Sensors - 2

I was mainly working with the sensors this week. I try to find out where we should position the weight sensors on the seat. One of the problem is that I have only two sensors yet, so I had to run several tests to know where they are useful or not. The difficulty is that in order to obtain comparable results, I have to act exactly in the same way while testing the sensors. I quickly designed a protocol with some classic drivers' actions and I try to run the tests.  The results for the different positions were hardly comparable but I still managed to choose some potentials interesting positions for the sensors on the backrest.

After discussion with Ronnie, he suggested me to do a timed Powerpoint presentation in order to know exactly how long each action has to go on. So, I did that for the tests on the cushion and it works much better. The results were much more similar so I could easily select some potential interesting positions for the sensors.

In an other hand, I improved the acquisition program, the new version is not totally working yet but  it should not take me to much time next Monday or Tuesday.

Test protocol

1. Define some classical drivers' actions 
2. Find a position for the sensor
3. Run the experiment and collect the data
4. While all potentials interesting positions are not tested go back to 2.
5. Normalize the data
6. Select the most promising data (the ones which provide different result and if several positions give the same patterns choose the one with the biggest amplitude after normalization)

Further work

• Do the same with the distance sensors 
• Run the experiment with other people with different morphology and see if we still have interesting results. I will certainly rune the experiments with few people and see if I can detect which movement they are doing based on the sensors' results. So, I will run some machine learning algorithms for a classification problem, if I get a good percentage of recognition we will consider that the sensors are well positioned and that we find some interesting features to analyze.