Ernesto Ramirez

Ernesto Ramirez
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Cliff Atkinson: Storyboarding the Psyche

CliffA_Storyboard

Cliff Atkinson is a consultant who helps people tell their stories and showcase their data in clear and understandable ways. It’s no surprise that when he became interested in understanding himself he turned to his experiences with visual storytelling. In 2012, at a New York QS meetup, Cliff spoke about how he’s embarked on a project to “quantify the “unconscious.”

What Did He Do?
Cliff began this project because he was noticed that there were “recurring patterns of procrastination and motivation” going on in his life. He began trying to understand them by turning to the large body of literature on human psychology. Then he asked himself, “Would it be possible to use some quantitative methods to track what was happening.” Using what he’d learned in his research and his experiences he decided to track his body, emotions, and mind.

How Did He Do It?
Cliff used his expertise and knowledge around visual storytelling to create an interesting system of visual diaries with which he could record information in his three areas of interest: the body, emotions, and the mind. Using Penultimate, and iPad app for sketching and notation, along with some clip art, he tracked physical, emotional, and cognitive events.

What Did He Learn?
The process of creating a space to reflect and record how he’s feeling across these three chosen domains has created a space for Cliff to better understand himself and how his mind works. This is still a work in progress and it sounds like Cliff is still exploring how to better understand the data he’s capturing over a longer period of time and even correlating it with other information such as his work and speaking engagements.

“One of the models for therapy is that somebody else helps you. I think with the quantified self and the things we’re doing we can take some of that power into our own hand and start to come to some personal understanding of what’s going on in our own lives.”

Tools
Penultimate

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Steve Zadig: Life in the Fast Lane

Steve Zadig is the COO of Vital Connect, but when he’s not busy with his job he’s out racing high performance vehicles. In this talk, presented at our 2013 Global Conference, Steve explains how he uses data to help him achieve his racing goals.

What did Steve do?
Steve wanted to get more information about how his body was reacting during racing. Frustrated that he was getting a lot of diagnostic data from his car and not any from himself he sought to track different biometrics to see what he could learn about what happens while he’s behind the wheel.

How did he do it?
Steve wore a Vital Connect patch to record and transmit his respiration rate, heart rate, and stress levels while he was was racing.

What did he learn?
After the race Steve was able to match the data with specific points and events during the race. He learned how something major, like spinning out of control, caused a large spike in stress, and how when he’s feeling in the zone his body responds with a lower heart and breathing rate.

“It’s about knowing. It’s about the knowledge of what’s happening with your body and how to deal with that.”

The QS15 Conference and Exposition is fast approaching. We invite you attend and give show&tell talks just like this one about your tracking and personal data experiences.

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What We Are Reading

We’ve put together an nice list of articles for you to enjoy this weekend. As always, please get in touch if you have something you’d like us to share!

Articles
Finding Patterns in Personal Data by Kitty Ireland. Another great post from Kitty about using personal data to uncover interesting, and sometimes surprising, patterns. Some great examples in this post!

The Tale of a Fitness-Tracking Addict’s Struggles With Strava by Jeff Foss. Just because you can track, and you can get something out of it, might not mean you should. (I had a similar experience on a recent trip to Yosemite so this article was quite timely.)

Algorithmic skin: health-tracking technologies, personal analytics and the biopedagogies of digitized health and physical education by Ben Williamson. Quantified Self and self-tracking tools are not limited to only being used by conscious and willing adults. They’re also being developed for and used by a growing number of children and adolescents. What does this mean of health and fitness education, and how should we think about algorithms in the classroom and gym?

Seeing Ourselves Through Technology: How We Use Selfies, Blogs and Wearable Devices to See and Shape Ourselves by Jill Walker Rettberg. I just started this book and it appears offer some interesting perspectives on the current cultural shift toward technically mediated representation. The book includes a chapter on Quantified Self and is available for download in PDF and EPUB under a CC BY license.

Show&Tell
Why Log Your Food by Amit Jakhu. Amit started tracking his food in March (2014) and has since learned a few things about his preconceived notions about his diet, food, and what it takes to keep track of it all.

Even When I’m active, I’m sedentary by Gary Wolf. Gary and I used our recently released QS Access app to download his historical step data. Using some simple charting in Excel we found some interesting patterns related to his daily movement.

Visualizations
SleepJewel
When Do I Sleep Best by Jewel Loree. Jewel presented her sleep tracking project at a recent Seattle QS Meetup. The image above is just a small piece of a great set of visualizations of her data gathered with SleepCycle and Reporter apps.

20min
Lightbeam Visualization by Simone Roth. Interesting tool described here to track how your data and web activity is being tracked. You can check out the Firefox extension here.

Minard It’s About Time by Hunter Whitney. A nice post here about the different methods of visualizing temporal data.

Respiration Machine 0.3 (bellows) by Willem Besselink. A neat physical visualization and art project that represents breath using a Hoberman sphere.

From the Forum

There has been a lot of great discussion on the forum lately. Check out some of the newest and most interesting topics below.

QS Access App
Hypoxic – An App for Breathing Exercises with HRV Tracking
Sleep Tracking & Hacking Google Hangout
Personal Analytics Service for Software Developers
Using Facial Images to Determine BMI
The Right Tool? (tracking and plotting sleep)

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What We Are Reading

We hope you enjoy this weeks list. Feel free to submit articles, show&tell self-tracking stories, and QS data visualizations. Just email me!

Articles
Why can’t you track periods in Apple’s Health app? by Nat Buckley. With the recent re-release of Apple’s HealthKit enabled self-tracking and personal data system it no wonder that people are taking a long hard look at what data is being excluded. With the popularity of menstruation tracking apps (this app has nearly 30,000 ratings) it’s surprising this was overlooked. This excellent post is a must read on the topic.

Now That Cars Have Black Boxes, Am I Being Tracked? by Popular Science Editors. Questions and concerns about surveillance are becoming more commonplace. As someone who is looking to purchase a car in the next year or so I was happy to see this post come across my stream.

The Quantified Self community, lifelogging and the making of “smart” publics by Aristea Fotopoulou. I love it when people take a thoughtful look at the Quantified Self community and write about their experiences:

For me, the potential of QS for public participation lies in the show and tell meet-ups that constitute a central feature of this community. Meet-ups enable the exchange of stories about the success or failure of lifelogging practices; they allow people to connect and form synergies around common interests, and to explore wider questions such as personal data management and ownership. [...] members touch upon key political issues and create temporary spaces of dialogue: what happens to personal data, who has access to these data (is it private individuals, governments or corporations)? For what purposes (medical research)? And how can these data be interpreted (by algorithms, visualisations) and used to tell stories about people?

Stepping Down: Rethinking the Fitness Tracker by Sara M. Watson. Sara uses her personal journey of recovery from hip surgery to frame an interesting question: Should we trust our fitness trackers to prescribe movement goals?

Show&Tell
Practical Statistical Modeling: The Dreaded After-School Carpool Pickup by Jamie Todd Rubin. Jamie wanted to understand if there was a way he could reduce how much time he spent waiting in line to pick up his son from school. Why not track it and model it!

Bulletproof Diet and Intermittent Fasting: 1.5 Year Results by Bob Troia. Bob takes a deep dive into his data to see if this particular diet is having beneficial health effects. Click for the great data, stay for the wonderful discussion and very, very thorough write-up.

Visualizations


Quotidian Record by Brian House. I’ve been a fan of Brian House since his early days visualizing Fitbit data. I was reminded of this work during a conversation about geolocation data and thought it would be a nice addition to our visualization list.

KMcCurdy_SMVisualizing My Daily Self-Management by Katie McCurdy.

What does my daily medication and self-management look like? How could I visualize this regimen? How can I communicate the ‘burden’ and work of caring for myself?

I decided to draw pictures of the things that I need to do on a daily basis; that way I could show the workshop attendees what my day was like instead of just telling them.

JawboneTimetoEatIt’s Time to Eat by Karl Krehbiel. Karl, a data science intern at Jawbone used the data from their global community of users the determine the likelihood of food and drink consumption during the day. Really fun and interesting visualizations here.

From the Forum
Seeking opinions of diabetic self-trackers for non-profit project
Five Years of Weight Tracking
What Disclaimer should I use when making my personal #quantifiedself data public?

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How to Download Minute-by-Minute Fitbit Data

IntradayDataChart

Earlier this week we posted an update to our How To instructions for downloading your Fitbit data to Google Spreadsheets. This has been one of our most popular posts over the past few years. One of the most common requests we’ve received is to publish a guide to help people download and store their minute-by-minute level step and activity data. Today we’re happy to finally get that up.

The ability to access and download the minute-by-minute level (what Fitbit calls “intraday”) data requires one more step than what we’ve covered previously for downloading your daily aggregate data. Access to the intraday data is restricted to individuals and developers with access to the “Partner API.” In order to use the Partner API you must email the API team at Fitbit to request access and let them know what you intend to do with that data. Please note that they appear to encourage and welcome these type of requests. From their developer documentation:

Fitbit is very supportive of non-profit research and personal projects. Commercial applications require additional review and are subject to additional requirements. To request access, email api at fitbit.com.

In the video and instructions below I’ll walk you through setting up and using the Intraday Script to access and download your minute-by-minute Fitbit Data.

  1. Set up your FitBit Developer account and register an app.
    • Go to dev.fitbit.com and sign in using your FitBit credentials.
    • Click on the “Register an App” at the top right corner of the page.
    • Fill in your application information. You can call it whatever you want.
    • Make sure to click “Browser” for the Application Type and “Read Only” for the Default Access type fields.
    • Read the terms of service and if you agree check the box and click “Register.”
  2. Request Access to the Partner API
    • Email the API team at Fitbit
    • They should email you back within a day or two with  response
  3. Copy the API keys for the app you registered in Step 1
    • Go to dev.fitbit.com and sign in using your FitBit credentials.
    • Click on “Manage My Apps” at the top right corner of the page
    • Click on the app you created in Step 1
    • Copy the Consumer Key.
    • Copy the Consumer Secret.
    • You can save these to a text file, but they are also available anytime you return to dev.fitbit.com by clicking on the “Manage my Apps” tab.
  4. Set up your Google spreadsheet and script
    • Open your Google Drive
    • Create a new google spreadsheet.
    • Go to Tools->Script editor
    • Download this script, copy it’s contents, and paste into the script editor window. Make sure to delete all text in the editor before pasting. You can then follow along with the instructions below.
    • Select “renderConfigurationDialog” in the Run drop down menu. Click run (the right facing triangle).
    • Authorize the script to interact with your spreadsheet.
    • Navigate to the spreadsheet. You will see an open a dialog box in your spreadsheet.
    • In that dialog paste the Consumer Key and Consumer Secret that you copied from your application on dev.fitbit.com. Click “Save”
    • Navigate back to the scrip editor window.
    • Select “authorize” in the Run drop down menu. Click run (the right facing triangle).
    • Select “authorize” in the Run drop down menu. This will open a dialog box in your spreadsheet. Click yes.
    • A new browser window will open and ask you to authorize the application to look at your Fitbit data. Click allow to authorize the spreadsheet script.
  5. Download your Fitbit Data
    • Go back to your script editor window.
    • Edit the DateBegin and DateEnd variables with the date period you’d like to download. Remember, this script will only allow 3 to 4 days to be downloaded at a time. 
    • Select “refreshTimeSeries” in the Run drop down menu. Click run (the right facing triangle).
    • Your data should be populating the spreadsheet!

If you’re a developer or have scripting skills we welcome your help improving this intraday data script. Feel free to check out the repo on Github!

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Paul LaFontaine: Upset Every Other Minute

How many times during the course of the day do you find your mental state drifting into negativity, feeling like you’re lost, or just plain stressed? How could you even keep track of this, and why would you want to?

PaulLF_upsets

What Did Paul Do?
Paul LaFontaine has been tracking what he calls “upsets” to better understand himself, the way he works, and to see if he can improve his mental and physiological response and recovery.

Upsets are something physiological that were happening beneath the surface, and they’re trackable. It didn’t have to be emotional, but there had to be a signal. This project is part of an longer ongoing study. Before this current iteration I manually logged over 3,000 upsets and what I found is that most of my upsets were self-induced. I’d be in a calm environment, but then become upset about something. I wanted to use technology because I was afraid of bias and I know I was missing some upsets.

How Did He Do It?

I used the HeartMath EMWave2 that measures heart rate variability and indicates when you’re in and out of coherence. When I was out of coherence I captured that as an upset. I would stop what I was doing and use an audio recorder to keep track of the time, how long I was upset, the reason, and what method I used to recover. I tracked 71 sessions (each session was 25-45 minutes) totaling 42 hours of tracking time. I logged 1292 upsets during this period.

What Did He Learn?
Paul analyzed his data and found some very interesting insights about his upsets, his reasons for being upset, and the effectiveness of his recovery techniques.

I found that I was triggering an upset every 2 minutes. My wife said something must be wrong with me, but this stayed relatively constant through the tracking period. I started to think of it like skiing a mogul course. The moguls didn’t move, it was about how effective I could move through them. And, dealing with upsets is like playing whack-a-mole. They come fast and furious and every second counts.

For recovery I was able to find that my most effective technique was breathing. By returning to six breaths per minute routine I was able to improve recovery time from 33 seconds to 17.8 seconds. It was the primary way I could remove myself from being upset and make myself calmer.

We want to thank Paul for presenting this great QS project at the Bay Area QS Meetup group. Make sure to watch the full talk below to learn more about Paul’s methods and findings, then hop over to his website where you can read about how he tracked his stress during this talk.

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Downloading Fitbit Data: Update

We’re posting a quick note today to let you know that we’ve updated our “How To Download Your Fitbit Data” post. It now included separate instructions for both the old and new versions of Google Spreadsheets. This is just the first in a series of planned updates. We hope to post additional updates to allow you to have deeper access to your Fitbit data including, heart rate, blood pressure, and daily goal data.

If you’re using this how-to we’d love to hear from you! Are you learning something new? Making interesting data visualizations? Discussing the data with your health care team? Let us know. You can email us or post here in the comments.

ERFitbit_092214

Click to view the interactive version.

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What We Are Reading

We hope you enjoy this week’s list!

Articles
Big Data in the 1800s in surgical science: A social history of early large data set development in urologic surgery in Paris and Glasgow by Dennis J Mazur. An amazing and profoundly interesting research paper tracing the use of “large numbers” in medical science. Who knew that is all began with bladder stones!

Civil Rights, Big Data, and our Algorithmic Future by Aaron Rieke, David Robinson and Harlan Yu. A very thorough and thoughtful report on the role of data in civil and social rights issues. The report focuses on four areas: Financial Inclusion, Jobs, Criminal Justice, and Government Data Collection and Use.

Caution in the Age of the Quantified Self by J. Travis Smith. If you’ve been following the story of self-tracking, data privacy, and data sharing this article won’t be all that surprising. Still, I can’t help but read with fascination the reiteration of tracking fears, primarily a fear of higher insurance premiums.

Patient Access And Control: The Future Of Chronic Disease Management? by Dr. Kaveh Safavi. This article is focused on providing and improving access and control of medical records for patients, but it’s only a small mental leap to take the arguments here and apply them all our personal data. (Editors note: If you haven’t already, we invite you to take some time and read our report: Access Matters.)

Perspectives of Patients with Type 1 or Insulin-Treated Type 2 Diabetes on Self-Monitoring of Blood Glucose: A Qualitative Study by Johanna Hortensius, Marijke Kars, and Willem Wierenga, et al. Whether or not you have experience with diabetes you should spend some time reading about first hand experiences with self-monitoring. Enlightening and powerful insights within.

Show&Tell
Building a Sleep Tracker for Your Dog Using Tessel and Twilio by Ricky Robinett. Okay, maybe not strictly a show&tell here, but this was too fun not to share. Please, if you try this report back to us!

Digging Into my Diet and Fitness Data with JMP by Shannon Conners, PhD. Shannon is a software development manager at JMP, a statical software company. In this post she describes her struggle with her weight and her experience with using a BodyMedia Fit to track her activity and diet for four years. Make sure to take some time to check out her amazing poster linked below!

Visualizations
The following two visualizations are part of Shannon Conners’ excellent poster detailing her analysis of data derived from almost four years of tracking (December 2010 through July 2014). The poster is just excellent and these two visualizations do not do it justice. Take some time to explore it in detail!

SC_calorieweight

SC_sleep

Tracking Energy use at home by reddit user mackstann.

EnergyApp

“The colors on the calendar represent the weather, and the circles represent how much power was used that day. The three upper charts are real-time power usage charts, over three different time spans. I use a Raspberry Pi and an infrared sensor that is taped onto my electric meter. The code is on github but it’s not quite up to date (I work on it in bits and pieces as time permits I have kids).”

From the Forum
Help With Livestrong Data Export
Need Help Deciding Which Device
New to Fitness Tracking

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Welcome Steven Jonas

skjonas2

Today we are excited and honored to announce that Steven Jonas has joined QS Labs as our Senior Editor/Information Architect. As has been the case with previous additions to QS Labs, we welcome Steven as a friend and fellow community member. Steven serves as a co-organizer of the Portland QS meetup group, and has participated as our speaker coordinator for our past two conferences.

In addition to his work supporting our global QS community, Steven is an active self-tracker, having engaged in many different projects. We’ve been delighted to highlight a few of those here on the QS website. We invite you to welcome Steven and get to know him a bit by exploring the posts linked below.

Memorizing my Daybook
Tracking Stress
Stress Out Loud

Photo by Mark Krynsky

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James Norris: A Life of Firsts

Like many of us, James Norris remembers his first kiss. Unlike many of us, he also knows who it was with, where it was, and his age. How does he know this information? When he was 13, he realized that he forgot some detail about his life that he thought was important. To prevent that from happening again, he decided to carry around sticky notes to record important life events and has been doing it ever since. Fast forward 15 years and James has recorded 1,500 “firsts.” Watch this talk, presented at the Washington DC QS meetup group, to hear James talk about the data he collects, and the lessons he’s learned along the way.

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