AI and Machine Learning for Exercise

All of a sudden it’s trendy rather than scary to talk about artificial intelligence and machine learning. So in this post I’ll explain how delivering a compelling virtual coaching experience depends on these technologies.

Let’s start with AI. It’s easy now to imagine a kind of activity-centric Siri that can produce and respond to all kinds of interesting sentences about the exercise you’re doing. In your imagination, what makes these sentences interesting? Certainly not the ability to recite your speed, steps, and heart rate.

What matters is context:

These are all things that a virtual coach has to ‘know’ about you, across a dizzying amount of contexts. And then it needs to deliver the right advice at the right time in light of this knowledge. So yes, we’ve been busy.

That’s the AI aspect: understanding context and designing appropriate and natural interactions and advice based on that context. The machine learning comes in because everyone’s context is unique.

In the example above, “high effort” was the key concept. High effort is a physiological state, and what counts as high effort will change as a user gets fitter. A virtual coach needs to learn about this. It’s the same with optimal form, which varies by person and by exercise type. Using first principles of physiology and the sports covered by our engine, our algorithms look for patterns in a user’s data, and adjust key coaching concepts as a result. Some of the patterns we can respond to immediately, because they nearly exactly match what we’ve seen and learned about before. Others the system needs to learn about from scratch for this user.

So machine learning helps us identify a particular state that an individual user is in. And our AI puts that state in context, allowing our customers to give users timely and relevant advice. Both are crucial for true virtual coaching.