Mise à jour de 'README.md'

README.md

@@ -32,9 +32,27 @@ You can find a picture of the haarcascades algotithm in the picture section.
 
 We then export a .txt file with all the average color to use it in an octave code.
 
+### FOURTH STEP - Heart rate determination
+
+Concerning this step, we decided to did it in octave, and it is devided into 5 main steps :
+
+	- Firstly, we have to extract the data from the txt file which is the output of the python code. The data in this file are actually the red, green and blue average of each sample of the 1 min video. In order to do that, We use functions to open, extract and close the file and store the data into 3 different vectors : R, G and B.
+
+	- Secondly, we normalize those three vectors by substracting the mean value of each and dividing by there standard deviation. Normalizing data aims to put into a standard form. It ensures that all of our normalized data have an average equal to zero and a standard deviation of 1.
+
+	- Thirdly, we use the algorithm FastICA (Independent Component Analysis) in order to transform our matrix of our normalized values (each row being each vector) into a new matrix composed of three channels of 450 elements each.
+
+	- Then, we compute the magnitude of the Fast Fourrier Transfrom of the new data and identify the index of the maximum of each channels so that we can relate it with its corresponding frequency. 
+
+	- Finally, we display the maximum of those three frequencies multiplied by 60 so that we convert Hertz into BPM.
 
 
-### LA TU METS TON CODE NATHAN
+
+### Conclusion & Results
+
+
+To conclude, the output of our code is 8 BPM which is obviously false. There must be an error into the main code at the frequency identifying part. However, considering the fact that we struggled with FastICA, there might be an error into the algoritm that we don't understand for the moment.
+
 
 ### POSSIBLE ALTENATIVES