Ernesto Ramirez

Ernesto Ramirez
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What We Are Reading

Enjoy this week’s list!

Articles

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.

Show&Tell

0*7MPPXFrgXIfXEi06 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.

 

6713040-3x2-940x627 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.

 

Visualizations

RW_Dating 8 Years of Dating Data by Robin Weis. Robin details her dating history, starting when she was 15, in this wonderful visualization.

 

image02 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

Cholesterol Monitoring
Sleep Tracker and Sleepwalking

 

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What We Are Reading

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!

Articles
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.

Show&Tell

1112195 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.

 

tumblr_inline_n4fxiifr6T1r6gaqp 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.

Visualizations

9aXr5Mm 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.

 

3 - nqfnVD8 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

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QSEU15 Preview: Ellis Bartholomeus on Doodling Mood

EllisMoodFaces

In just four short weeks we’ll be kicking off the 2015 Quantified Self Europe Conference, and we are so excited to hear from old friends, learn from new members, and interact with some wonderful toolmakers. It’s going to be a great time.

As you may know, we build our conference programs from the ground up with attendees submitting their projects and ideas when they register. It’s always fun to read about someone’s new self-tracking project or experiment, especially when it involves something we haven’t seen before. Today we’re going to begin our conference previews with one of those novel and interesting talks.

EllisB

 

Ellis Bartholomeus is no stranger to our QS Conferences, having given an excellent talk on using photos for food tracking at our 2013 Europe Conference. At this year’s conference Ellis will be sharing her experience with a very interesting type of mood tracking. For six months Ellis tracked her mood by drawing a face every day. This simple act of using a quick doodle to track how she was feeling led to some unexpected benefits:

 

This inspired and engaged me more than expected with other quantifications. The faces triggered my curiosity and provided many insights, which continue to motivate me.

Mood tracking is something that continues to intrigue our community. Understanding our happiness, what affects our mental state, and how to improve our moods is a common theme at meetups around the world. We’re interested to learn more from Ellis and her experiences at the 2015 QS Europe Conference. If you’re tracking your mood we invite you to join us in Amsterdam on September 18th & 19th for two full days talks, breakout discussions, and working sessions! Early bird tickets are still on sale. Register today for only €149!

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What We Are Reading

Enjoy this week’s list!

Articles
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!

Show&Tell
sleepwalking_beddit 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!

FullSizeRender-1 10,000 Steps at a Music Festival by Tim Hanrahan. A fun post about tracking physical activity at the Lollapalooza festival.

Visualizations

vjo_2015-Aug-13 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!

My-Steps_thumb 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.

From the Forum
How to acquire info about sent e-mails using gmail?
How to quantify myself
We want to track you!

 

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2015 QS Visualization Gallery: Round 4

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 1Part 2, and Part 3 as well!

daily habits 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

 

tock_b_tock_goal_page 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.

 

 

qs1 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.

 

qs2Name:Steven Zhang
Description: Log of all my sleep for the last 6 months, labeled by the types of sleep I most often encounter

  1.  Normal sleep
  2. Napping
  3. 3. Trying to achieve normal sleep, but failing to

Tools: Tableau for visualization. Sleep as Android for logging sleep.

 

Digits
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!

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QS15: A Review

QS15 Tweet Robot designed by The Living. Photo: Rajiv Mehta

QS15 Tweet Robot designed by The Living. Photo: Rajiv Mehta

In just little more than a month we’ll be convening in lovely Amsterdam for our 2015 Quantified Self Europe Conference. While some might call us crazy since we just wrapped on our big QS15 Conference in San Francisco, we like to think that we’re on a tour, inviting people from around to world to engage and learn about the power of personal data.

With QSEU15 so close, we decided to take a quick look back at what makes our conferences so special. Rather than telling you what we think we thought it would be best to highlight the thoughts and writing from individuals who attended and participated in our 2015 Quantified Self Conference. We’ve gathered up links to articles, blog posts, and write-ups of all types and are posting them here for you to read and review.

If you’re intrigued by the ideas and events described in the links below make sure to register for QSEU15. Early Bird tickets are on sale for just a bit longer so take advantage now!

Training the Next Generation of ‘Quantified Nurses’

Quantified Self ’15 (Day 1 Recap)

Quantified Self ’15 (Day 2 Recap)

What’s a Self Anyway?

Compass Alpha at the Quantified Self Conference 2015!

Quantified Self Expo, Part I

Quantified Self Expo, Part II 

About the Quantified Self Conference and Expo

Own your Biological Machine

Quantified Self 2015

Architecting health data for the cloud

What you can learn from the 2015 Quantified-Self conference

QS15: Measurement with Meaning

More About Me at QS15

What I learned at Quantified Self 2015

Notes from the 2015 Quantified Self Conference

My Data, Your Data, Our Data

News from the Quantified Self movement

Quantified Self Labs

Some of the Best from the 2015 Quantified Self Conference

Personal Gold @ Quantified Self ’15

Learning about new self-tracking technology at QS15

If you wrote something about your experience at QS15 let us know! We’d love to feature it.

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What We Are Reading

We’re back again with another round of What We’re Reading. Before diving into the great articles and links below why not take some time to subscribe to our QS Radio podcast! We just released our fourth episode and would love to know what you think!

Want to tell us in person? Why not join us for our fourth QS Europe Conference this September in Amsterdam! Register now to take advantage of our early bird pricing.

Articles
Got Sleep Problems? Try Tracking Your Rest with Radar. by Rachel Metz. Researchers at Cornell University, the University of Washington and Michigan state are conducting research using off the shelf components to see if non-contact sleep tracking is possible. Turns out it is!

Apple’s Fitness Guru Opens Up About the Watch by Scott Rosenfield. A nice interview with Jay Blahnik here, where he speaks to Apple’s focus on self-tracking and fitness with the Apple Watch.

To share is human by Laura DeFrancesco. In this great news feature, Laura DeFrancesco exposes some of the issues with sharing personal data, as well as the initiatives hoping to break through those issues to help bring more data into the public sphere.

Using Twitter data to study the world’s health by Elaine Reddy. A great post here profiling John Brownstein and his work in Computational Epidemiology, specifically how he and his research team use public data sources like Twitter to tease out signals for health research.

Show&Tell

Charts.001 Comparing Step Counts: Apple Watch, Fitbit Charge HR, And IOS Withings App by Victor Lee. An awesome and in-depth post comparing almost two months of steps counts from three different tracking methods by our friend Victor Lee. Glad to see he put our QS Access app to good use!

Follow-up to how I lost over 40 pounds using HealthKit and Apple Watch by Jim Dalrymple. Jim tells his story of how using a variety of apps and tools, all linked to his Apple Healthkit app, helped him learn about himself and eventually put him on the path to sustained weight loss.

Tracking Confidence by Buster Benson. Buster always has something interesting to say about self-tracking. This time is no different. Here he briefly talks about asking himself, “how confident do I feel right now?”

Visualizations


The Heart Chamber Orchestra.

The Heart Chamber Orchestra – HCO – is an audiovisual performance. The orchestra consists of 12 classical musicians and the artist duo TERMINALBEACH. Using their heartbeats, the musicians control a computer composition and visualization environment. The musical score is generated in real time by the heartbeats of the musicians. They read and play this score from a computer screen placed in front of them.

From the Forum
How to acquire info about sent e-mails using gmail?
Looking for Android exercise tracking app
Resting Heart Rate tracking

This Week on QuantifiedSelf.com
QS Radio: Episode #4

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QS Radio: Episode #4

QSradio_iTunes

We’re back with another episode of QS Radio! In our fourth episode we learn a bit about Steven Jonas, and the many different trackers he’s using and then it’s on to a great show&tell interview g with Mark Leavitt, co-organizer of the Portland QS meetup group about his experience with measuring HRV and it’s effect on his willpower. We wrap up as usual with a short discussion on things we’re finding interesting out there in the QS world. Click the links below to listen and/or subscribe and follow along with our show notes.

Download the episode here or subscribe on iTunes.

Links:

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What We Are Reading

A long list for this week’s What We’re Reading. I actually had to stop myself from adding in even more visualizations and show&tell examples! We’re always on the lookout for more though, so make sure tweet us your favorite links!

Articles
Fitted by Moira Weigel. A very thoughtful essay on gender, identity, and confession – all while using the Fitbit as the narrative backdrop.

What kind of love does the FitBit prepare us to feel? Is it self-love? Or is even the self of the exorexic a kind of body armor?

How to Build a Smart Home Sensor by Dave Prochnow. If you have 2 hours, $95, and know how to solder, then you too can build this DIY sensor to measure the temperature, humidity, light, and noise for any room in your home. If someone builds and tests this please let me know (Would love to see air quality sensors included too!)

It’s Hard to Count Calories, Even for Researchers by Margot Sanger-Katz. New research shows Americans are eating less, but can we really trust the data? Margot does an excellent job here of rounding up the various ways we measure food consumption in the United States while coming to a commonly heard conclusion – food tracking is just plain hard.

Hadley Wickham, the Man Who Revolutionized R by Dan Kopf. If you’re knee deep in data analysis, or just like poking around in stats software, you’ve probably heard of and used R. And if you’ve used R, then there is a good chance you’ve used many of the packages written by Hadley Wickham. Great read, if for nothing else you learn what the “gg” in ggplot2 stands for.

Heart patient: Apple Watch got me in and out of hospital fast by Neil Versel. When Ken Robson wasn’t feeling well he turned to his Apple Watch. After noticing lower than normal heart rate readings his checked himself into the emergency room and soon found out his hunch was right, he had sick sinus syndrome.

New Australian experiment rewards joggers with 3D printed chocolate treats based on exercise data by Simon Cosimo. Sign me up!

Show&Tell

Screen_Shot_2015-07-24_at_11.41.15_AMHow Does Giving Blood Affect Your Iron Levels? by Ryan W. Cohen. Simple and to the point blog post by Ryan explaining how he discovered elevated iron levels in his blood, and the simple test he tried to find out why.

The Quantified Athlete by Matt Paré. Matt is a minor league catcher in the San Francisco Giants organization. In this post, the second in a series (read Part 1 here), Matt discusses how he became interested in tracking his biomarkers, and what he’s experimenting with.

What I Learned When I Stopped Wearing a Fitbit After Seven Years by Michael Wood. Michael writes up a brief post on how he felt when he was separated from his Fitbit activity tracker.

1*n0JEGs6Mzgiri0kAUYPDkgHow I tracked my house movements using iBeacons by Joe Johnston. Joe uses a few iBeacons to find out where he spend time in his house. Fascinating idea, makes me want to play with this technology as well!

Visualizations

Screen Shot 2015-06-30 at 8.55.26 amVisualizing a Simpler RunKeeper Training Plan by Andy Kriebel. Andy presented his running data, and how he uses a few tools to keep track and visualize his data as he trains for a marathon. Follow the link and you can see his Tableu workbook, which includes a screencast of his presentation, and links to his workflow.

gIV8BlGI decided to take a peek at my Netflix viewing data by Reddit user AmericanPicker69. This enterprising individual decided to take a peak into his user account to understand his Netflix viewing habits. Turns our a simple copy/past is all you need to do to get the raw data. Who knew?!

eUKFHBOMy weight loss journey by Reddit user IMovedYourCheese. Loved this graph and the implementation of BMI categories, a moving average, and lower/upper bounds for weight loss. He even provided the excel template if you’d like to use it with your own weight tracking.

From the Forum
Teach Arduino from Beginner to Making a Quantified Monitor
Google Fit

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2015 QS Visualization Gallery: Part 3

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 and Part 2 as well!

eddie-flights Name: Edward Dench
Description: All recorded flights I’ve taken.
Tools: Manual entry into openflights.org (there is an interface using TripIt though).

 

QS Visualization Name: Siva Raj
Description: After 6 months of regular exercise failed to improve my fitness and blood pressure levels, I switched to training above my endurance limit (anaerobic threshold). This was higher intensity but half the cycling time, yet my fitness and blood pressure improved within weeks.
Tools:Revvo – tracking fitness and intensity of workout; Withings – weight; iHealth BP Monitor – BP. Visualization created by overlaying Revvo screenshot with other information in photoshop.

 

Screenshot 2015-06-05 08.07.14 Name: Kurt Spindler
Description: Grafana is a common tool in the Software community to create beautiful dashboards to visualize server health (network, requests, workers, cpu, etc.) and therefore more easily diagnose problems. I created a custom iOS app that allows me to publish metrics to the same backend as Grafana, giving me Grafana dashboards for my personal health.
Tools:Custom iOS app, Grafana, Graphite
RyanODonnell_PagesReadPerMonthName: Ryan O’Donnell
Description: This semi-logarithmic graph is called the Standard Celeration Chart (SCC). It’s beauty is that anything a human does can be placed on this chart (i.e., standardized display). This also allows for cool metrics to be developed that lend well to predictability. I charted the number of pages that I read for my field of study, Behavior Analysis. I wrote a blog post on the display to speak some to the reading requirements suggested by professionals in the field. There were many variables that led to variations in reading rate, but the point of this work was to try and establish a steady reading repertoire. A recent probe in May of 2015 was at 2800 pages read. Essentially, I learned how to incorporate reading behavior analytic material almost daily in my life, which indirectly aids in the effectiveness I have as a practitioner and supervisor.
Tools: Standard Celeration Chart and paper-based data collection system (pages read each day on a sheet of paper).

 

Graph4_red_black Name: Francois-Joseph Lapointe
Description: This *Microbial Selfie* depicts the gene similarity network among various families of bacteria sampled from my gut microbiome (red) and oral microbiome (black). Two bacteria are connected in the network when their gene sequences are more similar than a fixed threshold (80%). The different clusters thus identify bacterial families restricted to a single body site (red or black) versus those inhabiting multiple body sites (red and black).
Tools: In order to generate this data visualization, samples of my oral and gut microbiome have been sequenced on a MiSeq platform by means of 16S rRNA targeted amplicon sequencing, and the resulting data have been analyzed using QIIME, an open-source bioinformatics pipeline for performing microbiome analysis. The gene similarity network was produced with the open graph viz platform Gephi, using the Fruchterman–Reingold algorithm.

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!

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