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zntfdr |
Our devices have tons of sensors:
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).
- 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