Sky Christopherson – Self Quantification and Performance from Gary Wolf on Vimeo.

Stan James – Project Life Slice from Gary Wolf on Vimeo.

Doug Kanter is a designer, photographer and a student in the Interactive Telecommunications Program (ITP) at NYU. He’s also a Type-1 diabetic who has a keen interest in applying actionable design and interaction schemes to the data he gathers from his monitoring systems.

It is time to re-imagine the entire user experience of being a patient with diabetes. There is tremendous potential in applying information technology, creative design and research into behavior change into a comprehensive product for patients. Technology-based solutions are increasingly important resources in these times of skyrocketing treatment costs and lmited doctor availability.

Doug has been using his skills to better visualize and understand his own data, particularly his continuous blood glucose monitor. His first project, 7729, explored one month of his continuous blood glucose monitoring – the 7729 readings to be exact.

His second project expanded on the 7729 project to include not only his blood glucose monitoring, but also the insulin he was receiving. Insulin on Board, is based on 100 days of data collection and includes 820 insulin pump reading and 25,012 blood glucose reading. By coordinating these two data sets he was able to look for patterns and identify the efficacy of his insulin dosing.

The goal of Insulin on Board was to better understand the relationship between the insulin I take and the resulting blood sugar readings. It visualizes not simply when I take a dose of insulin, but when that insulin “kicks in.” Because insulin has a latency, it is helpful to see it actually has an effect on blood sugar. Often times I’ll take two or more doses of insulin within a few hours. Insulin on Board calculates the sum overlapping effect of these dosages.

I think patients like me could benefit massively from having improved visualizations that give you both a solid overview of how you are doing but also allow you to dial down into the details if you want.

Being a student and designer, Doug has done a great job explaining the process he takes for developing these visualizations. If you’re interesting in learning more about how he created these visualizations, what he learned, and future work you can follow along at Databetic and his blog.

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.

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

Hobeika: Butterfleye is a heart rate monitor for swimmers:  a waterproof module that can be mounted on all types of swimming goggles and that visually displays the athlete’s heart rate in real-time. Butterfleye has an integrated light sensor that measures the heart rate by reflection from the temporal artery (a ramification of the carotid artery that runs through the neck), and a 3 color LED that reflects indirectly into the goggle lens indicating the status relative to the target: green if the swimmer is on target, red if above target and yellow if below target.

Butterfleye is still in the prototyping stage, I am currently working on iterating the design to get to a market product.

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

Hobeika: I used to be a professional swimmer during my school and university years, and all of the trainings were based on the heart rate measurement. As a matter of fact, in all professional trainings, there are 3 main target zones that are dependent on a percentage of the maximum heart rate, and that lead to different results from the workout: the swimmers try to stay between 50-70% of their maximum heart rate for fat burning, 70-85% for fitness improvement, and 85-95% for maximum performance. In every single workout, the coach used to combine different sets of each of the zones to make sure the swimmer gets a complete workout and works on different aspects of his body. The problem was that there was no effective way of actually measuring heart rate during the practice! What we did is count the pulse manually after each race. Other options would have been to wear the watch + belt or use a finger oximeter, but both of these were very impractical for a swimmer.

I built the first prototype during the ‘Stars of Science’ competition, which is kind of like the Arab version of the ‘American Inventor’ initiated by Qatar Foundation. Following a Pan-Arab recruitment campaign, I was one of the 16 candidates to get selected among 7,000 initial applicants to go to Doha for the competition. Once I got to the Qatar Science and Technology Park, I was able to combine my passion for swimming and my background as a mechanical engineer, along with the experts and the resources available in Education City to build the first concrete version of my idea. After four long months, I won the third prize, and got a valuable cash award that I used to file for a US patent, start a joint stock company in Lebanon, and hire an electronics engineer and an industrial designer to get started on the prototyping process.

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

Hobeika: The product is not on the market yet, so the reactions I have been getting so far are from swimmers and athletes hearing about the idea or testing the first prototype.

Swimmers I have talked to have commonly agreed that there is a very big lack of monitoring tools for practice in the water, and that Butterfleye would be filling a very big gap. As for people who have tested it, they are surprised of how lightweight it is and how they don’t feel it when wearing it in the water.

Here is my assumption on the impact Butterfleye will have: Swimming is a very solitary sport, and it is very difficult for athletes to get feedback on the performance if swimming without a coach or a team. It is the main reason why most people prefer practicing another activity. Having a practical monitor that can not only measure the heart rate but give all kind of information a swimmer would want to know (such as lap counting, stroke counting, speed, distance, etc.) will encourage more people to practice this complete sport and change its status of ‘solitary’.

Q: What makes it different, sets it apart?

Hobeika: Butterfleye is innovative when it comes to its sensor design: it is the first heart monitoring tool that doesn’t require wearing a chest belt, a finger clip or an ear clip, elements that would add a lot of drag in the water, and that would be cumbersome for the swimmer. Butterfleye’s sensor is integrated in the module itself, and measure the heart rate from the temporal artery.

Butterfleye’s design is also one of its competitive advantage: it is specifically designed for swimmers. It is waterproof, modular- it can be mounted on any type of goggles, light-weight and in the shape of a waterdrop in order to minimize the drag. It is also flat so it doesn’t interfere with the swimming motion. It is designed to be perfectly compatible with the biomechanics and the dynamics of swimming.

Butterfleye also stands apart by comprising a waterproof heads-up display, where the swimmer can visualize his target zone on his lens. This way, the swimmer would not have to interrupt the motion of his arms (as he would do if he was wearing a watch), and could visualize the heart rate in real-time, compared to using a pulse ox right after the race.

Swimming technology, unlike all of the other sports, is widely unexplored to date, especially when it comes to monitoring and self tracking devices. Butterfleye is one of the first tools to tackle this market gap.

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

Hobeika: My next target is to release a first version of the waterproof heart rate monitor in the market. After that, comes a series of other monitoring products for the swimmers, so they would be able to track calories, strokes, lap count, etc.

I am also planning on expanding this platform technology to models compatible with running, skiing, biking and diving.

Q: Anything else you’d like to say?

Hobeika: I participated in ‘Stars of Science’ when I was still a university student, and after winning the third prize I got a job at a renowned Lebanese engineering design firm. I was very scared of working full time on my project and giving up the sense of security I had, and was only able to do it a year down the line.

The entrepreneurship ecosystem is still very nascent in Lebanon and in the Middle East, and I am part of the first generation that is working on a hardware startup in the region. It is very challenging, simply because there aren’t many (or any) resources available. I have to ship and prototype everything abroad, which makes the entire process more lengthy and expensive.

However, I am also part of that generation who will, through our projects, develop and nurture the right resources to make it easier for the next crazy change makers! I am already working on a website An Entrepreneur in Beirut, which is a platform for all the resources needed for hardware development in Lebanon.

Product: Butterfleye
Website: www.butterfleyeproject.com
Price: tbd

This is the 16th 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.

Judy Kay is Professor of Computer Science at the University of Sydney. Her research is in technologies for human computer interaction, supporting personalisation, pervasive and mobile interaction.

Bob Kummerfeld is an Associate Professor of Computer Science in the School of Information Technologies at the University of Sydney. Bob carries out research into system support for pervasive user models.

People’s long term, important goals are drivers for using personal informatics tools. For example, if a person’s goal is achieve and maintain good health, this is a driver to capture data such as blood pressure, exercise, activity, sleep and food eaten. Personal informatics tools aim to make it easy for people to capture such information and so that it is available for self-monitoring, so people can see how they are progressing towards their goals. It can also help people decide how to alter their behaviour and then to see if this helps them achieve their goals.

Our research aims to create a personal informatics framework for lifelong goals, by enabling people to have a new form of flexibility and control to:

1. set relevant and realistic personal goals;
2. link these flexibly to tools that capture relevant personal data;
3. monitor their progress towards goals;
4. and manage the data over the long term (update, share, delete, archive).

As one might expect, given the importance of goal setting and tracking, there are many goal setting systems, such as HealthMonth, GoalsOnTrack, stickK. While these provide a variety of valuable support for goal setting, they lack support for 2 and 4 above. We aim to address the broad challenges of enabling people to flexibly manage and control their data associated with their long term important goals.

User control over personal data during goal setting:

To help people think about the personal data that will be useful for achieving their goals, we are exploring a rich representation of goals. This should enable people to think more effectively about their goals and the kinds of personal data that could be useful. We draw on theories such as Goal-setting Theory and Social Cognitive Theory which point to the importance of aspects such as specificity, importance and difficulty of the goal, deadlines and feedback about the goal, commitment and self efficacy about being about to complete the goal. So we aim to help people think about these aspects. We explain each of these at the goal setting interface. We suggest personalised default values, and explain the reasons for those recommendations, and allow users to set their own values if they wish.

User control over personal data while linking devices to goals:

Social cognitive theory also indicates that if a person is aware of their potential resources (e.g. monitoring tools, social support) towards achieving goals, they gain insight about their own capabilities. In our system, for example, if a person acquires a step counter, they are advised to set an initial goal of using it to get a baseline, by tracking daily steps walked each day over a week. Suppose this indicates they walk an average 5,000 steps a day. Our system recommends an initial goal of 6,000 steps a day for the next week, explaining that while it is well below the recommended 10,000, it is more likely to be attainable from this person’s baseline. Thus our framework both recommends goals that are likely to be achievable and explains the reasons for the recommendation.

Personal informatics now has many different tools for monitoring health and activity. Users can choose different tools for monitoring different goals. This can create a problem which we call ‘scattered subgoals’. For example, maintaining wellbeing includes several subgoals such as “Walking 10000 steps a day”, “Do at least 30 minutes moderate activity per day”, or “Avoid more than 30 minutes of sitting in front of computer”. Users might use step counters such as Fitbit for monitoring a step goal, mobile applications for logging minutes of activity, or notifiers to remind them if they are in static posture for more than 30 minutes. In most cases, they have to visit different web sites to monitor different goals. This makes it hard to monitor goals. Available goal setting systems have not addressed this issue so far.

Our vision is to make it much more easier for people to monitor their diverse goals because our system enables them to aggregate their personal data for all their goals, extracting it from different systems and keeping it in a single store that the individual controls. Since more and more APIs are becoming available for developing mashups for personal health informatics, we can readily extract such information. The challenge still remains to ensure the person can control this aggregation and then manage the information effectively so that it serves their goals.

User access to aggregated information for goal monitoring:

An important part of our work is to enable people to see several goals together and to log salient notes about them. The example in Figure 1 shows a hypothetical user monitoring three goals:
walking 10k steps/day goal (green graph),
having 5 periods of intense activity per week (red dots)
at least 60 minutes moderate activity daily (blue graph).

Figure 1: Visualisation for goal monitoring

The figure illustrates the user noting a quiz that interfered with achieving the goals (just as they noted that they were sick in the previous week). Theories of metacognition indicate the importance of enabling people to for log such salient life events to explain the progress achieved and make sense of long term information and trends.

User control over managing personal data:

Finally, existing systems lack support for people to manage the lifelong personal information. We have identified several important levels of control:
determining which information can be shared with others;
easy ways to remove information, for example when sensor data is wrong (such as when they allowed someone else to use their step counter);
transforming the information into compacted forms, for example, reducing fine-grained sensor data into higher level information about goals, so reducing the amount of information kept, reducing the risk to privacy it creates.

To achieve user control over goal related data, we will design and evaluate interfaces for managing goals and reflection over long term by defining goals; monitoring the social and cognitive information associated with each goal; and reviewing goals. These will enable users to connect sensors and choose the type and frequency of feedback, including e-mail, tweets, desktop notification and ambient displays. The driving design goal of our framework is to ensure user control of personal data.

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!

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.

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.

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.