I appreciate your support. Thank you for the comment.
There will be more self-quantification software projects in the near-future. Ideally involving sensors and geolocation data.
Edit:
Tonight I found a recently uploaded YouTube video, describing a self-quantification hardware/software setup involving sensors and Raspberry Pi computers. Which is, everything considered, rather similar to what I would like to do next.
Here is the video:
I am glad others find this sort of software experimentation interesting as well. Thank you for your comment and positive feedback.
I recently acquired a Raspberry Pi Zero and second generation RPi camera. Although I was initially unsure about the hardware, the Raspberry Pi camera now looks outside through a window in my home. And can stream a secure video feed to any other computer on my LAN.
But I wanted to go deeper, to turn the camera itself into a self-quantification tool.
So I wrote a program with Python, that utilizes the RPi camera to take measurements of brightness every thirty seconds. Then save each record to a CSV file (with timestamp and brightness values) for later analysis.
What you see below are the first thirty two hours (or so) of collected data. The x-axis is “time”, whereas the y-axis is “brightness”. And each calendar date has its own color:
I am initially running this brightness detection program for seven days. There may be, however, reasons to extend this experiment further. To perhaps a month’s worth of measurements? That would be interesting to see visualized.
Would be interesting to see several years of worth of data.
Here in the UK we have been experiencing “anti-cyclone gloom” for a week or more which would cause the brightness level to be much lower than average.
Indeed it would be interesting to visualize that much information. Especially if data could be gathered from multiple locations simultaneously.
Based simply on two nights worth of measurements, it is possible and interesting to see different, consistent ambient light levels. Here is a screenshot of what I’m referring to:
Not only can we see daylight hours getting shorter, one night was clearly darker than the other.
Edit:
Here is the final visualization from four days of taking measurements:
Next I will be using the same Raspberry Pi camera to measure both brightness and color temperature, every thirty seconds. I will then graph both sets of measurements, overlaying each other, for a more interesting readout.
After my next self-quantification experiment, I will likely have acquired numerous other RPi sensors. And will be putting them to work as well. Who knows what sorts of insights we’ll discover?
Wow, that’s pretty cool!!!
Would still love to see longer periods on those graphs for example seasonal variations rather consecutive days.
I’m glad you think so. Working with a Raspberry Pi and the associated camera has been pretty eye opening in terms of what can be done. Especially with Python and JavaScript.
More interesting projects to come soon.
Agreed.
I might build a more permanent installation/experiment to capture data over such a period of time. Just need to work out a couple of details.
Hello everyone! I continue working towards further experiments with my Raspberry Pi computers and newly acquired environmental sensors. In the meantime, I have also been building a project related to open source intelligence (OSINT), using Python and JavaScript.
The tool (found on GitHub) is referred to as the “OSINT Searches Tracker”. Ultimately, it is an application for organizing and analyzing saved queries made to four different platforms; YouTube, Bluesky, Reddit and Google.
Other details aside, here is a screenshot of the self-quantification aspects of it:
On the left side of the image above, you see hyperlinks to (redacted) time-based searches for different social media and information platforms. Which is a strategy I use for staying up-to-date on the subjects I am interested in.
In the center of the screenshot is the “Stats” popup, containing two visualizations with three weeks worth of usage data. The line graph is a breakdown of the number and variety of activities I have been engaged in with this program. Whereas the bar graph displays the platforms my searches have been on, as well as their respective volume(s).
And at the upper right you see some of the other primary UI elements. Including the ability to search through my keywords, add new ones and perform more complex combination searches.
Yesterday I purchased the soldering equipment needed to connect header pins to the Adafruit temperature sensor used for my next self-quantification experiment. Which involves recording temperature measurements from inside my home every few seconds, over an extended period of time. Then visualizing said data to find patterns.
Instead of posting regular progress updates in this thread, I have opted to use Hackaday.io to document my (current and future) project(s). If you would like to follow along for a detailed overview of proceedings, here is a link to the Hackaday.io project page.
I will be sharing the end results from this experiment on the Quantified Self Forum as a response to this thread. But likely nothing until then, to keep posts to a minimum. Looking forward to sharing the final outcomes.
Good morning all! I wanted to provide a brief update on my current self-quantification experiment, using a Raspberry Pi Zero 2 WH computer and Adafruit MCP9808 temperature sensor.
For the past three weeks, I have collected ambient temperature readings from inside my home once every second. Resulting in 1.77 million measurements. To help me explore this data, I have developed a number of interesting graphs using the D3 JavaScript library.
For example, here is a screenshot of average temperatures on an hourly basis displayed as a line graph:
For another perspective, here is a screenshot featuring a heatmap, which visualizes temperatures for an average twenty four hour period; sourced from twenty one days worth of data:
In addition to these two graphs, I programmed heatmap visualizations for each day measurements have been collected. But those are, in my humble opinion, less interesting and relevant than the two charts shared above. At least in the context of this post. Except to say, each day has its own unique “temperature fingerprint”. Similar to the outdoor light measurement project, shared in an earlier update.
Ultimately, this is just the tip of the iceberg regarding what I would like to accomplish with building customized hardware and software for my self-quantification purposes. A larger goal is to complete and document ten of these DIY experiments, using various measurement tools and the Hackaday platform. As I assume I will learn important lessons about how to design, develop and deploy computer/application combinations of my own making.
This current project will be coming to a close within the next two weeks. Once it is finished, I will update this thread with my final observations, and information concerning the next such project.
I appreciate the support the Quantified Self Forum has shown me; being a major reason why I am pursuing this line of hobbyist tinkering in the first place. Cheers to citizen science.
