On October 23rd, the QS Stockholm meetup group meetup collaborated with the Bionyfiken, a Swedish biohacking meetup, to host a meeting at the Karolinska Institute. We’re happy to share a recap from Mina Makar and Dina Titkova, a member and co-organizers of QS Stockholm .
The meetup was conducted in a very relaxed atmosphere starting with a small introduction by the organizers followed by a short video of Gary Wolf introducing Quantified Self. We then had a few presentations from our group members.
Tina Zhu is a PhD student at KTH with focusing on Biofeedback. She talked about her interesting project of visualizing self-tracking data in the form of a fish in an aquarium. It was very interesting to see such data representation being taken to another level which could be easier for some to accept, understand, and interact with. Learn more about her work here: http://bodyandnature.
Glenn Bilby is an experienced sport medicine specialist then took the audience on a journey of all the gadgets that he has been using over the last 20 years to keep track of his different activities. During his presentation, Glenn discussed different topics related to how to use the data generated from the different devices and concluded that there is still much to be done when it comes to adding more meaning to the data.
Fredrik Bränström and Tom Everitt started a very interesting platform using a new algorithm to visualize data and highlight links between variables. This method is designed to give users a different perspective on how their daily activities are associated to each other. Their platform is available for testing here: www.kaus.se.
Sina Amoor Pour presented his reflections on what biofeedback and biohacking are and background on the Bionyfiken group.
Chai demonstrated how different chips could be implemented into the human body in order to perform different activities. He spoke about the wide range of applications for such chips ranging from starting your own car or bike.
If you’re based in Sweden, don’t miss the upcoming QS Stockholm meetups for more inspiration and ideas.
“There was nothing in my life pushing me to to have these more intimate relationships, the few people I actually care about.”
When Akshay Patil was putting together the guest list for his wedding he realized that it had been a long time since he’d spoken with some of the the people he was inviting. Even with his good friends, he surprised by his lack of communication, his inability to stay connected. As anyone faced with this realization he decided to try and change, but the realities of life quickly crept back and as they say, old habits die hard. When he left his last job and began looking for projects to work on, this troubling area of his life crept back to the fore. Maybe there was something he could do better track and change his communication and relationships. Using his development skills, and the ability to gather data from his Android phone, he decided to build a system that helped him stay in touch with the people that mattered most to him. In this talk, presented at the New York QS meetup group, Akshay talks about what’s he’s learned from using this app, including when it fails.
Siva Raj was interested in lowering his blood pressure. With a family history of cardiovascular disease and heart attacks he was worried about slightly elevated blood pressure (pre-hypertension). As someone engaged with understanding and building fitness applications he thought he would be able to lower his blood pressure by staying on track with a regular exercise program that focused on cycling. Interestingly his blood pressure measurement didn’t respond to his constant exercise or weight loss. After reading more research literature about the link between fitness and cardiovascular health Siva decided to change his training to improve his fitness. He decided to incorporate a increased intensity into his routine. After a short period of time he had increases in this fitness and was able to observe the reduction in blood pressure he was looking for. In the video below, filmed at the Boston QS meetup group, Siva explains his methods and talks about how he was able to track his body’s response to different fitness routines.
We’re back after missing last week (sorry!) with a bit longer list than usual. Enjoy!
Thoughts on Quantified Self for Modifying Long Term Life Goals by Mark Krynsky. Mark, a member of our QS Los Angeles meetup group, is consistently putting together interesting ideas in the QS space. In this short post he explore how QS tools might be used to understand long-term life goals.
Open Data for Open Lands by Alyssa Ravasio. The value of data isn’t confined to what we can understand about ourselves. There is so much beneficial information out there, especially when it comes to public data. In this post, Alyssa makes the case for protecting and promoting open data ideas and concepts regarding out most precious public spaces – the national parks system.
Art at the Edge of Tomorrow: Lillian Schwartz at Bell Labs by Jer Thorpe. A wonderful biographical piece about Lillian Schwartz, a pioneer in the field of computational art and exploration.
Terms of Service by Michael Kelller and Josh Neufeld. A reporter and nonfiction cartoonist team up to use a comic to tell us about the new world of data and privacy we currently inhabit. Interesting format and compelling content!
Narrative Camera by Morris Villarroel. Morris has been wearing a Narrative personal camera for six months. In this short post he explains what he’s learned and experienced over that time.
Where my 90 Hours of Mobile Screen Time in September Went by Bob Stanke. Bob used an app (Trackify) on his Android phone to track how much time he was spending on his phone and what apps he used the most.
Quitting Caffeine by Andrei-Adnan Ismail. Andrei wasn’t happy with his relationship with coffee and caffeine so he he decide to try and quit. Using tracking and really interesting use of “sprints” to gradually reduce his consumption, Andrei was able to quit. Great post here describing his process and the data he gathered along the way (including how his change affected his sleep).
Twitter Pop-up Analytics by Myles Harrison. Myles takes us through the process of downloading, visualizing, and analyzing personal data from Twitter.
Seven Months of Sleep by Eric Boam. A bit of an old one here, but beautiful and informative nonetheless. Make sure to read the accompanying piece by Eric. (I’m also looking forward to seeing more about this dataviz of his Reporter app data soon.)
My latest effort to visualize my calorie intake and weight loss by reddit user bozackDK. Using data collected from MyFitness pal, bozackDK has created this great visualization of his data. I asked what was learned from making this graph and received this wonderful response:
“I make graphs like these to keep myself going. I need some kind of proof that I’m doing alright, in order to keep myself wanting to go on – and a graph showing that I can (somewhat) stay within my set limits, and at the same time showing that it actually works on my weight, is just perfect.”
Jamie Williams found himself with almost two years of self-tracking data including physical activity, blood pressure, and weight. Because of his interest in data visualization and coding he decided to learn how to access it the data and work on visualizing and understanding some of the trends and patterns. In this talk, presented at the QS St. Louis meetup group, he takes a deep dive into his activity and step data as well as his blood pressure data to learn about himself and what affects his behavior and associated data.
What Did Jamie Do?
Out of pure interest in seeing what the data would reveal, Jamie utilized a combination of devices to track his physical activity, blood pressure, heart rate, weight, numbers of drinks, and automobile travel. He then went on to explore ways in which he could pull down, integrate, visualize, and ultimately make sense of what he collected.
How Did He Do It?
In order to obtain his data on a minute-level resolution, Jamie had to email FitBit for a specialized use of their API. He then employed Mathematica to develop a number of (beautiful) visualizations of his activity – along with other key moments in his life (moving to St. Louis, changing job location, preparing for a Half Marathon, etc.). Jamie was able to compare his data not only to his peers through FitBit, but also to others of his demographic in the U.S. using the publicily available NHANES data set.
What Did He Learn?
Through Jamie’s Quantified Self collection and analysis efforts, he learned a lot not only about the patterns and changes in his activity, but why they were the case. He also presented great feedback about one’s mindset when comparing to peers vs. the general population.
Withing Blood Pressure Cuff
Thank you to QS St. Louis organizer, William Dahl, and Jamie for the original posting of this talk!
Sue Lueder had a mystery stomach ailment that started after a vacation to Spain in 2011. When she returned from her trip she was beset by consistent and frequent burping attacks. After visiting her physician and receiving a diagnosis for heart burn, which she didn’t trust. she began to track her attacks and her diet. In this talk, presented at our 2013 Global Conference, Sue how she tracked he symptoms and used the data to make sense of this mystery food allergy.
What Did She Do?
Sue tracked her diet and the frequency and severity of her attacks.
How Did She Do It?
Sue was able to explore the data she was entering in to her self-designed spreadsheet tracking system. She used a few of the analytical tools and visualizations built into Excel to explore her data.
What Did She Learn?
Her analysis was able to pinpoint that dairy was probably the main culprit responsible for her attacks. Sue found out that she was able to improve her “good” days from 32% to 51% of the days she was tracking when she reduce dairy in her diet. When she experimented with adding dairy her findings were confirmed.
This week we have four different meetups in three countries! The great community in Toronto will be meeting for the 26th time to share personal stories of self-tracking. In Estonia they’ll be discussing the future of wearables and biome tracking. Over in Minneapolis members will be talking about making sense of data from “old school” scales.
To see when the next meetup in your area is, check the full list of the over 100 QS meetup groups in the right sidebar. Don’t see one near you? Why not start your own!
Tuesday (October 28)
Thursday (October 30)
Saturday (November 1)
In 2009 Tim Ngwena switched on Last.fm and he’s been running in across all his devices ever since. Earlier this year he decided to take a deep dive into his listening data to see what he could learn.
I realized that I was listening to the same old thing and I began to think about changing what I was listening to. But how can I change? Where can I start? I also wanted to learn something about my music, what I was listening to and who was behind the sounds. I decided to focus on music because it was doable.
In this talk, presented at the London QS meetup group, Tim explains how he was able to make sense of almost five years of data and learn more about himself and his listening habits.
What Did Tim Do?
Tim explored his music data along side additional information such as location data from Moves to learn about his musical tastes, listening habits, and explore new visualization and data analysis techniques.
How Did He Do It?
Tim exported his data, used the Last.fm API and some data cleaning and organizational tools to create a simplified and extensive database of his music listening history and associated data. He then visualized that data using Tableau.
What Did He Learn?
Tim learned a lot about himself and what the music he listens to says about him. He describes a few of the most interesting below,
Basically 80% of my listening comes form 10% of the artists that I have in my library.
I’ve listened to Erykah Badu for over a week (7.2 days). It led me to ask what is she saying to me?
Monday is my jam time. I’m listening from the morning into the evening.
I listen to music mostly when I’m walking.
Tim also learned a lot through the process of designing and creating his data visualization. The visualization, which you can explore here, made him think about being able to see the big picture when he has so much linked data.
I think context is important and you need to see all that information in one place and the tools I’m using allows me to do this.
Two weeks ago we announced the release of the QS Access App so you could access your HealthKit data in tabular format for personal exploration, visualization, and analysis. In that short period of time, we’ve seen a good number of downloads and positive feedback.
We know from our experiences hosting in-person and online communication about personal data that seeing real-world examples of what is possible is what inspires people to engage and ask questions of their own data. With that in mind we’re excited to announce our QS Access Visualization Showcase.
We are looking to you, our amazing community of trackers, designers, and visualizers, to show use what you can do with data gathered from using the QS Access App. Make heatmaps in D3, complete analyses and visualizations in Wizard, or just make meaningful charts in Excel. If you’re visualizing your QS Access data we want to see it.
We also know that data visualization design and creation is not trivial work. To support the community and help expose the visualization work we’ll be awarding free tickets to our QS15 Global Conference & Exposition to individuals who use QS Access to create unique and interesting visualizations. We’ve earmarked two tickets (a $700 value) for outstanding work. If you’re selected, we’ll also work with you to showcase your work at the QS15 Conference and Exposition so other community members and attendees can explore and learn from their own data.
If you’re in the Bay Area come to our QS Meetup on November 11th at the Berkeley Skydeck. You can showcase your visualization and tell our community what you’ve learned from accessing and visualizing your data.
HealthKit is still new and the number of apps that integrate with it is growing by the day. At QS Labs we’ve done a bit of work making simple visualizations that are meaningful to us.
Steps and Sedentary Activity
Gary has an iPhone 5s which has native step tracking. We used the QS Access app to export his hourly step totals and made these simple line graphs in Excel. You can read more about what he learned from these simple data visualizations here.
How Much Do I Run?
Ernesto is an avid runner and enjoys running along the quiet trails in Los Angeles. He was interested to see how often he actually runs and if there’s any pattern to his running. Using a well-designed D3 template he was able to make a calendar heatmatp of his running distance.
If you don’t have any HealthKit data to work with, or just want to play with some example data we’ve created a few files that you can use as examples. Download the files below from our GitHub account and make sure to read the documentation to understand where the data is coming from. Descriptions of the data files and sources are available in our QS Access Data Examples repo on Github.
Shawn Dimantha is always looking for easier ways to track his health. He uses a variety of self-tracking tools, but a few months ago he became interested in exploring what he could do given his engineering and health IT background. He was inspired by immersion, an MIT-developed email analysis tool, which helped him understand who he was communicating with, and by Wolfram Alpha’s Facebook analysis tool. Focusing on Facebook and the wealth of image-based data in his profile he asked himself if images could be a window into his health. After reading a research paper on the use of images to predict body mass index he decided to see what he could learn my implementing a similar procedure on his own images.
What Did Shawn Do?
I used photos from my Facebook account to track my health, the reason I did this because I wanted to see how a simple heuristic I used for tracking my health daily could be implemented in the online world given the huge amount of photos that are and have been shared on a daily basis. I notice when I gain or lose weight, am stressed or relaxed from my seeing my face in my mirror. I was partly inspired by the self-photo collages presented on YouTube.
How Did He Do it?
I selected photos of my face from my Facebook account, cropped out my face and used some software and manual tagging to measure the ratio of different fiducial points on my face (eye-eye length, and cheek to cheek length) over time to help serve as a proxy for my health.
What Did He Learn
Facial image data needs to be cleaned and carefully selected. Face shapes are unique and need to be treated as such. Data that is not present is often more telling than what is present. Life events effect my weight and should be put into context; however causation is harder to determine than correlation. By being more conscious of my score and I can change my behavior before things get off track.
Right now I’m turning this into a product at Enfluence.io where I’m focused on using it to help with preventive health.
Facebook (my own images)
Python / OpenCV
Slides from Shawn’s talk are available here.