Creating Addictive Technology, Live Muscle Testing, and more conference fun

Posted on May 17, 2012 by Alexandra Carmichael

We’re excited to announce another new batch of sessions at the upcoming QS conference. Thanks to everyone who is stepping up to speak! The full roster of show&tell talks and breakout sessions so far is listed here.

Check out these awesome new topics:

Breakout Conversations

Is QS science?  The role of QS in scientific discovery (Daniel Gartenberg)
Creating Addictive Technology (Nir Eyal)
A Memex for the Quantified Self (Betsy Masiello and Jess Hemerly)
How to evangelize QS to the mainstream (Phillip Thomas)
Privacy Issues (Jodi Schneider)
The Health Optimized Patient (Mike Gerstenfeld)
Health as a Team Sport (Mei Lin Fung)
Exploring the Quantified Us (David Fetherstonhaugh)
Muscle testing – live experimentation! (Alex Grey)

All sessions are defined by attendees in advance of the conference, like a curated unconference. There will be overlapping breakout sessions, show&tell talks, and posters for you to choose from. We will keep posting them here as the date approaches.

If you’d like to join us, you can register here. 400 out of 600 tickets are already taken. And if you have a personal self-tracking story to share or would like to lead a breakout discussion, please let us know!

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Personal Informatics in Practice: Deep Personalization

Posted on May 15, 2012 by Ian Li

Bon Adriel Aseniero is currently a computer science undergraduate researcher at the University of Calgary under the supervision of Dr. Sheelagh Carpendale and Dr. Anthony Tang. He has an interest in Art and Aesthetic Design, while his research is mainly in Personal Informatics and Visual Analytics.

I have used some applications in my phone that keep track of my activities. Most of them do a good job in their own right; however, they always seem to come out short –no single application tracks my activities in the way I really want it to be tracked, and the feedback is almost always some graphs which are either unappealing or doesn’t give room for self-discovery. I can’t play with my data.

From the above anecdote, we can agree that users of personal informatics tools are not just members of a generalized population but also individuals. As such, they have their own goals and reasons on why they use the tools, and use a variety of reflection methods, some of which may be unique to the individual. While it is true that these goals and reflection methods may be similar enough that they can be addressed by a generalized one-size-fits-all type of personal informatics tool, but I just can’t let go of the fact that some of their needs may not be met fully. Moreover, the feedback mechanism lacks participation from the individual –what you see is what you get (WYSIWYG); there is little room for an individual to experiment on his or her data to answer questions beginning with “why” or “what if”.

So if Personal Informatics is all about Personal Data, why not make the tools for reflection personalized as well?

As a possible way of supporting the above question, I propose Deep Personalization which is the process of allowing individuals to create, or to customize to a certain extent visualizations that represent and or integrate their data. In addition to the ability to have more meaningful visualizations as a result, I argue that the process of tailoring and customizing different visualizations as an activity that in of itself provides considerable insight to individuals.

This idea stems from the time when I created three different visualizations of different aspects of my life which I found interesting, and their integration. The first visualization is Activity River, which shows a stream representing my activities throughout a day. The second visualization is D’Ripples or Directional Ripples, which shows ripples representing the directions I’ve looked at through the day and the things I see in those directions. Lastly, Place Well is a visualization of the places I went to in a day. Integrating all of these visualizations is Hours, in which I took the visual aspects I deemed important in the previous three visualizations and combined them into a new interactive visualization. The design process of each visualizations required several sketches which provided me with a wealth of insight that is generally not accounted for by pre-created visualizations. Not only did it ensure that the resulting visualization visualizes my data correctly, but it also allowed me to find personally meaningful representations of my data. Furthermore, being able to participate in the feedback mechanism allowed me to uncover correlations that I may not have seen with current WYSIWYG feedback tools. It is almost like when we learn new things e.g. cooking; it is better to actually try to perform or participate in the act of cooking rather than to just look at someone else do it.

However, even though the rewards of Deep Personalization may prove really beneficial to the individual, it faces a big challenge. Much like cooking, not everyone who tries to do it on their own actually ends up cooking something great, some fails at cooking while some excels. Creating visualizations is not a trivial task. Some questions we as a community should try to address could be “to what extent should the individual be able to customize the visualizations or any other tools for reflection?”, “What type of tool should we provide for Deep Personalization? A tool as extensively freehand as Photoshop, or a more restrictive tool that gives the individual a set of building blocks to play with?” Nevertheless, there is a philosophical benefit that can rise from Deep Personalization and it all lies in finding an effective method for providing its support in our current Personal Informatics tools.

 

This article is a summary of a position paper by Bon Adriel Aseniero and his colleagues that was discussed at the Personal Informatics in Practice workshop at CHI 2012 in Austin, TX on May 6, 2012. The workshop was a gathering of researchers, designers, and practitioners exploring how to better support personal informatics in people’s everyday lives.

 

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New York QS Show&Tell #16 Recap

Posted on May 14, 2012 by Steven Dean

Last week we had over a 100 folks attend our 16th NY Show&Tell with a Demo Hour held on Tuesday, May 8th at an incredible space that was generously provided by Digitas, a digital brand agency that has watched and supported QS closely over the years.

Thank you to my co-organizers, Ben Ahrens and Brian Gallegos, who helped pull together this recap for the blog.

DIGITAS LABS DEMO HOUR

Digitas Labs and Ben Ahrens assembled a fascinating group of QS members to share their stories, innovations and experiences in our first demo hour that had a real science fair feel. Some of the demos ran on some awesome touch screen devices provided by Digitas Labs. It started at 6pm with the following demonstrations:


Zack Freedman demos Optigon

Sandy Santra gave a lively demonstration of a truly unique DIY self-tracking system built for the iPad that not only charts psychological changes and their effects, but also provides users with full editorial control over data fields and allows them to customize their own personal experiments.

Kat Houghton, founder of ilumivu, displayed a wearable emotional state detector designed to empower people with the ability to tap into their own behavior and the behavioral responses of children with autism to help facilitate positive health and lifestyle changes.

Folks gathered in the Innovation Room as Alex Smith demonstrated his software called “Timebinder” which he designed to create visual timelines out of timestamped data — particularly useful for bringing asynchronous time series data from multiple sources into a single view for analysis.

Craig Dunuloff took spectators through a virtual blast into the past with his app Rewind.me. Where was that restaurant? How may friends were there? What did the gang do last night? This app allows users to get more value out of what they’ve done in their lives by aggregating data from other services such as Facebook, Foursquare, Tripit, Runkeeper, and more. It also lets you see and compare your activities to those of your friends and the world at large.

Amelia Rocchi gave QS members a behind the scenes look at InsideTracker – a web-based service that helps individuals optimize their overall health and performance by giving them a unique view into their personal biochemistry.

Christian Monterroza unveiled his time-tracking project that uses geo-fencing to passively track and organize daily activity. One of the most fascinating and helpful aspects of Christian’s app is that it allows the user to easily and personally allocate different regions of spaces for different activities, i.e., the park is for running; the freeway for driving; the living room for sitting; the grocery store for food shopping, etc. The app then takes over and auto-logs the activities based on its users geography. Fully customizable – NO LOGGING REQUIRED!

Zack Freedman (@ZackFreedman) was quick to draw a crowd with “Optigon” – a wearable wireless cyborg system that integrates with the user’s smartphone allowing him or her to access all data and keep it in plain site – even view nearby mobile user’s text messages, or as Zack puts it, “read people’s minds”! This awesome demo was every bit as impressive as it looked. Zack is currently seeking partners and investment to turn his devious device into the Arduino of wearables.

SHOW&TELL TALKS

Following the demo hour, we had four inspiring talks from QS members of the NY community.

How analytics improved my personal life and helped a losing soccer team

Stefan Heeke has a background in analytics and wanted to start using this skill for three self-improvement projects.

The first project was measuring his physical health. He was using the Fitbit to track his activity. He discovered that it takes some time at the beginning but then eventually he discovered what works for him. Specifically, he identified three areas: don’t eat fried food, cut out snacks, and cut out alcohol.

The next project was a daily journal. He decided to write down numbers to better understand how he feels each day. He found that he could gather some very actionable data by correlating the right metrics with each other. His approach is to identify both a positive and negative correlation to the activity. For example, he would correlate stress, whether he had a successful day, or general feelings of satisfaction. He also tracked his commuting time. He wanted to figure out how his daily commute impacts his mood. He found that as his personal time available decreased, his food quality decreased and his television time increased. Overall, he found that a) social days are good days, b) proximity to work is important, c) stuff in general has no impact, and d) TV is a time killer.

The third thing he tracked is how to apply personal metrics to a soccer team. He tried to model the most probable outcomes for certain soccer scenarios in terms of likelihood of success. As a result of the tracking, the team made it to the finals of the soccer league.

Ultimately, Stefan learned that whenever you apply data, it has a transformative impact and if you want to improve your life, data can help. He was also surprised at the number of distractions he ran into and how much that had an impact on his life.

Quantifying Diabetes

Jana Beck started her self-tracking journey with the goal of better understanding the impact of her diabetes. She was diagnosed with type 1 diabetes at age 19 and has been dependent on synthetic insulin for survival since. Her problem is that dosing insulin is not easy and is not a one-size-fits-all thing. It requires a lot of adjustment and impacts people differently. She set out to better understand her diabetes and better optimize her glucose management.

She started using a continuous glucose monitor on the back of her arm last year. This device transmits blood readings every 5 minutes and she gets trend and rate of change information. She has a target goal of keeping her readings between 70 and 130 mg/DL.

Her first experience was shock and her next was frustration. She found it hard to change her patterns. So she developed a hypothesis and set out to test it. Her hypothesis is that she needs to restructure her carbohydrate intake. The first step was to read a book on the topic (Good Calories / Bad Calories by Gary Taubes). The next was to use her monitoring device to track how her glucose changed based on her changes in carbohydrate intake. Her conclusion is that a low carbohydrate diet had a significant impact on her readings.

To run her analytics, Jana built her own statistical analysis program using R that tracks daily percentages over time for each type of blood sugar reading (carb-restricted vs. regular diet) against a target. Her program is called iPancreas and is available on Github.

Her next step is to try and start pulling in other variables (exercise, mood, etc.) to see how this changes her patterns. Ultimately, Jana’s self tracking project taught her how to best eat so that she can control her diabetes.

Walk all of Manhattan

Alastair Tse recently moved to NYC six months ago to work at Google. He hadn’t spent much time in NYC previous to moving here and wanted to better discover his new home city. Each day he commutes from 27th St. to 14th St. in Manhattan. One day he was trying to figure out the optimal route to work and wondered how many patterns are there to get from point A (home) to point B (work). He further extrapolated on this idea to see if it’s possible to walk all of Manhattan, and track it.

He started by writing down his walking experience in a notebook and just using general Google Maps. This turned out to be a bad idea because it wasn’t scalable and Google Maps can be buggy. So he built his own mapping app that uses Google Maps but allows him to map his own routes. The app tracks the streets he goes down and allows him to edit each route. It then tracks the routes he takes and shows his walking history.

He learned that it was possible to track something like where in a city a person walks and it’s very useful. In fact, he found that he hadn’t walked one square block north and one square block south of his apartment, much to his surprise. It got him to wonder, what other areas of the city is he often near, but never explored. The app helped Alastair adjust to living in a brand new city and has given him some ideas for places he wants to eventually explore.

How visualizing health problems could help solve medical mysteries

Katie McCurdy is an interaction designer with Myasthenia gravis, an auto-immune disease that causes muscle weakness in voluntary muscles She’s had it for 20 years and has been taking a drug to help the disease. She decided to take an alternate route and consult a holistic doctor. This was a new doctor so she was very motivated to make sure this new doctor understood her entire 20 year history with the disease. So she decided to make a timeline, from memory. She drew a timeline that included when she was feeling good and when she was feeling bad. She annotated the timeline for when she took certain drugs.


Initially, this was all drawn by hand. But as she worked on it, she decided to digitize it. So she next built the timeline into Adobe Illustrator so the graphs can be more accurately represented.

But it wasn’t enough to see all of her mood timelines separately. She wanted to overlay them so when symptoms go up and down, she can see how they are associated with each other.

Two variables she tracked were gut feelings (physical) and voice strength. These are two areas in which the muscle constriction has a high and very noticeable impact. This experience has helped her tell her story in a structured and coherent way and for that reason, this entire project has been helpful.

She learned that antibiotics were probably making her sicker, that docs are busy and probably skeptical of yet another patient created graph, that better health visualizations can be a great storytelling tool, and that memories are data too. Ultimately, she ended up being inspired and is currently doing more focused tracking in other areas of her life.

See our interview with Katie in an earlier QS post here.

Thanks to everyone who came out. We’ll get the videos up soon. See you this summer at the next NY QS Show&Tell.

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Beijing Quantified Self?

Posted on May 11, 2012 by Seth Roberts

I recently had lunch with Richard Sprague, an engineer at Microsoft Beijing. He raised the possibility of starting a Quantified Self Meetup group in Beijing. The meetings could be held in one of Microsoft’s two brand new buildings, which are in the exact center of Zhongguancun. If you might attend, please let me know (e.g., by commenting on this post).

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Numbers From Around the Web: Round 7

Posted on May 10, 2012 by Ernesto Ramirez

Where are you? A pretty easy question to answer. But, what about, “Where was I?” Not so easy to answer, especially when we start talking about periods of time more than a few days or weeks. Sure, we all have GPS running on our phones now. We can check in with Foursquare/Facebook/Path etc. to keep a log of locations, but that data is fragmented and only represents certain specific locations. What about paths? What would we learn if we knew more about how we traveled about our world?

Aaron Pareki is one of the founders of Geoloqi, a location-based services platform. He has also been tracking his location every 6 seconds for the last four years and he has created some amazing visualizations to better understand his movement:

You may think this is just a boring old map with some travel data layered on top, but what makes this map special is that there is no underlying geospatial data. The lines you see above are Aaron’s actual travel paths from his GPS data. Using this information you can easily see the well traveled roadways by finding the thicker lines. You can even quickly pick out freeways and interstates due to their high speed.

Here you see Aaron’s data for the last four years (again, there are only the GPS traces). You can see he’s color-coded the data ro represent different years in order to see where he spends his time.

Aaron has a lot more visualizations of his GPS traces, but I’ll leave you with this neat video showing a timelapse of his minute-by-minute movement:

Every few weeks be on the lookout for new posts profiling interesting individuals and their data. If you have an interesting story or link to share leave a comment or contact the author here.

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Toolmaker Talk: Michael Forrest (Happiness)

Posted on May 9, 2012 by Rajiv Mehta

In talking with many toolmakers, I find myself constantly surprised by how different people approach the same, and seemingly simple, issue with very different perspectives. A few months ago I wrote about Mood Panda which went from private to community. In contrast, Michael Forrest’s Happiness has evolved from shared to private. I also find Michael’s experimentation with the look of his app both beautiful and fascinating.

Q: How do you describe Happiness? What is it?

Forrest: Happiness is an iOS mood tracking app. You get randomized reminders to record your mood, and then can view this data graphically and as a journal. The idea is that by using this app, you’ll be able to make better decisions in your life.

Q: What’s the back story? What led to it?

Forrest: I’ve always been inspired by technology’s potential to solve old problems in new ways. I was looking for novel ways to solve mental health problems without resorting to pharmaceutical hacks like antidepressants. I came across Daniel Gilbert’s TED talk “Why Are We Happy?” and read his book where he talks about the marked differences between what we think will make us happy versus what will actually make us happy.. My idea was that even if we can’t make good predictions about how we’ll feel in the future, we can at least start gathering accurate data about our past and use that to reflect on the present moment. I first built a Facebook app, and then moved to the iPhone.

Q: What impact has it had? What have you heard from users?

Forrest: I’ve sold a few copies without doing a great deal of marketing – people seem to discover it on their own. The feedback I have had has been amazing – when it helps people, it is helping them with a fundamental aspect of their life so it didn’t seem beyond the bounds of reason when one user told me it was the ‘single best reason for owning an iPhone’. I have seen an increase in uptake since I put this page together http://goodtohear.co.uk/happiness – people are finally starting to see the point of it and I’ve been getting useful feedback about details of the UI and so on. I’m still really only starting out though.

Q: What makes it different, sets it apart?

Forrest: I know my app isn’t the only way to track your mood, but I want it to be the best way to do so. A lot of decisions have gone into this seemingly simple app.

Single focus: I have deliberately avoided trying to track any other information because happiness has an infinite variety of possible influences that I would never presume to be able to predict for any particular user.

Design: It was important to me that I give the app a personality of its own. Finding a look that wouldn’t interfere with the user’s mood (or annoy them) but still had some personality was not trivial. Initially I drew from artists like Kandinsky and Miro (see here) for the style but over time realised that a journal was a more appropriate look. I have avoided smiley faces in the latest and came up with a very tactile way to report mood from a blank canvas – I don’t want the app to influence the user’s mood in any way at the reporting stage by suggesting anything (but it should still look good!).

Exploration: The charts in Happiness have evolved a lot over time. My original designs were largely tag cloud based. As I personally accumulated entries (I have over 700 reports in my database!) I realised that time-based reporting would become increasingly important. After a lot of trial and error I settled on a monthly reporting cycle. I also made the graphs simple by moving away from multicoloured heatmaps to simple areas filled with red or green. The algorithms used to calculate these areas need to be complex enough to find patterns but self-evident enough that when users look at the reports these seem to match their input. Details of the reports give the tool different usage styles. Simply by numbering my ranked taggings I’ve now started setting myself challenges (e.g. move “Music” from #2 in my life to #1!). There’s also something interesting about getting a blank slate each month to see if you can do better than last month.

Price: Happiness isn’t a free app, and this is a conscious decision. I want users to feel invested immediately since you don’t get instant gratification. The price will always stay around this level while I continue to add value to the app in a multitude of ways.

Privacy: A big benefit of making this app as a native iPhone app is that the data can be stored locally. I want users to feel they can be 100% honest when writing in their diary. There’s even a passcode lock feature to make sure people definitely can’t get in, even if your phone is unlocked.

Q: What are you doing next? How do you see Happiness evolving?

Forrest: Soon I’ll be releasing an iPad version of the app that will sync data via iCloud, and enable larger, more in-depth views of the data. I’ve done some fun experiments around bringing in information and media from users’ social networks which really helps contextualise the more private comments. I like the idea of people being able to share their mood maps as artworks so I have some ideas around this – making this possible without necessarily revealing details to the world.

Q: Anything else you’d like to say?

Forrest: I’m working as a one-man-team on this project. I love that it’s possible to achieve so much on my own but I’d also prefer to be working more collaboratively. I’m looking into clinical trials, and enabling others to build their own visualizations. Happiness is such a fertile subject that I’ve barely scratched the surface of what is possible with this tool. So if anybody feels inspired by what I’ve done so far and can see opportunities to work together, get in touch.

Product: Happiness
Website: http://goodtohear.co.uk/happiness
Platform: iOS
Price: $1.99 / £1.49

This is the 15th post in the “Toolmaker Talks” series. The QS blog features intrepid self-quantifiers and their stories: what did they do? how did they do it? and what have they learned?  In Toolmaker Talks we hear from QS enablers, those observing this QS activity and developing self-quantifying tools: what needs have they observed? what tools have they developed in response? and what have they learned from users’ experiences? If you are a “toolmaker” and want to participate in this series, contact Rajiv Mehta at rajivzume@gmail.com.

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Understanding Self-Efficacy and the Design of Personal Informatics Tools

Posted on May 8, 2012 by Ian Li

Adrienne Andrew is a PhD candidate in Computer Science at the University of Washington. She is interested in studying how people use food diaries on mobile phones: what challenges “typically motivated” users have, balance between capturing less detailed yet still valuable food information, and identifying new ways to organize food databases to support a wider range of dietary analysis.

One primary concern for the field of personal informatics (PI) is supporting people in making changes in their life. A driving theory for pesonal informatics (PI) designers and researchers is Social Cognitive Theory [Bandura, 1977], which posits that a person’s behavior, environment and inner qualities all contribute to how a person functions. This theory has been applied to understanding how people learn, how social environments impact what people do, and how people regulate their own behavior. A key component in this theory is self-efficacy (SE), which is summarized as a belief in one’s abilities.

The question I pose to both PI researcher and self-quantifiers is if your experiences support whether self-efficacy truly reflects intent and ability to engage in key behavior change strategies.

Measuring Self-Efficacy

SE is traditionally measured by self-report. To develop SE measurements for a particular domain, researchers use open-ended approaches to identify common challenges and barriers to the problem. They then develop a series of statements of the form “How confident are you that you can [achieve goal] even though [challenge]?” with a 4-unit response scale ranging from “Cannot do it” to “Highly certain can do”. An example of a statement is “How confident are you that you can stick to a healthy eating plan after a long, tiring day at work?” SE measures provides valuable feedback about whether an intervention is supporting adherence to behavior change strategies, and indicate whether participants complete the study with an intention to continue.
This is an important feature for PI researchers: we are familiar with a domain and common challenges, so can build the scales easily; we usually use short-term studies to indicate long-term impact; and properly designed scales can help us to discover where a PI tool breaks down.

Self-Efficacy Influencers

Now that we have described how SE can be measured and its relevance to PI researchers, it is important to acknowledge factors that may impact the measurements as applicable to PI tools. In addition to basic usability (which I would also argue is more important to the “common consumer” as opposed to highly-motivated quantified-selfers), a user’s goals (internal motivation) and trust in the technology are key.

How well the tool matches the user’s goals.

This is a point that is likely more important to researchers than to quantified-selfers. It refers to both a goal the user has and that the user has a belief in what they need to do in order to attain that goal. A user who is trying to lose weight may choose to focus on restricting caloric intake as well as increasing caloric expenditure, or choose to focus on only one of those areas. Social cognitive theory says these beliefs are based on what the user has observed amongst their peers, and how similar or different the user is from their peers.

We observed this in the BALANCE studies. BALANCE consisted of a food diary to capture caloric intake, an automatic physical activity detection platform to measure caloric expenditure, and a visualization that provided real-time feedback of the person’s caloric intake/expenditure balance throughout the day, all on a mobile phone. Overall, about 40 people participated in the evaluation by carrying the phone and tracked their food intake for 3 days.

One recurring theme in the feedback was that tracking food intake with such detail was too much work, and would only be worth it if they had a medical condition that made it very important to keep detailed records. However, some participants wanted to reflect on a coarser grained summary of their dietary intake for general health and disease prevention. There participants had a different wellness goal, and therefore didn’t have the internal motivation to make this tool useful to them.

Understanding the underlying technology.

Another factor is how well the user understands the technology, or more specifically, how the technology may fail. Part of the BALANCE project was using sensors to identify and calculate calories expended via activity throughout an entire day. Other related tools are GPS-based run trackers that use GPS to track the location, duration and other metrics of the run. Technologies that use sensors to identify bouts of physical activity have some level of uncertainty associated with the recognition. This uncertainty comes from a variety of sources, such as parameters that reflect a tradeoff between power consumption and accuracy. GPS trace quality depends on terrain and location of satellites in the sky.

A recent New York Times article reflects the concern of GPS run tracker users. Runners sometimes measure certified race courses, and report discrepancies to the organizers. These runners appear to trust the technology more than the organization. In the case of BALANCE (which exposes less detailed data about the calorie calculation and depends on more parameters), some users reported a feeling that the calculation “didn’t feel right”, but were unable to express how they thought it might be wrong. With both of these examples, the uncertainty with the technology could impact measures of SE. This raises the question of what other factors influence a person’s trust in the technology, as well as how SE may be impacted, and how it may vary from person to person.

So, I pose this question to quantified selfers: What do you track, and what aspects of the tools you use impact whether or not you can or will keep using them?

 

This article is a summary of a position paper by Chloe Fan and her colleagues that will be discussed at the Personal Informatics in Practice workshop at CHI 2012 in Austin, TX on May 6, 2012. The workshop will be a gathering of researchers, designers, and practitioners exploring how to better support personal informatics in people’s everyday lives.

 

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10 New QS Conference Sessions by Awesome Attendees

Posted on May 8, 2012 by Alexandra Carmichael

We’re excited to announce another new batch of sessions at the upcoming QS conference. Thanks to everyone who is stepping up to speak! The full roster of show&tell talks and breakout sessions so far is listed here.

Check out these awesome new topics:

Breakout Conversations

How to separate user feedback signal from noise and incorporate into product development (Nick Gammell)
Breath tracking – how and why (Danielle Roberts)
Using smartphone and behavioral data with ginger.io (Michael Nagle)
How to start and run a QS Show&Tell meetup group (Adam Butterfield)
Using software to exercise your brain and grow focus (Dave Asprey)
Time management design (Michael Kotas)
Psychological and social-cultural consequences of QS going forward (Yuri van Geest)
Manly Dieting (John LaPuma)
Sleep and consciousness (Marcin Kowrygo)
The brain and self-quantification, a bidirectional relationship (Matt Keener)

All sessions are defined by attendees in advance of the conference, like a curated unconference. There will be overlapping breakout sessions, show&tell talks, and posters for you to choose from. We will keep posting them here as the date approaches.

If you’d like to join us, you can register here. 400 out of 600 tickets are already taken. And if you have a personal self-tracking story to share or would like to lead a breakout discussion, please let us know!

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Adding Smart Gestures to Everyday Objects and the Human Body

Posted on May 6, 2012 by Alexandra Carmichael

Imagine if you could switch your music track while running just by tapping on your hand or your arm. What if your TV and lights knew when you had fallen asleep and automatically turned off. Or if doorknobs were as smart as your current tablet touchscreen and you could send messages to people before they knock. Anything becomes a programmable sensor. This video, presented at the CHI conference going on now in Austin, completely blew me away this morning. You have to watch this!

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Sarah Lewington and Michelle Hughes on Empathic Design

Posted on May 5, 2012 by Alexandra Carmichael

Sarah Lewington and Michelle Hughes study and teach fashion communication at Nottingham Trent University. In the 5-minute Ignite talk below, they talk about designing with empathy for a project they’re doing with Unilever, with more questions than answers, such as: what is the relative importance of data and functionality vs. emotional attachment to a device? What do you think? (Filmed at the QS Europe conference in Amsterdam.)

The impact of self-tracking on empathic design and market research by Sarah Lewington & Michelle Huges from Quantified Self Amsterdam on Vimeo.

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