Search Results for: sleep
Tobias Zimmer tracked what he ate and, in particular, what he didn’t eat. The image above comes from a series of ceramic plates that were created using generated graphics based on the crumbs he left. For more, see his Tumblr: Food-Data:
»Food Data« elevates an everyday occurrence to the realms of art. Minimalistic crumb compositions that emerge while eating every day, are enhanced by generated graphics, which refer to the topic of computerized data tracking of human behavior. The final plates encourage to contemplate on everyday life and to find beauty in daily routines, but at the same time remind of technological advancement and practices of (self-)surveillance, that doesn‘t even stop before the private ritual of eating.
Haunted By Data by Maciej Cegłowski. A keen sense of how things can go wrong is needed if we are to have any hope of – well, if we are to have any hope! This essay by Maciej Ceglowski about the highly toxic nature of large scale data aggregation is highly recommended.
How Your Device Knows Your Life through Images by Graham Templeton. This research demonstrating that an artificial neural network was able to train itself to correctly identify 83% of the time the activity that a person was engaged, just based on the images collected from that person’s lifelogging camera is especially interesting in light of Ceglowski’s talk.
Life Stress by Marco Altini. Marco reviews an exhilarating but stressful 15 months of his life through the lens of heart rate variability.
Body Metrics Under Stress by Justin Lawler. Another stress-related piece. Justin shows through data how his body responded to the stress of giving a talk about his lifelogging experiences at QSEU15.
Pathways Project by Mimi Onuoha. This project looked at what story could be told from a month’s worth of mobile phone data from four groups of people, each with a different type of relationship: co-workers, a couple, a family, and roommates. The charts are interactive and fascinating. As Onuoha writes:
…data visualizations add a level of abstraction over real world events; they gather the messiness of human life and render it in objective simplicity. In life, goodbyes can be heartbreaking affairs, painful for all involved. But on a map, a goodbye is as simple as one dot moving out of view.
The project’s data is available in this Github repository.
My Hamster’s Activity Index by /u/snootsboots
This reddit user used a motion sensor connected to a raspberry pi to make sure that his hamster is ok when he’s away. Here’s a picture of the hamster, if you’re curious. His name is Timmy
My internet’s median ping over time by /u/asecretsin. This a very simple chart, and a simple idea. What I like about it though is that it illustrates how just a little bit of logging and data visualization can reveal a pattern in one’s environment. It clearly shows that the response times slow down from 6pm to 10pm. I have a home office and it often felt like the internet slowed down around the time people starting getting off work.
From the Forum
Activity trackers without online requirement
My review of the H2O-Pal – A Hydration Tracker
Consumer genome raw data comparison – Which has the most health information?
Benefits of 24/7 heart rate monitoring
Can You Quantify Inner Peace?
How to find all major volunteer bioscience projects I can partake in?
As someone who still is not satisfied with any sleep tracking device or app that I have tried, I related to this dialogue from a tumblr called Zen.Sen.Life:
- Sleep Tracking App: I see you’re not violently throwing yourself around your bed, you must be in a deep sleep. Sweet dreams, buddy!
- Me: I’m actually still awake.
- Sleep Tracking App: But you’re lying still…
- Me: Because I’m trying to get to sleep.
- Sleep Tracking App: You mean you ARE asleep.
- Me: I really don’t.
- Sleep Tracking App: You’re going to have to trust me, I do this professionally and I know sleep when I see it, and I’m pretty sure you’re asleep right now.
- Me: I couldn’t be more awake.
- Sleep Tracking App: This is all a dream…
A man who tracked five years of sneezes might have a fix for your pollen allergy by Akshat Rathi. Thomas Blomseth Christiansen has spoken about tracking his sneezes at QS conferences. This article is a good telling of Thomas’s story.
Good tool with too small market can get a second chance – a hardware hack saves Zeo by Portabla Media. A short article on how Philipp Kalwies responded to the demise of Zeo. Since the sensors in the headband need to be replaced every three months and official supplies were dwindling on the secondary market, Philipp began to make his own and hopes to have this resource available to the small group of users who continue to get value from their Zeo devices.
The Right to Repair Ourselves by Kim Bellard. A common question in the QS community is “who owns your data?” Another question that should be given more time and is explored here, is “who owns the knowledge of how to ‘fix’ yourself?”
The Habits of Tracking My Diet and Exercise Data by Shannon Connors. Shannon has some of the most impressive personal data sets that I have ever seen. In this post, she gives an overview of the tools that she uses, what about the data she finds useful, and how she integrates the data collection into her day.
What you can learn from 2 years of Coach.me habit tracking + Machine Learning by Bryan Dickens. Applying association analysis to his coach.me data, Bryan was able to see which of his habits tended to occur together. There are some intriguing insights in here.
Visualizing Data in My Sleep with Tableau by Robert Rouse. Robert shows how his sleep patterns changed after the birth of his child.
A year ago we released QS Access, a simple app that allows you to see your healthkit data in a table. Our idea was to make it easier for people to explore their data using familiar tools, such as Numbers, Excel, or any spreadsheet program that can open a .csv file. We’ve really enjoyed hearing its been useful, and we’ve received lots of good feedback. This week we released a new version of the QS Access App that contains some commonly requested features. You can now:
- See raw data from individual elements, such as running.
- Store the query details, so you don’t start from scratch each time.
- Choose units for many quantities.
- Get a table of your sleep data.
We’re still listening, so if you are using QS Access and have feedback for us please let us know by emailing email@example.com.
Eleven days and counting!
On September 18th and 19th the Quantified Europe conference returns to the beautiful and affordable Casa 400 hotel in Amsterdam. If you’ve been before you know how special this conference is. The dozens of high-handled guest bikes waiting just outside the hotel door suggest it’s going to be hard to stay inside, but we have a lot of experience programming both “with” and “against” the lure of the city and we expect that nobody will be riding away until the last session ends. With over 70 different talks and sessions scheduled between social breaks with excellent food, our “carefully curated unconference” is the fruition of nearly a year’s work getting to know what’s going on the QS community. We’ve been deeply inspired by what you’re thinking about. It’s time for everybody to get in on what we’ve been learning.
Especially notable themes this year include novel ways of measuring sleep; widening interest in blood glucose sensors; popularization of genome and microbiome tests, and, as always, an amazing range of handcrafted and deeply personal tracking stories about health, sports, emotion, and more.
You can read a preliminary program here. [PDF]
As you’ll see, we’re opening the conference with 10 special “how to” sessions covering topics from heart rate variability to accelerated learning. Our goal with these sessions is to give everybody a chance to learn practical tips from experienced trackers. The heart of the program will be our Quantified Self Show&Tell talks, first person stories on topics like home EEG measurements to improve reading skill, self-collected data on distracted driving, and measuring the effect of music on concentration.
Lively informal breakouts will help set the agenda for the Quantified Self movement in the coming year, and we’ll be joined by dozens of Quantified Self toolmakers bringing their ideas and demos, with special thanks owed to the generous sponsors and Friends of QS who make this meeting possible, including Bayer, Abbott Labs, Intel, Scanadu, Oura, Emfit, and Beddit.
Tickets are almost sold out so register today.
Enjoy this week’s list!
Cell Phones Help Track Flu on Campus by Karl Bates. In 2013, Duke University students participated in a unique research trial to track the spread of influenza. Using sensors from their mobile phones and a few medical tests, researchers were able to see how personal habits and their social networks affected who got the flu.
How San Diego is Using Big Data to Improve Public Health by Mallory Pickett. A nice article here on some new research efforts being led by our friends at the University of California, San Diego.
“You Get Reminded You’re a Sick Person”: Personal Data Tracking and Patients With Multiple Chronic Conditions by Jessica S Ancker and colleagues. A very interesting research study examining the role of self-tracking and health technology in the lives of individuals with chronic conditions.
Next Steps in Developing the Precision Medicine Initiative by DJ Patil & Stephanie Devaney. After a few months of meetings and feedback, the folks helping steer the Precision Medicine Initiative are looking for new ideas and leading examples.
My 40-Day Journey into Meditation with Muse (the brain-sensing headband) by Kal Mokhtarzada. An interesting post examining meditation and the data provided by the Muse. Kal dives deep into his data, and gives a few examples of why things tended to work, and when they didn’t.
What reporter Will Ockenden’s metadata reveals about his life by Will Ockenden and Tim Leslie. A fascinating look into what you can learn from someone just from the metadata their phone collects.
8 Years of Dating Data by Robin Weis. Robin details her dating history, starting when she was 15, in this wonderful visualization.
See it, believe it: The Web Visualization Library by Jasper Speicher. Our friends over at Open mHealth are building a great set of open source tools to work with personal health data. In this post, they describe why they built their visualization library.
From the Forum
Maggie Delano hit her head while helping a friend move. She was diagnosed with a concussion and, later, post-concussion syndrome. In order for her to heal, she had to give her brain a break from cognitively stimulating activities. In this show&tell talk, presented at the 2015 Quantified Self Conference, Maggie discusses how she tracked her progress toward recovery with Habit RPG (recently renamed Habitica) and improved her sleep with Sleepio.
To see great presentations like Maggie’s in person and get the chance to talk with the speakers, come to our Quantified Self Europe Conference on September 18 & 19. Our early-bird tickets (€149) expire in less than 24 hours, so get yours now!
Have you registered for our 2015 Quantified Self Europe Conference? If not, this weekend is your last chance to take advantage of our special early bird rate (€149!). We’d love to see you there so register today!
Our friends at Oura are currently crowdfunding their amazing heart rate, sleep, and activity tracking ring. Check out their Kickstarter to learn more.
Now, on with the show!
You may just have updated the map with your RunKeeper route by Alex Barth. Short post here describing a fascinating use of publicly available data from Runkeeper users around the world.
A Six Month Update on How We’ve Been Using Data, and How it Benefits All Americans by DJ Patil. A nice update on some of the current initiatives being championed at the federal level to make data more available and beneficial for all Americans. I can’t wait to see what happens next.
Discovering Google Maps New Location History Features by Mark Krynsky. Mark walks us through the new features embedded within Google Maps and Location tracking. Want to find out where you spend most of your time or how often you visit your favorite coffee shop? Google may already know!
Drowning in Data, Cities Need Help by William Fulton.
No city government, university or consulting firm can possibly figure out how best to use all the data we now have. The future lies in having everybody who understands how to manipulate data — from sophisticated engineering professors to smart kids in poor neighborhoods — mess around with it in order to come up with useful solutions.
Just Talking with Maggie Delano by Christopher Snider. Take a listen to a great conversation with our friend and QS Boston and QSXX organizer, Maggie Delano. Well worth your time.
HRV Measurements: Paced Breathing by Marco Altini. Marco is back at it again with a in-depth post about his experiments on how breathing rate affects HRV and heart rate measurements. Starting with a great review of the current literature, he then dives in to his own data and what he’s found through various experimental protocols.
Resuming Quantified Self Practices by Emily Chambliss. A short post here on using Excel to track and understand food consumption. Make sure to check out the slides from a talk she gave in 2012 at a New York QS Meetup.
My Sleep Quality of the last 2 Years by Reddit user Splitlimes. A beautiful visualization of just over two years of sleep data tracked with the Sleep Cycle app.
Time-histogram of 10 Million Key Strokes by Reddit user osmotischen.
These are plots of 10 million key strokes and about 2.4 million mouse clicks logged over a bit more than a year’s time on my computer. (Make sure to click through for more visualizations.)
From the Forum
Descriptives and visualizations for large numbers of variables
I created this site to make decisions better with an algorithm. I’d love feedback!
HRV apps for Polar H7 that include SDNN
I first got a look at the Oura ring at the Quantified Self Public Health Symposium last May. I was surprised that the Oura engineers had managed to get sleep and activity tracking into a bit of jewelry the size of a ring, and ever since I’ve been deeply curious to experiment for myself. Although a few samples showed up at QS15, there was nothing we can could take home with us. But the Oura ring campaign on Kickstarter launches today, with delivery estimated for November 2015. The company is a QS sponsor, and they’re offering readers here and our followers on Twitter a few hours head start on the campaign’s very limited number of $199 rings. (They have just 500 0f these, after which the minimum pledge to get a ring rises to $229).
The Oura ring has both optical sensors and an accelerometer, an increasingly common duo, used in the Apple watch and quite a few other devices. But I thought that the combination of sensors and battery demands would make a ring-size sleep and activity sensor challenging.
Of particular interest to me is the offer of “laboratory accurate” measurement of heart rate variability, or HRV, using the optical pulse sensor. Heart rate variability is the the variation in the time between heart rates, and it’s useful for Quantified Self experiments involving measurement of emotional arousal and stress. HRV is relatively easy to get, if you have an accurate heart rate monitor, but typically these have taken the form of elastic chest straps. Even Apple, with its relatively capacious watch, doesn’t yet promise accurate measurement of HRV. If the Oura ring ends up offering accurate HRV in a ring that is easy to keep on at all times, it will spark a lot of very interesting new projects.
Thank you to Petteri Lahtela and Hannu Kinnunen, the Oura founders, for giving us a few hours head start. We wish you good luck on your campaign!
For early access use this link: Quantified Self Access to Oura Kickstarter.
Note: both Petteri and Hannu will be at Quantified Self Europe conference in Amsterdam on September 18 & 19.
Enjoy this week’s list!
When ‘Special Measures’ Become Ordinary by David Beer. What does it mean to have measurement, personal and institutional, as part of our everyday experience? A nice article that begins to expose what it means to operate in this new world.
Building smarter wearables for healthcare, Part 1: Examining how healthcare can benefit from wearables and cognitive computing by Robi Sen. In this article, Robi Sen describes what IBM sees as the “analytics gap” in current wearable technology. Specifically, that devices and the data they present don’t fully understand and utilize contextual information, and therefore are not providing meaningful information. What’s the answer? IBM’s Watson, of course.
Doping scandals, open data, and the emergence of the quantified athlete by Glyn Moody. A short and interesting piece that wonders if opening up athlete performance data might be a useful part of combating doping and illegal performance enhancement in professional athletics.
N of 1 Trials and Personal Health Data with Dr. Nicholas Schork. The Health Data Exploration Network hosted their inaugural webinar this past Friday. The focus was on N of 1 trials: why they’re important, how to conduct them, and the role of Quantified Self and self-tracking data.
Lifelog: Pilot Tasks of NTCIR–12. Our good friend and lifelogging researcher, Cathal Gurrin, is spearheading an innovative project to improve search and information access to lifelogging and self-tracking data. If you’re a researcher or information systems specialist you may want to take a look at data and see if you can help push the field forward!
Sleepwalking. Rather than point to one post over another we’re going to highlight this entire blog by one anonymous scientist who’s exploring his sleepwalking. The whole blog is chock full of insights into measurements, devices, and experiments to see what may or may not affect their sleepwalking. Start here to get a good overview.
How I Hacked Amazon’s $5 WiFi Button to track Baby Data by Ted Benson. Have $5 to spend on an Amazon Dash button? With a little bit of programming you can turn it into your own DIY internet-connected tracker!
10,000 Steps at a Music Festival by Tim Hanrahan. A fun post about tracking physical activity at the Lollapalooza festival.
Basis Data Analysis by Victor Jolissaint. I saw this Victor tweet this visualization and was immediately drawn in. Turns out he’s been exploring ways to analyze and understand his Basis watch data using R. Check out the link for his code and take a crack at analyzing your own data!
Tableau: Helping Me See and Understand Myselfb y Craig Bloodworth. Craig pulled all his self-tracking data into Tableau and designed his own personal dashboard to better understand what was going on with his activity, personal finances, and other lifestyle information.
We’re excited to share another round of personal data visualizations from our QS community. Below you’ll find another five visualizations of different types of personal data. Make sure to check out Part 1, Part 2, and Part 3 as well!
Name: Damien Catani
Description: This is an overview of how I have been doing today against my daily habit targets. Yes, I had a good sleep!
Tools: I used a website I’ve been building for the purpose of setting and tracking all goals in life: goalmap.com
Name: Bethany Soule
Description: This is my pomodoro graph. I average four 45 minute pomodoros per day on my work, and I track them here. This is where most of my productivity occurs! There’s some give and take.
Tools: The graph is generated by Beeminder. I use a script I wrote to time my pomodoros and submit them to Beeminder when I complete them. The script also announces them in our developer chat room, so there’s also some public accountability there as well.
Name: Steven Zhang
Description: This plot shows the time I first go to sleep, against quality of day (a subjective metric I plot at the end of every day). What this tells me is that if I get a full night’s sleep of 8 hours, for every hour I got to bed, I can expect a .16 decrease in my QoD rating, which, given my range of QoD around 2 to 4, is about a 5% decrease in quality of day.
Tools: Sleep as Android to track sleep and some python scripts for ETL.
- Normal sleep
- 3. Trying to achieve normal sleep, but failing to
Tools: Tableau for visualization. Sleep as Android for logging sleep.
Name: Eric Jain
Description: Benford’s Law states that the most significant digits of numbers tend to follow a specific distribution, with “1″ being the most common digit, followed by “2″ etc. But my daily step counts show a slightly different distribution: The fall-off from “1″ to “2″ is larger than expected, and the frequency of digits larger than “5″ increases rather than decreases. Is this pattern typical for step counts? Could suspicious distributions be used to detect cheaters?
Tools: Fitbit, Zenobase, Tableau
Stay tuned here for more QS Gallery visualizations in the coming weeks. If you’ve learned something that you are willing to share from seeing your own data in a chart or a graph, please send it along. We’d love to see more!