Sensors
Our devices have tons of sensors:
What is an Activity Classification?
Activity Classification is a task that allows us to recognize our pre-defined set of physical actions that the user does with their devices.
In the session, the presenter shows us an example of a Fressbee throws classifier.
An example of activity data (it’s a csv table with time stamps and x, y, z values):
In Create ML we can filter which axis of which acceleration/rotation we should consider for the training, we can also define a Prediction Window Size to let Create ML know how much is the size of the sample to analyze (this way we can have multiple gestures/measurements in one table).
Best practices
Use relevant sensor for your motion (understand your motion)
Collect irrelevant motions as “other”, to avoid false positive
Provide balanced classes (same number of samples for each class/category)
Provide raw data instead of processed device motion data
