Speak, (Random Access) Memory is a project that hopes to monitor depression's cyclical nature, identify correlations between mood and my activity/environment, predict the onset of depression by feeding this data into a machine-learned model/neural network, and ultimately preempt it by teaching the system to recommend and enforce interventions. As a preliminary exercise, I've trained a couple ML models to predict mood with only a month of data; see the documentation here.
Presently, there are seven trackers collecting data on 45 metrics; below are visualizations of the data collected by four of them: my homemade python trackers. The last chart visualizes correlations among 40 select metrics.
Use the brush at the bottom of the window to filter and navigate through my entire history of data.
█ Final Count
A pearson coefficient matrix can be generated by comparing "All" with "All" via the dropdown menus to the right (may take a minute to generate). Comparing a metric with another metric, or "All", will generate scatterplots and trendlines.
Generate a matrix: