Topic Archives: Personal Projects

Robby Macdonell: The Data Is in, I am a Distracted Driver

“When I see someone driving towards me with their face buried in their phone, I get gloriously indignant about it.”

Robby Macdonell has given great talks on transportation logging and time-tracking. Here, he combined those two data streams, using Automatic and RescueTime, to prove that he does not use his phone while driving nearly as often as other drivers.

Only the data didn’t agree.

Watch how Robby confronts the realization that he is more distracted than he thought and the changes he made because of it.

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Tuning My Brain With Music For Reading, Programming, and Archery by Rocio Chongtay

In this fascinating talk Rocio Chongtay shares her novel and thoughtfully designed experiments in using music to adjust her concentration and relaxation depending on what she’s doing. Using a consumer EEG device from Neurosky, Rocio tried different types of music while tracking the relaxation and concentration dimensions identified by the Neurosky algorithm. She had experience experimenting with Neurosky in her lab, and then turned these techniques on understanding something about her own mind.

 

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Re-Living My Life with Mood Tracking by Kouris Kalligas

Kouris Kalligas, a long time participant and contributor at Quantified Self meetings, is the creator of the very easy to use data aggregation service AddApp. AddApp is an iPhone app that makes it simple to gain insights from data gathered on dozens of different devices. While running his startup, Kouris has also been doing  ongoing self-tracking experiments. At QS Europe 2014, he gave a excellent show&tell talk about his sleep, diet, and exercise data. In the talk below, he discusses using mood data in combination with calendar data to reflect on the relationship between emotion, experience, and self-image.

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Randy Sargent on Using Spectrograms to Visualize Heart Rate Variability

Let’s start 2016 with a very interesting talk by Randy Sargent about how to visualize the very large data sets produced by some kinds of self-tracking. Randy’s idea about using spectrograms, normally used for audio signals, to create a portrait of your own time series data, is completely novel as far as I know. If you have tried something similar, please get in touch.

 

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Effects of A Year in Ketosis by Jim McCarter

In this fascinating short talk by geneticist Jim McCarter, we see detailed data about the  effects of a ketogenic diet: lower blood pressure, better cholesterol numbers,and vastly improved daily well being.  Jim also describes the mid-course adjustments he made to reduce side effects such as including muscle cramps and increased sensitivity to cold.

Jim begins: “When I tell my friends I’ve given up sugar and starch and get 80% of my calories from fat, the first question I get is: Why?”

The rest of the talk is his very clear answer.

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Gary Wolf: Even when I'm active, I'm sedentary


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We recently released our QS Access app, which allows you to see HealthKit data in tabular format. Not very many tools feed data into HealthKit yet, but Apple’s platform does pick up step data gathered by the iPhone itself. I have step data on HealthKit going back about two weeks. When Ernesto Ramirez and I were playing around with QS Access, loading the data into Excel and looking at some simple charts, I learned something: Even when I’m active, I’m sedentary.

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My daily step totals ranged from a depressing 3334 steps on Thursday, September 18 to an inspiring 21,634 steps on Friday, September 25, but – as these charts clearly show – even  on the extreme days my activity was concentrated into relatively short periods when I got up from my desk and went out to do something. Most hours, every day, were spent with hardly any movement at all. I’m sitting at my desk, and sitting at my desk some more, and sitting at my desk still more. That’s probably not good. No, not good at all.

Pulling my data out of HealthKit and seeing a few simple charts gave me a bit of insight that I hope will lead to a change in how much I sit. It was a great to be able to easily make some simple analysis of my data. I hope you’ll find QS Access useful also (you can learn more about it here). Please share what you learn in the QS Access thread in the QS Forum or by emailing us about your projects: qsaccess@quantifiedself.com.

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How to Download Minute-by-Minute Fitbit Data

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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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Matthew Ames: One Small Step on a Scale

In this video from the Boston Quantified Self Show&Tell, Matthew Ames describes the self-tracking project that dramatically changed his weight and fitness. Beginning with simply measuring his weight daily using a Withings scale, he added together a number of common QS tools, including Weight WatchersRunkeeper, MyFitnessPalGarmin Forerunner watch, and the Nike+ system, to support his self-transformation.

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Quick and Reliable Mood Measurement

Sami Inkinen, triathalete, self-quantifier, and founder of Trulia, measures his mood on a five point scale every morning, within five minutes of waking up. This method fascinates me. I do something similar (though I use only a three point scale). Sami has found that this quick and easy measurement reliably correlates with his athletic performance, suggesting that it indeed measures something significant about his overall well being in the day ahead.

Read Sami’s full post here: What the first 2 minutes after waking up can tell you about the day ahead?

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Living Without Numbers

Here at Quantified Self we talk about living with numbers. Look a few inches above these words and it’s right there in our four-word tagline: self knowledge through numbers. Information, increasingly numerical information, is becoming a driving force in the world. We’re surrounded by numerical representations of ourselves in almost all aspects of our lives. Probably no more so than in our digital interactions with each other.

Those meaningful and mundane comments, retweets, posts, likes, scores, friends, and followers all have raw or algorithmically defined numerical value. What you think of those value isn’t as necessary as understanding that a value exists. This is the place where you would probably expect me to go on  long diatribe about the nature of scoring our social lives, but I’ll leave that for others to handle. It’s a hot topic and I’m sure you’ll able to find well-written arguments with a quick google search. Instead I want to use this space to show you something interesting and thought provoking.

Facebook Demetricator

Ben Grosser is an artist and a composer, and is currently completing an MFA in New Media at the University of Illinois at Urbana-Champaign. He’s also the creator of a nifty little piece of software called Facebook Demetricator. Simply put, the Facebook Demetricator is a  browser extension that hides the numerous instances of social metrics that live in the Facebook interaction experience:

The Facebook interface is filled with numbers. These numbers, or metrics, measure and present our social value and activity, enumerating friends, likes, comments, and more. Facebook Demetricator is a web browser addon that hides these metrics. No longer is the focus on how many friends you have or on how much they like your status, but on who they are and what they said. Friend counts disappear. ’16 people like this’ becomes ‘people like this’. Through changes like these, Demetricator invites Facebook’s users to try the system without the numbers, to see how their experience is changed by their absence.

How does it work? Take a close look at the short gif below or watch this short explanatory video.

Facebook Demetricator Prototype Removing Metrics on the Friends Page

Ben did a great interview with Matthew Fuller that I highly suggest you read if you’re interested in learning more him and the reasoning behind this project. I preparation for this short post I emailed Ben and asked him his thoughts on how this fits into the broader conversation around Quantified Self:

I suppose a key difference between the metrics on Facebook and a more typical QS approach is that in QS one has substantial control over what they collect and how they interpret it.  On Facebook, the what, how, and even in some cases, the why is largely left to the system to manage.  In response, Demetricator removes the numbers and thus makes the familiar unfamiliar, focusing us on the ways those numbers are functioning as drivers of interaction. It’s not that the numbers themselves are bad, but they’re certainly worth questioning—especially when their accumulation and presentation are handled by others.

I’ve leave you with a few thoughts I’ve had on this as I’ve been contemplating this work and Ben’s response.

Numbers are powerful. Ben mentions in his announcement and again in the interview that one of reasons for creating this software was to combat the “(capitalism-inspired) innate desire for more.” Numbers have this seemingly magical evolutionary trait in that they seek to increase and we as creators and consumers of those numbers tend to oblige (I understand this isn’t true for all numbers). This isn’t a judgement statement, just something we need to be aware of as we build and interact with social and behavioral systems that are becoming increasingly quantifiable.

Mind the data. There are these great little inlaid signs in the London Underground that remind passengers to “mind the gap.” It serves as a simple warning to make people aware of the distance between the train and the platform. Being mindful or ourselves through the lens of the data we produce and use has been a recurring theme lately for some of us in the QS community. So much so that we opened up the QS Conference with a wonderful presentation by Nancy Dougherty on Mindfulness in QS. With the increasing scorification of our social interactions in various online mediums the Facebook Demetricator reminds us to be mindful of the role numbers play in our lives and how we choose to use them.

What do you think? Take a look at Ben’s project and if you use it let us know your thoughts. I’ve created a post on the Quantified Self Forum for discussion, but feel free to comment here as well.

Thanks for Ben Grosser and Alex Carmichael for providing feedback on this post. Also thanks to everyone (especially @xarodai) who uses the #quantifiedself hashtag on Twitter. Without you this unique tool would have never surfaced for me. 

 

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