Fusion – towards better understanding of self
usefusion.app
usefusion.app
> For example, we found out that the best time for Ore to do exercise […] is to do so in the early hours of the morning between 5-7AM the night before.
Honest typo or written by GPT?
Using this as example, here's how an insight like this comes about:
- user downloads data from different sources to device
- We use activitywatch (https://activitywatch.net/) to know what activities I'm doing on the computer + when it happens and auto categorize into different groups (e.g neurotech, angel investing etc)
- Exercise/high intensity training data is obtained from Oura ring (https://cloud.ouraring.com/docs/activity) - e.g `activity.high` (number of mins of high intensity training in a day), `activity.met_1min` (metabolic equivalent of activity in 1min granularity) variables
After pre-processing, we combine these features (amongst others), and the calculate correlation matrices + conditional probability to see the impact of given variables on desired outcome.
It still pretty early days and we hope to generate higher signal predictions from aggregated sources trying out different modelling techniques.