Experimenting with the Leap Motion controller (bottom right corner)

Yesterday, we presented the results that we have achieved to half of the class at the second feedback session. It was valuable for two reasons:

• to get some feedback from the course instructors (more on that later).
• to listen to the other groups, particularly another group that is also doing a Bartender project with the Leap motion controller.

We found out that we are doing quite well compared to the other students (we have a "head start" like one instructor said) although there is a lot of work that remains to be done in the next two weeks. On a meeting we had after the feedback session, we decided that the next immediate step should be to finish with all necessary data collection, so we plan to ask some friends to attend a meeting on Tuesday where we will collect video clips of them performing hand gestures on the Leap Motion controller. Then we will use that data to train our final version of the artificial neural network (see the previous blog post) which is probably the crucial part of our project.

On the feedback session, there was some discussion about what one needs to do in order to achieve a high course grade. The head teacher said that for the machine learning groups, it is important to compare two different techniques, which means that we need to implement an additional learning method than a neural network. A simple method to implement (just for the sake of doing a comparison) would be the k-nearest neighbor algorithm, although we might also do something a little more complicated. However, the neural network appears to be working so well at the moment (at least with the data that we have collected so far) that it might be difficult to find a method that will perform better. Possibly we can find a method that performs equally well but has some advantages over neural networks, such as simplicity or ease of implementation. In addition to that, one instructor proposed that we try to go deeper into the inner workings of our neural network through visualization of its last layer.