Circadian Phase Estimation and Deep Learning

One of the most common questions we get at Arcascope is…

“Can’t you just do circadian phase estimation using machine learning?”

Living in the data age, we have become used to thinking that big data and machine learning can do just about anything. In this post, I will break down some of the unique challenges for circadian phase estimation with an eye towards machine learning techniques.  I’ll also do a brief review of the previous attempts to apply machine learning to this task. 


Wearable Headaches (and how to fix them)

So you want to get somebody’s internal time from a wearable…

Let’s talk about wearable data. On the one hand, wearables are an incredible innovation, allowing self-quantification and anomaly detection with unprecedented ease, at unprecedented scales. 

On the other hand, they’re a data science nightmare. Or three nightmares, really. 

Nightmare #1: All the devices are different, and you have to use different ways to get raw data off them. 

Sure, apps like Apple Health that act as clearinghouses make this easier for you. But you can’t use Apple Health for everything. Sometimes, wearables require permission to be granted for you to access their full data. Sometimes, wearable companies go out of business after you’ve built an infrastructure to work with them. 

Can you process heart rate signals from two wearables using the same algorithm? What if they decide what counts as a “step” in different ways? What if the firmware changes? People have certainly thought about these questions, and that’s the whole point: you have to think about them. The effort of keeping track of everything adds up.