Ultimately, I hope to predict the onset of depression by feeding this data into a neural network, and perhaps even preempt it by teaching the NN to recommend/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.
Below are visualizations of a week's worth of data collected by my homemade python trackers. The brush at the bottom of the page controls the time domain of all charts.
I choose these variables as an attempt to measure mindwandering, a marker strongly correlated with depression. The emojis were included to illustrate the concept of valence (and also just for funsies).
The area graph visualizes my productivity score as calculated by RescueTime, and is included to contextualize Affectiva's attention scores.
I have recently added keystroke dynamics and simple text analysis to the keylogger, and hope to visualize these metrics soon.
A pearson correlation coefficient matrix—based on two months of data—can be generated by comparing "All" with "All" via the dropdown menus, below. Comparing a metric with another metric, or "All", will generate scatterplots and trendlines. Tabcounter data to come.