Tag Archives: videos
Ellis Bartholomeus has many of the standard self-tracking tools like pedometers, heart rate monitors, and eeg sensors. But she explored a different type of tool when a friend gave her a logbook with a place to record her daily mood by drawing a smiley or frowny face on a colored circle.
Although it initially felt like a silly exercise, she was surprised by how she responded to these faces over time. There was a visceral pleasure to seeing these faces. Even though they were representations of her own emotional state, they seemed to take on a life of their own.
Although Ellis had the day-to-day pleasure of rendering her mood as a cartoon, she couldn’t resist the urge to structure these images to see bigger trends. You’ll see her amusing methods in the video. How do you measure a smiley face? (hint below)
Peter Torelli had $2000 saved when he entered college. He knew that it wouldn’t last long, so he had to be careful about his spending. He switched to using a credit card in order to have a record of his purchases and reconciled his accounts every month. It became a habit that he kept for a long time. A really long time.
Peter now has 20 years of financial data, and the way he’s logged his data has followed larger technological trends. Starting with manually logging transactions in Quattro Pro and storing his data on floppy disks, his data now resides on Quicken’s servers. These changes have brought better security with better backups, but also uncertainty about the ownership of his data and lack of flexibility to move his information elsewhere because of proprietary data formats.
One of the surprising findings is how many memories flooded back when he reviewed past transactions. Both memories and transactions are tied to places. A simple line item can trigger a forgotten moment with an out-of-touch friend. When Peter’s spending trends are displayed on a multi-year timeline, it’s not just a representation of his finances, but the chapters of his life as well.
There are many more great insights from Peter’s talk at the Quantified Self San Francisco meetup in April:
Abe had an issue with staying up too late. The early morning hours often found him on his couch, working on his laptop.
The problem is that he simply lost track of time. To help make his bedtime unforgettable, Abe built a reminder he could not ignore. He wrote a simple app that uses colors to gently prod him to get ready for bed and installed it on an old android phone that he mounted on the wall in his living room. When the screen first lights up in the evening, the colors are blue (“bedtime is coming.”) and increasingly become red (“bedtime is here.”). When he long-presses the screen, it means that he is ready to sleep, and the phone responds by lighting up with a celebratory array of colors.
It was a simple intervention, but did it work? Abe thought so. But the skepticism of friends spurred him to dig into the data to make sure. The problem was that his simple app didn’t record any data. He had an idea, though. For the past year, a webcam connected to a Raspberry Pi had been recording his living room. Abe used the light levels of the video stream as a proxy for his bedtime. When the light levels dropped, it meant that he had gone to bed. This proved to be a reliable indicator because, as Abe says, “I’m always the last one to sleep, and the last light I turn off is always the living room light.”
Would this work for you? Possibly not, but that’s not the point. It is an excellent example of a person building a solution that is specifically designed for his personality, and also how meaning can be found in the unlikeliest of datasets. In the video, you will find out how much sleep Abe saved and learn more about how he set up his device and ran the analysis.
“The heartbeat is a treasure chest of information…”
Mark Leavitt has a unique perspective in that he is both an engineer and a physician. In his retirement, he is applying his wealth of knowledge to keeping himself healthy.
In this talk, Mark looks at how heart rate variability relates to his willpower. Does he lift more weight when his HRV is high? What happens to his eating habits when his HRV is low? And if the term “heart rate variability” is new to you, Mark gives a lucid explanation.
Also, you will get a glimpse of his amazing customized workstation with pedals to keep him active, a split keyboard on the armrests to keep his knees free and built-in copper strips for measuring HRV. Cue envy.
Historically, the most prevalent self-tracking tool in the home was the scale and the relationship between people and weight is complicated. Akhsar found healthy weight loss to be an emotionally difficult process. His breakthrough came with the Withings smart scale with which he lost 65 pounds in the first year and has kept it off for the last three. In this talk he discusses how the data helped him gain the self control to overcome temptations.
Weight has been a popular topic for Show&Tell talks:
Julie Price on the effect of running and family events.
Nan Shellabarger on seeing her life story in 26 years of weight data.
Kouris Kalligas on the relationship between his weight and sleep.
Jan Szelagiewicz on being motivated by family history.
Lisa Betts-LaCroix on using spreadsheets, forms and wireless scales changes the tracking experience.
Rob Portil on how he and his partner experience weight tracking differently.
Amelia Greenhall on using a 10-day moving average.
When someone comes into your life and takes up a special place in your heart, do they also occupy a similar place in your data? Shelly used GMvault to look through 5 years of Google Chat logs to “hunt for signals that I loved my husband and not somebody else.”
She looked at whom she messages, the time of a day, and the words she uses. She was able to extract meaning from innocuous metrics like “delay in response” to show whether her or her future husband were “playing games” at the beginning of the relationship. She also found that use of the word “love” did not correspond with the object of her affections (case in point: “This cytometer needs love too.”)
If you would like to do a similar analysis of your Google Chat log, contact Shelly to get the scripts she used.
“I started [tracking location] because I’m interested in all these invisible systems that we are immersed in.”
Stephen Cartwright has been tracking his latitude, longitude and elevation every hour since 1999. Even though the GPS in smartphones has made location tracking automatic, Stephen finds that he gets more reliable data from manually logging his location, of which he has almost 150,000 entries.
In this talk, Steven shows how seventeen years of location tracking has given him a wealth of data to explore in the form of three-dimensional data visualization sculptures. He has even brought some of these to QS conferences. They are amazing to behold in person.
While his visualizations show where he’s been, he says that it’s the negative space that can be more interesting, prompting the question, “Where do I need to go? What do I need to see?”
Other location tracking talks that we’ve featured include Jamie Aspinall‘s adventures in the UK, Robbie MacDonell on logging his transportation, and Alastair Tse on walking around Manhattan. We’ve also featured some great location-related visualizations from Bob Troia, Aaron Parecki, Eric Jain, and Tom McWright. If you have some location data from Moves, we’ve also written a guide on mapping it.
Paul LaFontaine is the organizer for the Denver QS meetup and has given many fabulous talks on heart rate variability. If you are not familiar with HRV, you can think of it as the measurement of your nervous system’s reaction to a perceived threat.
“Vapor lock” is Paul’s term for that feeling when you are trying to retrieve something from memory in a conversation, but because of the stress of the situation (especially if it is with a boss), you lock up as your recall fails. To better understand this phenomenon and learn how to prevent it, Paul measured his HRV during 154 conversations with bosses and co-workers.
Because “vapor lock” is not a standard measurement, Paul shows the criteria he used to identify these moments in his data. His analysis revealed a likely cause for what locks him up, but it was not what he expected and it changed his approach to meetings and conversations at work.
If you want to watch more talks about heart rate variability, Randy Sargent showed us what his HRV looks like through a spectogram. Matt Dobson talked about using it, along with other measurements, as a way to passively detect emotions. And I used a HRV device to track my stress at work.
“My luteal phase went from 10 days to 16, which is a frickin’ Quantified Self miracle.”
In this great talk, Ilyse Magy describes how tracking her menstrual cycle with the Fertility Awareness Method and Kindara worked for more than birth control. Tracking her cycle helped her understand how it affects her emotional state, and led her to find out that she had a previously unnoticed vitamin deficiency. ”Once I started charting, I was pretty amazed by what I was learning, but also kind of mad that no one had ever told me this stuff before.”
You can discuss this show&tell talk at the QS Forum.
Jon Cousins has given wonderful show&tell talks on mood tracking. Like most methods for measuring mood, his process involves a subjective assessment of his well being. But what if there was a physical measurement related to mood that doesn’t involve blood work?
Inspired by an anecdote about a man’s beard growth while working on a remote island, Jon explores whether there is a relationship between his mood and facial hair. Yes, you read that right.