CLASSIFYING STEPS WITH MACHINE LEARNING

When we first began to explore the idea of building a step classifier, we knew we would be constrained to a very limited population of individuals (Jawbone employees) available to us for early development and testing. It seemed certain that the development of the classifier would be very iterative in that, as we tested larger and more varied sets of individuals and behaviors, we would undoubtedly find issues that we needed to quickly correct. So we would need a technical approach that was suited to rapid updates and that those updates would need to be essentially risk free. We could not afford the risk and development time of actually writing new code as we iterated. In short, we needed a step classifier that learned.