Using OpenCV as a stress detector

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I think you could use a Support Vector Machine (SVM). It can easily handle 9 dimensions. Train your SVM with positive and negative samples (stressed / not stressed). You can then classify your live data every second, it's very fast. By averaging the results over time you should be able to filter out false classifications. But that depends of course on the quality of your data.

I think the problem can be formulated as a clustering of data into 2 categories (stressed, not stressed) (see answer to question in this q&a entry) -- main difference with that situation is that here we know how many clusters to create. The openCV method for clustering in N-dimension is cvKMeans2. The method is quite easy to use, just being careful to create a matrix where each row contains a sample vector, in this case the 9 points mentioned.

The results would be optimal if the underlying distributions are concentrated, i.e. there are indeed two states to distinguish. Computationally speaking k-Means is fast because you identify a termination criteria via number of iterations and/or solution accuracy; the larger the gradient the faster it would converge, and the gradient is basically the concentration of samples. LDA or similar approaches rely on creating a big matrix description of the system, then inverting the matrix or solving the large linear system at once, vs. the iterative approach of the k-Means algorithm.