Research at Hypnos

Machine Learning at Hypnos

Our dataset resources are the followings:

Supervised Learning


Data structure for training

Our data is a time series and we needed our algorithms to recognize previous and next data for a specific event. To do so, we prepared windows with various sizes containing heart rates, heart rate mean, heart rate consecutive differences etc. if we are going to be training with heart rates, for example.

Used Algorithms

We used following algorithms for machine learning,

Observed Results

These are currently the most succesful models trained with machine learning.

Unsupervised Learning


Continuous learning

The purpose of unsupervised learning is to support the supervised model in its classifications. As shown in the diagram, the outlier detection model is trained real-time with users' own data. This data is continuously fed to the model. As more and more a user sleeps with Hypnos, the more Hypnos will "get used to" the user, as a result, the more accurate its personal feedback will be.