Topic Archives: Videos
I think I spent more time flailing than planning in college. Though I’m not sure, because I don’t have the data. Tiffany Qi does, though. During her four years of undergrad, she meticulously tracked her time, putting it in one of several categories, “planning” being one of them. Now that she has her degree in Business Administration, she spent some time analyzing the wealth of data she collected as an undergrad. For this talk, given at the a Quantified Self meetup last month in Berkeley, California, she focused on the relationship (or lack thereof) between how she spent her time and her academic performance.
In particular, she explored the following questions:
- Did her commitment to her studies wane over time?
- How much did time spent studying matter for her final grade?
- Did the amount of time spent on fun help or hinder her grades?
- Did having a job or other job-like responsibilities lower her grades?
-CalenTools (Tiffany’s custom tool that she made for this project)
Get your tickets for QS17
Our next conference is June 17-18 in lovely Amsterdam. It’s a perfect event for seeing the latest self-experiments, debating the most interesting topics in personal data, and meeting the most fascinating people in the Quantified Self community. There are only a few early-bird discount tickets left. We can’t wait to see you there.
“I love reading random papers about the human body.”
Ahnjili Zhuparris came across a study on the menstrual cycle’s influence on cognition and emotion and was curious to see how hormonal changes may affect her day-to-day behavior. She figured her internet use may be a convenient and easy data set to assemble and examine for this effect. Using a few chrome plugins, Ahnjili was able to see not only where she spent her time online, but how she interacted with sites like Facebook and Youtube.
Her analysis yielded some interesting patterns. She found the most distinctive behaviors occurred during the fertile window, a span of about six days in the menstrual cycle when the body is most ready for conception. Looking at her shopping data from a clothing website:
”I found that there was no change in the amount of money I spent or the amount of time I shopped online… but while I was most fertile, I bought more red items. In fact, it was the only time I bought red items.”
In this talk, Ahnjili shows the differences in how she browsed Facebook, swiped in Tinder, and listened to music on YouTube.
Here are a few of the tools and papers that Ahnjili cites in her talk:
- Period Diary (iOS)
- timeStats (Chrome)
- Facebook Stats (defunct, it seems)
- Youtube Stats (Chrome)
- Menstrual cycle influence on cognitive function and emotion processing-from a reproductive perspective.
- Natural Born Cyborgs by Andy Clark
- A randomized trial of the effect of estrogen and testosterone on economic behavior.
- Romantic Red: Red Enhances Men’s Attraction to Women
- On the frequency of intercourse around ovulation: evidence for biological influences.
In this talk, Randy Sargent shows how he used a spectrogram, a tool mostly used for audio, to better understand his own biometric data. A spectrogram was preferable to a line graph for its ability to visualize a large number of data points. As Randy points out, an eeg sensor can produce 100 million data points per day. It is unusual for a person to wear an eeg sensor for that long, but Randy used the spectrogram on his heart rate variability data that was captured during a night of sleep. In the video, you’ll see an interesting pattern that he discovered that occurs during his REM sleep.
In this talk, Richard shares his attempt to improve his sleep quality by increasing the amount of bifidobacterium in his gut through eating potato starch. You’ll learn why he took the extreme step of flushing his digestive tract and rebuilding it from scratch.
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.