Final

Background:

This semester, I began work on a system of trackers for a whole host of potential/evidenced metrics of depression, in hopes of monitoring its cyclical nature and identifying correlations with my activity and environment. Because I had done a lot of prior research, there were specific metrics that I had in mind, but oftentimes appropriate apps were either only available for iOS, didn’t provide an API, didn’t track with enough granularity, or didn’t exist at all.

Being a grad student, I have not the funds for an iPhone (new or old), and so I decided to put my newly acquired python skills to the test.

 

Data collection with homemade trackers:

  1. Mood Reporter: Because affect is difficult to measure, psychiatry traditionally employs self-administered questionnaires as diagnostic tools for mood disorders; these usually attempt to quantify the severity of DSM-IV criteria. The module for depression is called the PHQ-9, and I’ve adapted several of its questions into my own questionnaire, which python deploys every hour via the command line:

    The responses are then appended to a tsv:
  2. Productivity: via python and the RescueTime API, my productivity score is appended to a json every hour:
  3. Facial analysis: Via my laptop’s webcam, the Affectiva API analyses my face for a minute every hour; all its responses are saved to a json file. My python script grabs the min and max attention and valence values, as well as the expressions made (plotted with emoji) and the amount of times I blinked (calculated by dividing the number of times the eyeClosure variable hit 99.9%, divided by 2). These calculations are then appended to another JSON file that feeds into my visualization. The final entry for each hour looks like this:

  4. Keylogger Sentiment Analysis: The idea for this is simply to discern the sentiment of everything I type. I wrote a keylogger in python, which collects any coherent phrase to be sent to IBM Watson’s Tone Analyzer every hour. The response looks like this:

    The API provides several sentiment categories: joy, confidence, analysis, tentativeness, sadness, fear, and anger.

 

The Dashboard:

In order to understand any of this data, I would need to create a dashboard. What was important to me was to create an environment where potential correlations could be seen; since much of this is speculative, this basically meant doing a big data dump into the browser. I visualized everything in d3js.

My local dashboard has access to the hourly updated data, which is unbelievably satisfying; the public version has about 2.5 weeks worth.

 

Next steps:

I’m in the process of building yet another tracker: a Chrome extension which will record my tab/window activity (the amount of which is probably/definitely positively correlated with stress and anxiety in my life!).

I would also like to add a chart that allows me to compare the trendlines of all the metrics, as a preliminary attempt to guess at correlations. This will definitely require me to do a lot of data reformatting.

I also need to visualize the data from the tracking apps I did download (Google Fit and Exist.io), and include other environmental information like weather, calendar events, etc.

Honestly, I will probably be working on this for the rest of my life lol

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