Topic Archives: Discussions
At last month’s QS Europe 2013 conference, developers gathered at a breakout session to compile a list of common obstacles encountered when using the APIs of popular, QS-related services. We hope that this list of obstacles will be useful to toolmakers who have developed APIs for their tools or are planning to provide such APIs.
- No API, or incomplete APIs that exposes only aggregate data, and not the actual data that was recorded.
- Custom authentication mechanisms (instead of e.g. OAuth), or custom extensions (e.g. for refreshing tokens with OAuth 1.0a).
- OAuth tokens that expire.
- Timestamps that lack time zone offsets: Some applications need to know how much time has elapsed between two data points (not possible if all times are local), or what e.g. the hour of the day was (not possible if all times are converted to UTC).
- Can’t retrieve data points going back more than a few days or weeks, because at least one separate request has to be made for each day, instead of being able to use a begin/end timestamp and offset/limit parameters.
- Numbers that don’t retain their precision (1 != 1.0 != 1.00), or are changed due to unit conversion (71kg = 156.528lbs = 70.9999kg?).
- No SSL, or SSL with a certificate that is not widely supported.
- Data that lacks unique identifies (for track-ability, or doesn’t include its provenance (if obtained from another service).
- No sandbox with test data for APIs that expose data from hardware devices.
- No dedicated channel for advance notifications of API changes.
This list is by no means complete, but rather a starting point that we hope will kick off a discussion around best practices.
This slide from Mary Meeker’s Internet Trends slide deck (link is to full deck on Slideshare) puts some numbers around what we’ve been noticing among QS Toolmakers: everybody wants to talk APIs.
At Quantified Self we’ve come to appreciate the interest in QS from scholars, researchers, and scientists. The essay below, which originally appeared on the Society Pages blog Cyborgology, was written by the thoughtful QS participant and scholar, Whitney Erin Boesel (we have collaboratively made minor edits for this posting). We learned quite a bit from it and are honored that Whitney allowed us to repost it here. Essays such as these help us think critically about QS and our growing community. We hope that posting it here will spur discussion and we invite you to add your voice in the comments or email us with essays of your own.
When people ask me what it is that I’m studying for my PhD research, my answer usually begins with, “Have you ever heard of the group Quantified Self?” I ask this question because, if the person says yes, it’s a lot easier for me to explain my project (which is looking at different forms of mood tracking, primarily within the context of Quantified Self). But sometimes asking this return question makes my explanation more difficult, too, because a lot of people have heard the word “quantified” cozy up to the word “self” in ways that make them feel angry, uncomfortable, or threatened. They don’t at all like what those four syllables sometimes seem to represent, and with good reason: the idea of a “quantified self” can stir images of big data, data mining, surveillance, loss of privacy, loss of agency, mindless fetishization of technology, even utter dehumanization.
But this is not the Quantified Self that I have come to know. Continue reading
If you’re a loyal, or even infrequent user of the Zeo sleep tracking device then you’ve probably heard the sad news that the company has shut down. This opens up a lot of questions about what is means to make consumer devices in this day and age, but rather than focus on those issues we’ld like to talk a bit about data.
Zeo has been unfortunately a little quiet on the communication front and there are quite a few users out there who are wondering about what will happen to all those restless nights and sound sleeps that were captured by their device. This has been compounded by the fact that the Zeo website went down for a short time (it is up as of this writing) closing off access to user accounts and the data therein. Lucky for you there have been quite a few enterprising and enthusiastic individuals who have taken the time to create or highlight ways to capture and store your Zeo data.
Use The Zeo Website
You can’t fault Zeo with making it hard to access your own data. As long as their website is up you can easily download your sleep data from by logging into your user account at mysleep.myzeo.com. After logging into your account you will see a link on the right hand side labeled “Export Data.” Click that link and you’ll be able to download a CSV file containing all your sleep data. They’ve even provided a description of the data and formats that you can download here.
Eric Blue’s FreeMyZeo Data Exporter
QS Los Angeles Meetup Organizer and hacker extraordinaire whipped up a simple data export tool using the Zeo API. The great thing about Eric’s is that even if the myZeo web portal goes down this tool should continue to work.
Download Data Directly From the Device
If you’re using a Zeo bedside device then you can continue to use it and download the data directly from the memory card without relying on uploading it to the Zeo website. In order to do this you’ll have to read the documentation and use the Data Decoder Library. These files are hard to find as they’ve been removed from the Zeo developer website, but you can access them from our Forum thanks to our friend Dan Dascalesu. Zeo also created a viewer using this library that you can use via this Sourceforge page.
If you’ve found another way to download Zeo data please let us know. You can also participate in the great forum discussion that inspired this post.
It’s no secret we love data here at Quantified Self, but we also love seeing how people interact with data. We’ve explored many of those interactions here and we’re always on the lookout for new and different ways people communicate their data and the insights therein. A few weeks ago we wrote up a short “how to” post describing a recent phenomenon on Twitter – sparktweets. It didn’t take too long before we started seeing the Quantified Self community using these new “data words.”
— P.G. Holder (@pat_holder) April 16, 2013
— Benny Wong (@bdotdub) April 14, 2013
We couldn’t stop thinking about sparktweets. What kind of data could you communicate in 140 characters? What would people do if it was easier to make a sparktweet? So we asked out friend Stan James to help us out and our Sparktweet Tool was born. Since then we’ve seen some great tweets roll though our feed, and we would love to see more. Need some inspiration? Here’s a few we really enjoyed:
▄▃▄▃█▁█▁█▁█ My heart when I walked up to her door, 13 years ago today. (quantifiedself.com/sparktweet-too…)
— Gary Wolf (@agaricus) April 30, 2013
— Robby Macdonell (@robby1066) May 1, 2013
— Martin Putniorz (@sputnikus) May 2, 2013
— BuildingIoT (@BuildingIoT) May 2, 2013
A quick post here to highlight some interesting developments in the heart rate tracking space. Tracking and understanding heart rate has been a cornerstone of self-tracking since, well since someone put two fingers on their neck and decided to write down how many pulses they felt. We’ve come a long way from that point. If you’re like me tracking heart rate popped up on your radar when you started training for a sporting event like a marathon or long distance cycling. Like many who used the pioneering devices from Polar it felt a bit odd to strap that hard piece of plastic around my chest. After time, and seeing the benefits of tracking heart rate, it became part of my daily ritual. Yet, for all the great things heart rate monitoring can do for physical training, there have been very few advances to provide people with a noninvasive method. That is, until now.
Thearn, an enterprising Github user and developer, has released an open source tool that uses your webcam to detect your pulse. The Webcam Pulse Detector is a python application that uses a variety of tools such as OpenCV (an open source computer vision tool) to “find the location of the user’s face, then isolate the forehead region. Data is collected from this location over time to estimate the user’s heartbeat frequency. This is done by measuring average optical intensity in the forehead location, in the subimage’s green channel alone.” If you’re interested in the research that made this work possible check out the amazing work on Eulerian Video Magnification being conducted at MIT. Now, getting it to work is a bit of a hurdle, but it does appear to be working for those who have the technical expertise. If you get it working please let us know in the comments. Hopefully someone comes along that provides a bit of an easier installation solution for those of us who shy away from working in the terminal. Until then, there are actually quite a few mobile applications that use similar technology to detect and track heart rate:
Let us know if you’ve been tracking your heart rate and what you’ve found out. We would love to explore this space together.
Update: Want to make your own Sparktweet? We made a simple tool that you can use. Check it out here!
I was stumbling around Twitter the other day when I was confronted with something new and different:
— Steve Cavendish (@scavendish) April 5, 2013
Apparently that little data representation is not all that new and different. Way back in 2010 Alex Kerin figured out that Twitter was accepting unicode and decide to play around and see if it could represent data. Lo and behold it could and a SparkTweet was born:
▁▁▂▂▃▄▄█▁▁▂ ▃▄▄▅▆▁▁▂▂▃▄▄▅▆▁▁▂▂▃▄▄▅▆ Can you guess what I’m coding in Excel? Eh? Eh?
— Alex Kerin (@AlexKerin) June 9, 2010
Before we get into how you too can start populating your Twitter feed and Facebook (I checked and it worked there as well) with representations of your own Quantified Self data let’s dive into some history.
a small intense, simple, word-sized graphic with typographic resolution. Sparklines mean that graphics are no longer cartoonish special occasions with captions and boxes, but rather sparkline graphic can be everywhere a word or number can be: embedded in a sentence, table, headline, map, spreadsheet, graphic.
In another wonderful book, The Visual Display of Quantitative Information, Tufte describes sparklines as “datawords: data-intense, design-simple, word-sized graphics.“ Of course, those of us in the QS community are deeply interested not only in data, but also in how data operates in society, what is means as a cultural artifact that is discussed and exchanged in language both written and verbal. This interest iswhat initially piqued my curiosity. The movement of data and a dataword distributed among text and publicly expressed in a tweet. I can’t help but wonder, what does this mean for how we think about and express data about our world?*
If you want display quantitative data in your Twitter stream it shouldn’t take you all that long to get started. Lucky for us Alex Kerin has provided a nifty little Excel workbook that will generate the unicode that can be pasted into your tweet. Just download this workbook and follow the simple instructions! Soon you’ll be able to send out tweets just like this:
My 30-day step history: ▄ ▄ ▄ ▅ ▅ ▅ ▄ ▆ ▄ █ █ ▅ ▁ ▃ ▆ ▅ ▁ ▄ ▇ ▃ ▅ ▆ ▂ ▂ ▅ ▃ ▄ ▄ ▅ ▄ #QuantifiedSelf
— Ernesto Ramirez (@eramirez) April 11, 2013
Now you’re ready and able to go forth and tweet your data! If you use a sparktweet to express your Quantified Self data be sure to let us know in the comments or tweet at us with #sparktweet and/or #quantifiedself.
*Of course the use of sparktweets is not without controversy in the world of data visualization. For more discussion on sparktweets and their utility I suggest you start here.
At the Quantified Self conference last year I attended a breakout session for scholars interested in QS as a research topic. There was an interesting range of fields represented, including medicine, anthropology, sociology, and public health. I’ve appreciated the criticism that the researchers bring. For instance, nearly all of the anthropologists are trained to see technologies of enumeration as tools of domination and control. That means that when they see us tracking ourselves, they wonder about whether the language of self-knowledge merely covers up for conformist obedience to corporate monitoring. They suspect we may be locking ourselves into our panoptical prisons. Their suspicions are sometimes soft pedaled out of politeness, but eventually they come out, and then the real conversation starts.
Recently, Dawn Nafus and Jamie Sherman, two anthropologists working at Intel Labs, have spent quite a lot of time talking with people they’ve met through the Quantified Self conferences and Meetups. (Intel has been a Quantified Self sponsor and we are currently collaborating on a research project on how to improve the ways we can learn from our own data.) In the paper linked below, Nafus and Sherman discuss the relation between QS, narcissistic focus on individual improvement, consumerist gadget love, and what they call the “soft resistance” of real self-tracking practice.
They’ve also described the direction of their work in this blog post.
The Quantified Self that we know has very little to do with trying to control other people’s body size or fetishizing technology. Indeed, people who use pen and paper are community leaders alongside professional data analysts. As a social movement, QS maintains a big tent policy, such that the health care technology companies who do try to control other people’s body sizes also participate. But QS organizes its communities in ways that require people to participate as individuals with personal experiences, not as companies with a demo to sell. This relentless focus on the self we suspect does have cultural roots in neoliberalism and the practices of responsibilization Giddens identified so long ago, but it also does important cultural work in the context of big data.
QSers self-track in an effort to re-assert dominion over their bodies by taking control of the data that many of us produce simply by being part of a digitally interconnected world. When participants cycle through multiple devices, it is often not because they fetishize the technology, but because they have a more expansive, emergent notion of the self that does not settle easily into the assumptions built into any single measurement. They do this using the technical tools available, but critically rather than blindly. It is not radical to be sure, but a soft resistance, one that draws on and participates in the cultural resources available.
At the upcoming Quantified Self conference in Amsterdam on May 11/12, there will be another breakout session specifically for researchers who take QS as their topic, and I hope the critical dialog will continue.
(Co-written with Gary Wolf)
In January we started asking ourselves, “How many people self-track?” It was an interesting question that stemmed from our discussion with Susannah Fox about the recent Pew report on Tracking for Health. Here’s a quick recap of the discussion so far.
The astute Brian Dolan of MobiHealthNews suggested that the Pew data on self-tracking for health seems to show constant – not growing – participation. According to Pew, in 2012 only 11% of adults track their health using mobile apps, up from 9% in 2011.
All this in the context of a massive increase in smartphone use. Pew data shows smartphone ownership rising 20% just in the last year, and this shows no signs of slowing down. Those smartphones are not just super-connected tweeting machines. They pack a variety of powerful sensors and technologies that can be used for self-tracking apps. We notice a lot of people using these, but our sample is skewed toward techies and scientists.
What is really going on in the bigger world? How many people are actually tracking?
A few weeks ago ABI, a market research firm, released a report on Wearable Computing Devices. According to the report there will be an estimated 485 million wearable computing devices shipped by 2018. Josh Flood, the analyst behind this report indicated that they estimated that 61% of all devices in wearable market are fitness or activity trackers. “Sports and fitness will continue to be the largest in shipments,” he mentioned “but we’ll start to see growth in other areas such as watches, cameras, and glasses.”
One just needs to venture into their local electronics retailer to see that self-tracking devices are becoming more widespread. So why are our observations out of synch with the Pew numbers?
The answer may lie in the framing of the Pew questions as “self-tracking for health?” For instance:
On your cell phone, do you happen to have any software applications or “apps” that help you track or manage your health, or not?
Thinking about the health indicator you pay the most attention to, either for yourself or someone else (an adult you provide unpaid care for), how do you keep track of changes? Do you use paper, like a notebook or journal, a computer program, like a spreadsheet, a website or other online tool, an app or other tool on your phone or mobile device, a medical device, like a glucose meter, or do you keep track just in your head?
We think it is likely that many practices we include in our definition of Quantified Self are not being captured by the Pew Research. A person who tracks a daily run with a Garmin GPS watch might show up in the wearables data that ABI looks at, and might look to us as a self-tracker for health, but might be invisible to Pew. There may be even self-tracking practices that fall outside health or wearables. We’ve seen a large number of people who track time and productivity using computer applications such as RescueTime, apps that support well-being such as meditation trackers, mood trackers, and diet trackers; and apps that support general self-reflection and journaling, such as a life-logging app. Many self-tracking practices do not fit neatly into “health.” (Though they may influence health!)
In a way, there is a parallel here to what we found when we compared Fitbit with Fuelband data. Both of them produced different numbers for “steps.” When we got into the details, we ended up thinking that this was not a matter of one being closer to the “ground truth,” but of intentionally different interpretations of messy accelerometer data. Fitbit gives more step credit for general movement, because it is a lifestyle/activity tracker; Nike might prefer to credit intentional exercise, since the Nike brand sits closer to sports. Context matters.
This confusion about what is health tracking, what fits in the frame, is closely analogous to many other confusions in the conversation about health generally. It is common now in the healthcare world to talk about how the larger determinants of public health are outside the control of the healthcare industry; for instance, diet, exercise, stress, and exposure to environmental toxins. Sometimes people who make these observations follow them with a call for the healthcare industry to begin addressing these larger concerns; for instance, to “medicalize” tracking apps by making them prescribable and reimbursable by health insurers.
But maybe this isn’t the only approach. If the “healthcare” frame isn’t adequate to capture the most important determinants of health, we could try switching frames. What our journey through the self-tracking data suggests is that the opposite approach might be useful to consider: start with the bigger world of self-care practices, and enhance these. Why? Because that’s where we trackers already are. That is, how are we deriving meaning from self-tracking? That’s the mental framework that we typically use, and that we like to use. That’s where the growth – in terms both of us, and of cultural understanding, engagement, and knowledge-making – might really be happening.
We don’t know this for sure. We take the Pew data as evidence that this approach is worth trying.