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.
We used following algorithms for machine learning,
These are currently the most succesful models trained with machine 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.