pinheiro14.pdf](http://jmlr.org/proceedings/papers/v32/pinheiro14.pdf)

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dharma1
[https://www.moves-app.com/](https://www.moves-app.com/) also uses machine
learning to classify walking, running, cycling etc. Not sure what algos but
there are some pointers on the founder's website -
[http://www.cs.cmu.edu/~akyrola/](http://www.cs.cmu.edu/~akyrola/)

