Finding My Optimum Reading Speed by Kyrill Potapov

As an English teacher Kyrill Potapov spends a lot of time working with 12 year old kids who are trying to improve their reading, writing, comprehension, and analytical skills. In this talk, he explores a remarkable method of speed reading, called Spritz, that promises to let you “read Harry Potter in three hours” with full understanding and recall. Could such a promise possibly be true? And, if the claim is true, another question arises. Is such a pace desirable and useful, or rather something quite alien to the activity of reading?

With his students, Kyrill decided to resolve these questions empirically, reading the same material in a book, on a screen using conventional scrolling, and on a screen using the novel method of Spritz, which displays words one at a time at a pace determined by the reader. They found high comprehension at the high speeds permitted by Spritz, but with some cost, which he outlines in this wonderfully clear and interesting talk.

 

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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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Daniel Reeves on Frictionless Tracking with Beeminder

It’s  been an honor to have Beeminder founders Daniel Reeves and Bethany Soule participating in Quantified Self meetings, giving us a chance to watch the evolution of their very useful tool for setting and achieving personal goals. These days they are working on the forefront of device and service integration. In this talk Daniel gives a brief explanation of how to bring data into Beeminder with minimal hassle.

(Note Daniel’s generous shout-out to another great QS toolmaker, Rescue Time.)

 

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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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Vinod Khosla on machine learning and the Quantified Self

I talked with Vinod Khosla over the summer about machine learning and the Quantified Self.

Khosla was a founder of Sun Microsystems and is one of Silicon Valley’s most experienced investors in Quantified Self companies. His portfolio includes AliveCor, Ginger.io, Jawbone, Misfit, Narrative, and many other toolmakers that people doing QS projects will recognize. In our conversation, I ask Vinod about the role machine learning can realistically play in QS practices.

Below are links to three papers Vinod mentions in the interview:

Khosla, Vinod. “20-percent doctor included: Speculations & musings of a technology optimist.” (2014).

Beck, Andrew H., Ankur R. Sangoi, Samuel Leung, Robert J. Marinelli, Torsten O. Nielsen, Marc J. van de Vijver, Robert B. West, Matt van de Rijn, and Daphne Koller. “Systematic analysis of breast cancer morphology uncovers stromal features associated with survival.” Science translational medicine 3, no. 108 (2011): 108ra113-108ra113.

Ioannidis, John PA. Why Most Published Research Findings Are False. PLoS Med 2, no. 8 (2005)

 

 

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Greetings to our friends at QS Torino

QSTorino

Last night’s meeting of the Quantified Self in Turin, Italy.

 

We haven’t gotten the full report yet, but this looks like a wonderful meeting!

 

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Visualizing Blood Glucose

 

For people who take insulin, self-measurement is a matter of life and death. No wonder, then, that people with diabetes who track their blood glucose have been so important in advancing techniques of visualization,and understanding data. At the Quantified Self Europe conference in Amsterdam this year, we were honored to host a panel discussion on Data Visualization and Meaning with Joel Goldsmith (Abbott Diabetes Care), Jana Beck (Tidepool), Doug Kanter (Databetes), and Stefanie Rondags (diabetes coach and blogger).

This discussion strikes me as widely important for self-trackers whether or not we have diabetes. Many  of us will be tracking blood glucose in the near future. And the issues of data access, understanding, and clinical relevance that people with diabetes are working on resemble challenges commonly faced by anybody who is tracking for health.

For instance, Jana Beck was asked during the Q&A about her health care providers. How receptive are they to the important experiments she’s done to improve her health based on the data she’s collected? ”None of my endocrinologists have been very receptive to this approach,” she answered. “My A1C tends to fall within the range of what’s considered the gold range for people with Type 1. But I’m interested in optimizing that further. Often, I don’t even see them more than twice a year.”

Jana, Stefanie, and Doug all showed their own data in the context of discussing experiments and decisions that have had a major impact on their wellbeing. All were clear that the domain of these experiments and decisions is not healthcare as traditionally understood; but nor is it a matter of general fitness or lifestyle. The domain of these experiments is different and perhaps still unnamed. Self-collected data can and should essential health decisions, but the most advanced techniques of understanding this data are still being developed in an ad-hoc, grassroots way, by knowledgeable and open minded individuals who have a strong interest in learning for themselves.

At the end of the session I asked Joel Goldsmith, of Abbott Diabetes care, about the future prospects of the Freestyle Libre, a minimally invasive wearable blood glucose monitor that is not yet available in the US. (Disclosure: Abbott Diabetes Care was one of the sponsors of the QS Europe Conference.) The Freestyle Libre has a sensor in the form of a patch worn on the arm, and a touchscreen reader device that you lift close to the sensor to get a reading. There is no finger prick involved. While this and competing minimally invasive or non-invasive glucose monitors will almost certainly continue to be regulated as medical devices and understood as part of the health care system, many other people will also use them, and the flood of data and the questions that go with it will challenge our understanding of where this type of information should live.

The video above contains the full session, including the Q&A.

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What We Are Reading

A plate from "Food-Data" by artist Tobias Zimmer

A plate from “Food-Data” by artist Tobias Zimmer

Featured

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.

Articles

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.

Show&Tell

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.

Visualizations
PathwaysProject_Co-Workers_Map

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.

WX7VJoM
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

Internet Pings

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?

Lastly
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…
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Upcoming Quantified Self Meetups #50

Eight Quantified Self meetups are coming up in the next two weeks. To see when the next meetup in your area is, check the full list of the over 100 QS meetup groups in the right sidebar. Don’t see one near you? Why not start your own! If you are a QS Organizer and want some ideas for your next meetup, check out the myriad of meetup formats that other QS organizers are using here.

Monday, October 26
Copenhagen, Denmark
London, England

Monday, November 2
Oxford, England

Tuesday, November 3
Reno, Nevada

Saturday, November 7
Denton, Texas

Tuesday, November 10
East Lansing, Michigan
Denver, Colorado
Zürich, Switzerland

 

 

 

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