Gesture Concept When we talk about finger gesture, you might already have a lot of different gestures in mind. The gesture of thumb up, the gesture of rock, the gesture of shaka, the gesture of love, the gesture of victory and many others. If you think of any of these gestures, you should be able to picture how these gestures look like: they look like symbols! They are static position of the hand where the fingers don’t move.
However, the finger gestures in our context has an opposite focus. We want you to move your fingers! So the gesture looks more as a motion, thus instead of asking what’s the shape of your gesture, we would ask “what’s the move?” Some other simple finger gestures that might be seen in daily life could help you immediately understand this different focus.
Gestures like flick, snap, tap... when mentioning them, you probably see the motion of the gestures instead of one shape, isn’t it? Option 1: we introduce now the 4 gestures Still, we strongly suggest you to start with simple gestures like flick, snap and tap so that you understand the requirements for the gestures. (If we are going to stick to this option, then the examples in the component part should be changed to two among these four)
Gesture Components In the previous section, we said that there are certain requirements for the gestures. In order to better explain those requirements, we need to show you how we breakdown the gesture movement. We will use the index flick and the index tap as examples:
Gesture Requirements (What & Why) You might wonder why there are some requirements for our gestures.. Here we try to get you a first insight on our technology: Theoretically when you repeatedly perform the same gesture, each biomechanical signal that you produce should be always the same. In reality this is true only when you reach a level of highly repeatability of the gesture, so these requirements are meant to get you as close as possible to the optimal performance. This means that following our requirements, you will be able to transform your gestures in a unique but coherent template that can be always recognised by FlickTek algorithm.
Everyone’s finger gestures generate unique biomechanical signals. Therefore you will need to record them in the Clip so they can be recognised. And to make sure the algorithm finds the repeatable pattern, you will be asked to repeat the gestures for several times until the algorithm is confident about what to look for. The followings will be a description on how to use the App to record the gestures. Since everyone does their gestures differently, we need to show Clip the gestures that you are going to perform and show it for enough times so when you do it again in the future, Clip will be able to recognise them. And this calibration process (record your gesture) is where it happens. When you press start, you will see the screen telling you which command this gesture is going to be linked to. You need to choose a gesture that pass all the training and to perform for five times. After every repetition, you will see the screen update to provide you feedback. Normally, you should just be informed that you are getting closer to get this gesture done. But you may also receive two different reminders. The first one tells you that this current recording is very far from you previous ones. You need to do them again. The second one tells you that at least one of your previously finished gesture has data pattern too similar to the current one. You should try to avoid this new gesture or we might mix them up.
When you finish all you four gestures, you are good to use Aria. But before that, you could also test if Aria is responding your gesture correctly by testing them out. Inside the Testing App you can: Freely check that your gesture are well recognized Run a test where the given a random sequence of gestures, you’ll be asked to perform them one by one and then get a score based on your performances.