In order to collect a maximum amount of data, different kinds of sensors will be used for the experiment (some of them will not be used for the analysis, at least non for mine).
The sensors have to respect some constraint, the most important is that they have to be non-intrusive. We have to keep in mind that our system could be used in a normal (future) car and works for anybody. Then everything has to be integrated in the car (for a low cost) without any constraint for the user.
Then the sensors, I will use are:
- eye tracking system (can be included in the dashboard).
- affectiva q-sensor: which provides skin conductance, temperature and movement of the arm data. It is the most intrusive sensor that we have since driver need to wear a wristband.
- seat sensors: including pressure and position sensors, it is part of my project to choose how to use them to track the position of the driver. These sensors will be integrated in the seat or the dashboard.
- we may use other sensors as pedal pressure sensor, microphone, ...
- the experiment will be recorded but the video is not part of the machine learning analysis.
Work on the sensors
- I "played" with the seat sensors to see how to use it and how hey react. I find out that the distance sensors are not robust when the object is far away (20-30cm) even if the official working range distance is 4-30 cm.
- I wrote a small program using the phidgets API in order to record the data generated by the seat sensors. The program is working (a demo has been done to Ronnie) but I will improve it in the next weeks to make it more efficient.
Further work
- Improve the acquisition program for example make it robust to crash.
- Find out how to integrate the sensors in the seat (or the car) in order to collect the most relevant data.
External links
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