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Toolmaker Talk: Eric Gradman (Facelogger)

This is the eight 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?

For me, some of the most interesting QS talks have been by those creatively repurposing existing sensor technologies for novel self-tracking applications — such as Mikolaj Habryn’s Noisebridge, at an early QS meetup, and Hind Hobeika’s ButterflEye goggles, at the QS Amsterdam conference. It’s fascinating to hear what the inventors are thinking long before their product is in the market. Here, Eric Gradman, master hardware hacker, tells how he is applying his skills to a focused life-logging application.

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

Gradman: The Facelogger is a passive lifelogger that helps me remember every person I meet by creating flashcards of their face, name, where we met, and our conversation. Facelogger consists of an always-on videocamera necklace, a software suite to process the video, and a smartphone interface for reviewing the flashcards.

The camera is a commercially available Looxcie camera, which was modified with a prism so it hangs around the neck. This camera continuously captures activity, and has a button that allows you to save the preceding 30 seconds of footage (footage that’s not saved is automatically discarded). When I meet someone for the first time and they introduce themselves, I press the button. The camera preserves the previous 30 seconds of footage which hopefully includes a good video frame of the person, their name, and what they said about themselves.

When I next plug the camera into the computer, all the captured video clips are automatically uploaded to a server, and sent to Amazon Mechanical Turk. There, human beings identify the most representative faces from the video, determine their names, and even transcribe the conversation.

Facelogger gathers all the information and creates a Facecard, which can be reviewed later on a smartphone. A Facecard is like a flashcard, but it shows someone’s smiling face, their name, a map of where you met, a link to the video of the conversation, and often even a transcript of the introduction. I  can search the text of the Facecards, sort them chronologically, or by geographic proximity.

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

Gradman: Like any self-respecting geek, I’ve always tried to stay a technological step ahead of my peers and a technological leap ahead of my parents. But when I discovered that my parents use the same model smartphone I do, I realized I was beginning to lose my edge. To me, the next frontier for personal electronics is wearable technology, and the natural application is self quantification.

But what to quantify? As a hardware hacker and artist, my first foray into QS wearable technology was definitely more for entertainment purposes. Called the Narcisystem, it was a biosensor suit featuring sensors for pulse, heading, EEG, pedometry, and breath alcohol level. I used the output of these sensors to drive the lights, sounds, and ambiance at a party venue. Fun, but not really a form of human augmentation.

I have terrible trouble remembering the names and faces of people I meet. Its hard to say which is worse: my face-blindness or my memory for names. I’ll meet someone, shake their hand, and we’ll introduce ourselves. Moments later I realize with panic that I’ve already forgotten their name! And hours later, if they’ve changed clothes, altered their hair, or removed their glasses, I’ll blithely reintroduce myself like we’ve never met. At least I’m not shy!

I’ve always wanted to offload the mental burden of remembering people. When I was in school and I needed to remember something I used flashcards. Why couldn’t that technique work for people too?

Q: What impact has it had? What reactions have you had?

Gradman: Because the Facelogger is a first-stage prototype I am its only user. Has it helped me remember people I meet? You bet it has. I’m amazed by the quality of the Facecards and by how effective they are at jogging my memory. I get the general sense that reviewing Facecards a day or two after meeting someone gives me an opportunity to properly commit someone’s name and face to memory at my own pace … something I simply cannot do “on the fly” as we meet.

There’s another purely psychological effect: because I’m confident that my technology is taking care of remembering for me, I can relax into the conversation. I was never shy about saying “hi” to people before, but I did experience stress over the fact that I immediately forgot their name and face. Now with that interaction captured and searchable, I’m not bothered at all.

I’m sensitive to the ethical concerns with capturing someone on video without their consent. When asked what I’m wearing around my neck—and as you might expect, that happens a lot—I never lie. I explain that I’m wearing a video camera to help me remember people I meet. Invariably, I’m asked “is it recording me now?” I’ve been asked to turn it off, and I always comply. But a surprising number of people tell me they want their own Facelogger. It turns out there’s demand for a device to help remember people’s faces and names!

Some have questioned the legality of wearing a video camera. But there are already cameras trained on us wherever we go. You can buy a video camera hidden in a pen, or a pair of sunglasses. Will our social mores (or our laws) surrounding cameras trail so far behind the technology?

Very few have actually questioned the morality of wearing a video camera. In the age of pervasive social networking we’re living highly examined lives. For anyone with a camera on their mobile phone, its not such a stretch to imagine wearing the camera around their neck.

Also, I’m careful to remove the Facelogger when I’m not likely to meet new people:at home, in a business meeting, etc. I do this because the purpose of this device is not to have a record of every conversation I have.

Q: What makes it different, sets it apart?

Gradman: Life-logging is always something that fascinated me, but I felt that an ever growing cache of unsearchable video of my life would just be a huge burden. Facelogger is an experiment in constrained lifelogging. By only capturing moments that share a particular characteristic and have common features Facelogger allows for a well-defined process of data extraction and collation that address a specific shortcoming.

Gordon Bell, the pioneer of life-logging described his always-on MyLifeBits image recorder as “write-once, read-never.” For me, the decline in storage costs is not sufficient reason to record my entire life on video. Huge amounts of unprocessed video is just video I’ll have to review someday! That’s why I find it so easy to resist the temptation to press the “capture button” more often. Unless I have automatic tools to convert video into a compact searchable representation—in this case, a Facecard representing a person I’ve met—the video just isn’t worth saving.

There are other tools out there designed to help remember names and faces. Evernote recently released Hello, a mobile app to record people. What distinguishes Facelogger is it’s passive form of information capture.

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

Gradman: Currently, a Facecard only expresses information captured in the 30-second clip. But APIs for face identification are getting really good. Soon the Facelogger will dig through my social network, and connect a Facecard to the social profile of the person it represents.

Next I will passively capture my meals, and use Mechanical Turk to help catalog my meals.

Face logging and food logging are only two well-defined applications of life-logging. I intend to identify others, and make them available as software for anyone wearing a compatible life-logging rig.

Q: Anything else you’d like to say?

Gradman: Face-blindness and poor memory for names are widespread problems! I designed the Facelogger with my own shortcomings in mind, but I’m now examining how I can make these tools more widely available, perhaps as a subscription service.

If you’re interested in updates on this project, have ideas to improve the system, or want to be contacted when the Facelogger service is available for beta-testing, please join the mailing list.

Product: Facelogger
Website: http://www.gradman.com/facelogger
Platform: Currently, iOS.  Coming soon to any HTML5 enabled smartphone.
Price: not yet for sale; to be contacted for beta-testing, please join the mailing list

(If you are a “toolmaker” and want to participate in this series, contact Rajiv Mehta at rajivzume@gmail.com) 

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Toolmaker Talk: Sam Liang (Placeme)

This is the sixth 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?

Location tracking apps and geo-tagging are becoming ever more common, and self-trackers have been finding ways to mine the data. The QS Amsterdam meetup group has featured many interesting talks (see Victor van Doorn, Joost Plattel, and Willempje Vrins and Leonieke Verhoog). At the QS Conference in May, Naveen Selvadurai of Foursquare showed how “check-in” data could be analyzed to understand your life. Now, Sam Liang, CEO of Alohar Mobile, and previously architect of Google’s Location Server, wants to make collection and analysis of personal location data much easier.

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

Liang: Alohar Mobile’s PlaceMe application is a tool to automatically remember all the places I have been to. It generates statistics like when I went there, how much time I spent there, how often I go there, etc. It also classifies the places I visited based on their categories, such as gyms, restaurants, parks, etc.  It is available now for Android phones and soon will be available for iPhones.  It will also remember the motion activities, such as how often I walk, how fast I walk, how much I drive, how much time I’m stationary.  It captures memories for you, and enables you to search your past for quick recall of the places you’ve visited.

For people who are conscious about themselves, Placeme helps them keep track of their activities, and better understand themselves.  People are always busy, and often forget to record what they want to log. Therefore, people need a tool to automatically remember things for them.  Placeme is such a tool.

Placeme can also be used to understand people’s personal activities and health habits, and help people improve their lives.

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

Liang: I have always been curious about how I spend my life everyday. I always wished there was a tool that can journal my life automatically, understand my behavior and habits, then intelligently suggest things to me, which can help me improve my time management and improve my life as a result.  As one example, although I often try to change some bad health habits, I almost always fail, because I’m busy working on something all the time, and can’t remember what I should do, and I’ll always regret it afterwards.  So I’d love an intelligent personal assistant to help me achieve all of these.

When I was the architect for the Google Location Server, I realized that smartphones today have so many great senses.  They can see, touch, and hear, in addition to sensing location and motion. With all these sensor data, the phone can learn so much about the mobile user, and can infer a lot about the user’s habits, interests and can predict future needs. So I wondered why can’t we make mobile phones more intelligent and help people automatically without requiring them to do everything manually.  So I founded Alohar Mobile with a couple of friends from Stanford to pursue this dream.

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

Liang: I have been running Placeme for Android and its predecessor for over a year. It has given me a lot of interesting insights, such as how much time I spend at work, at home, how much time I spend commuting, how often and how much time I spend playing tennis.  For example, I noticed that in the past several weeks, because we are working so hard on our next release of Placeme, my work time has significantly risen, and I didn’t play tennis for 4 weeks!  Seeing this data, I decided to go to swim at YMCA in the morning to increase my work-out time. Also, I saw that I spent far more time in office than at home for several weeks, to adjust the balance between family and work, I changed some of my work-hours, so that I can spend a bit more time with my family and I’ll do some additional work at home after the kids go to bed.

It automatically captured all the interesting places I visited during my trip to Alaska last summer and allowed me to easily reminisce about my trip. Interestingly, it also captured my black Friday shopping trips and the data showed me how much time and gas I wasted while driving around to and from the stores and malls, etc. The first screenshot (below) shows the places I spent some time at that day; the second screenshot shows some of the data Placeme automatically calculated from my location data; the third  is a pie chart I made myself from that data.

We are still in our early stage, however, we’ve got dozens of enthusiastic beta testers running our application now. Many beta testers told us that they discovered some interesting facts unknown to themselves before, such as how much junk food they are having each week, how much time they actually spend walking, or going to the gym, and how much time is wasted commuting everyday.

Q: What makes it different, sets it apart?

Liang: Placeme has a number of unique features. The most important feature is that, in contrast to some existing applications, Placeme does most of the work automatically. Once the application is installed, it runs in the background, and requires no user assistance. It remembers all the data automatically, and it automatically generates the analytics results (daily, weekly, and historical) and presents them to the user. The user is not required to manually open the application, except when the user would like to see the results.

So Placeme requires little effort from the user, and makes it easier to be adopted.

Also, Placeme uses some intelligent power management algorithms (patent pending) to reduce battery consumption caused by sensor sampling. Though there is still a lot of optimization for us to do, we believe we have achieved one of the best battery life scores among such apps.

It runs on a smartphone, which most people are already using today. The user doesn’t need to carry a separate data gathering device (like Fitbit).  All he needs is his smartphone running the Placeme app.  In addition, the application is always connected to the Internet. So it can automatically save data to the cloud, have the cloud run sophisticated analytics algorithms, search for related info over the Internet, and then generate more interesting recommendations to the user.

In the mean time, all the data is kept private, and the user has full control of the data.

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

Liang: The Android app has just launched, and we are currently developing the iPhone app.

We have an ambitious plan to build more and more intelligent features to better understand people’s habits and intentions, and make recommendations to help them improve their lifes.  In the long run, we see Placeme evolving into an Intelligent Personal Assistant.

In a future version of Placeme, we want to offer a reminder service to notify people to break from bad health habits, and form good ones. For example, when our app detects that the user has been stationary for too long today, the app will automatically talk to the user and ask him/her to take a walk. Also, when our app detects that the user has visited junk food restaurants 3 times in a week, the app will send a warning to the user and recommend healthy alternatives.

We realize that we won’t be able to build all the great future features by ourselves, so we plan to offer a platform to make the technical functionalities available through an open API. Therefore, any mobile app developer can use our SDK and open API to build their own unique mobile applications by leveraging the mobile data collection and data analytics algorithms we have already developed.  In addition, several mobile health application developers want to leverage the infrastructure Alohar is building, including the power-efficient data sampling algorithms and the mobile sensor data analytics system running in the cloud.  And, several mobile game developers would like to use Alohar’s infrastructure to build more personalized games. (Developers interested in SDKs and APIs: info@alohar.com)

Q: Anything else you’d like to say?

Liang: The QS group is very passionate about self-measurement and self-improvement. We would like to invite more QS members to try Placeme so we can learn your feedback and suggestions for additional features.

Product: Placeme
Website: https://www.placemeapp.com and http://www.alohar.com
Platform: Android (now); iPhone (soon)
Price: free

(If you are a “toolmaker” and want to participate in this series, contact Rajiv Mehta at rajivzume@gmail.com) 

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Toolmaker Talk: Bethany Soule & Daniel Reeves (Beeminder)

This is the fifth 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?

Bethany Soule and Daniel Reeves have presented at New York City QS meet ups (here and here) on a couple ideas that came together and turned into Beeminder, which they co-founded in 2010. Through much personal experimentation they’ve developed unique ideas on how best to visualize your progress towards a goal and how to set just the right amount of monetary incentives.

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

Soule: Beeminder is a goal-tracking tool with teeth. Report your progress every day and make sure to keep all your data points on a “yellow brick road” to your goal. If you fail to do so your graph will be frozen and you can pledge (by which we mean pledge actual money) to stay on track on your next attempt.

Reeves: The idea is to give yourself a kick in the pants. Here’s how to tell if Beeminder could be useful for you: Is there something you know you should do, you really do want to do, you know for certain you can do, yet that historically you don’t do? (Also, are you a highly nerdy data freak?)

Soule: What we mean by the “yellow brick road” is a line on your graph that gradually gets you from here to there and tolerates some daily deviation without allowing a slippery slope of sloth.

 

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

Reeves: I had a friend who wanted to lose weight. This was in February 2008. I had her email me her weight every day and I’d send her back graphs of her progress and tell her if she was on track to hit her target in time. I was mostly following the principles of The Hacker’s Diet, in particular the part about getting as much data as possible but smoothing it so as not to be discouraged by random fluctuations.

Soule: I quickly wanted in on it, because I was tracking my own weight in Excel — Lame! So we started automating it and getting more friends and family on board. We called it Kibotzer (the kibitzing robot), though no one got the pun. Even before we started with the data collection and visualization side of things we’d been making bets with each other as part of various productivity schemes for quite some time, so it was only natural to bet about staying on track with our graphs.

Reeves: We’ve since dropped the betting terminology but it’s equivalent. Now you’re pledging (money) to stay on track on your yellow brick road. (HT: PJ Eby)

Soule: In 2010 we decided to quit our day jobs and turn it into a real startup, which we renamed Beeminder.

Reeves: But if you really want to trace the roots, the backstory starts in 2005 when Bethany and I were dating and I was writing my dissertation. I’d been dragging it out forever so Bethany concocted a Voluntary Harassment Program, as she called it, and we tried out all kinds of crazy incentive schemes and productivity hacks. They apparently worked, since I got my PhD that year.

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

Soule: Our users think it’s the bees’ knees! I assume that bees have awesome knees.

Reeves: We do have a small number of users who find it powerfully motivating. Lots of weight loss success stories, of course. And we use it to force ourselves to keep up momentum on Beeminder itself.

Soule: Here’s an ongoing success story that we blogged about: Our friend and early beta user, Jill, wanted to join a new gym, which is often a recipe for throwing away money. But she actually worked out how often she would need to go to make the membership worth the money (1.8 times per week, on average) and then used a large Beeminder contract to force herself to maintain that average. That’s been going since March: beeminder.com/jill/gym.

Reeves: There are plenty of failure stories, too. We find that it really only makes sense to beemind things that are both objectively measurable and that you have complete control over. So you can beemind how much time you spend working but not, say, how focused you are. Weight loss is a borderline case: you don’t have complete control over it since your weight fluctuates randomly from day to day, but we’ve put a ton of work into adjusting for that with an auto-widening yellow brick road and other data-smoothing tricks.

Q: What makes it different, sets it apart?

Reeves: Primarily that it works as a commitment device. Most goal-tracking sites don’t work that way (nor do they want to). A notable exception is StickK.com. What sets Beeminder apart from StickK is the focus on the data and the graph and Yellow Brick Road. By having everything based on your data you get far more flexibility. We think it’s more motivating and insightful to pledge to keep your data points on a yellow brick road to your goal than to StickK to your goal.

Soule: Yeah, with StickK it’s all about the contracts. You have to fully pre-specify exactly what you’re committing to do and how much money to put at risk to force yourself to do it. With Beeminder you just first start tracking. Your data then informs you on what to commit to. You don’t even have to think about how much to risk — we tell you, and you climb up the fee schedule until you hit an amount that really motivates you. There’s also this clever thing called the “akrasia horizon” that lets you continuously adjust your commitment — the steepness of the yellow brick road — without it, y’know, defeating the whole point of a commitment contract.

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

Reeves: We’re working our butts off on a ton of features that our users are asking for in the Beeminder feedback forum.

Soule: Beeminder is literally getting better every day. In fact, we’re beeminding that: We have to make one User-Visible Improvement to Beeminder on average per day or pay one of our users $1000. We’ll have made about 300 improvements when this goes to press!

Reeves: In the near future we’d like to add more ways to automatically collect data instead of needing to report data points to the Beeminder bot. We can currently connect Beeminder to Withings scales and our own (very hacky) TagTime stochastic time tracker. Bethany also made a pushup counter for Android which semi-automatically counts pushups (you put the phone on the floor and touch your nose to it). Finally, we have a version of our API in private beta which a couple people have used to automatically send data to Beeminder as it’s collected.

Q: Anything else you’d like to say?

Reeves: If you want to keep up with the latest on Beeminder, follow the Beeminder blog — we’re committed (literally) to posting frequently!

Product: Beeminder
Website: http://beeminder.com
Platform: Web, email or SMS
Price: Free as long as you stay on your Yellow Brick Road

(If you are a “toolmaker” and want to participate in this series, contact Rajiv Mehta at rajivzume@gmail.com) 

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Toolmaker Talk: Gil Blander (InsideTracker)

This is the third post in the “Toolmaker Talks” series. The QS blog features many stories by those conducting personal QS projects that are about: what did they do? how did they do it? and what have they learned?  In Toolmaker Talks we hear from those closely observing all this QS activity and developing appropriate tools: what needs have they observed? what tools have they developed in response? and what have they learned from users’ experiences?

The two primary sources of data for most self-trackers are self-observations and  consumer-oriented sensor gadgets. Data from laboratory tests is generally limited to whatever you get from your doctor. Segterra, a new Boston startup, has launched a new service InsideTracker that makes personalized blood analysis much more accessible.

Founder Gil Blander explains what led to its creation and the impact it has had.

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

Blander: Segterra’s InsideTracker is a new web-based service that  automatically-generates a set of nutrition and lifestyle recommendations based on a panel of blood biomarkers and the person’s goals, circumstances and preferences.

I am sure QS’ers appreciate the adage that you can’t manage what you don’t measure and unfortunately most of us don’t have any real data about what is happening inside our body, so it is difficult to know if our efforts to be healthy are really moving us it the right direction.  That is the problem that InsideTracker solves.  Quite literally, InsideTracker gives you a window into your unique biochemistry so that you can make better informed decisions to manage and optimize your health and performance.  Depending on your personal goals it can help you to run faster or farther, have more energy, be more productive and in general feel healthier.

InsideTracker includes the following key components:

  • Measurement of a number of key blood biomarkers through a simple blood test, and analysis using InsideTracker’s proprietary algorithms
  • Individualized nutrition and lifestyle recommendations based on your diagnostic analysis and a rules-based expert system that matches the individual’s input data with a knowledge base of facts about the relationships between the biomarkers and the desired health and wellness outcomes, such as body weight, physical performance, and subjective criteria of wellbeing.
  • Ongoing testing lets you assess progress.

Q: What’s the backstory? What led to this service?

Blander: The creation of InsideTracker is a culmination of my fascination with the aging process, and a passion to preserve health and vitality throughout our lives.  When I was 12 I had a close family member die and it triggered in me a desire to know everything I could about why people age and become sick.  I did my postdoctoral work at MIT in Dr. Leonard Guarente’s Laboratory for the Science of Aging and came to appreciate the strong connection between aging and overall health.

While at MIT, I caught the entrepreneur bug, but I didn’t quite have a clear product idea. Then I met David Lester. David’s career path has taken him from academia to government, to industry, giving him a rich perspective on all matters of health.  David was interested in using systems approaches to create segments of populations based on outcomes. Could we segment the field of health information according to specific populations, even individuals, to improve quality of life?

‘Systems thinking’ is the process of understanding how segments influence one another within a whole. It is taking individual and dynamic characteristics (like biomarkers) and looking at them in terms of how they act as part of a larger environment (like the human body). The combination of using systems thinking plus affordable diagnostic results from a blood sample to create uniquely personal recommendations was something that no other company was offering so we jumped in and launched Segterra to provide that kind of service – and that is how InsideTracker was born.

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

Blander: While InsideTracker is still in its infancy, early adopters and participants from our pilot have been enthusiastic about their experiences. One user had been concerned about vitamin D deficiency, but using InsideTracker discovered he was fine. Another had been confident about his nutrition but learned he was in fact low on iron. A third has been able to make some minor changes to his diet to move several biomarkers in the right direction.

Q: What makes it different, sets it apart?

Blander: Several aspects of InsideTracker are unique:

  • We have made using blood results more affordable so that you can use it as a metric to measure health on a recurring basis.
  • We have made it easy for the consumer to access and interpret this information and to see time series so they can understand positive or negative trends and take the appropriate action.
  • We have defined personalized ‘optimal’ ranges for each of the blood markers we include in the service.  A general Lab report that your doctor sees has universal ‘norms’ for each value.  A 18 year old man and an 80 year old woman both have the same range of ‘normal’.  We’ve been able to bring a lot of science to bear to define optimal ranges for individuals based on demographic and lifestyle differences.  It is the difference between knowing what is pass/fail and knowing what gets you an ‘A’ on health.
  • We’ve made our recommendations engine very flexible so that personal preferences and needs can be easily managed.  If you are low in iron and don’t like spinach, we can make recommendations that reflect these preferences and still help you meet your goals.

It is also worth noting that InsideTracker is not affiliated with any supplement vendor and that micronutrient recommendations are based solely on the biomarker results.

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

Blander: There are a number of directions that we can take InsideTracker in terms of additional functionality, but we want to do a good job listening to customers and let them drive our priorities.  We will continue to enhance our algorithms to incorporate the latest scientific literature and are looking at integration with other tracking tools, adding additional blood markers to our panels, community-building functionality to our website, and a variety of other features, but the exact sequence of things will be driven by customer feedback.

Q: Anything else you’d like to say?

Blander: We love what you are doing with Quantified Self and we are looking forward to becoming more involved with the community. We think this audience can be power users of the service and give us valuable input on how to make InsideTracker better.  As a reflection of our commitment, we want to offer a special discount code to QS members that will allow them to save $50 when they purchase the service.

Product: InsideTracker
Website: www.insidetracker.com
Platform: Web
Price: $169 or $249 — QS Member Special Discount Code: QSNATM11156

(If you are a “toolmaker” and want to participate in this series, contact Rajiv Mehta at rajivzume@gmail.com)

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Toolmaker Talk: Nicholas Gammell (GAIN Fitness)

This is the second post in the “Toolmaker Talks” series. The QS blog features many stories by those conducting personal QS projects that are about: what did they do? how did they do it? and what have they learned?  In Toolmaker Talks we hear from those closely observing all this QS activity and developing appropriate tools: what needs have they observed? what tools have they developed in response? and what have they learned from users’ experiences?

Frequent visitors to San Francisco QS meetups have watched GAIN Fitness grow from an exciting idea to a very helpful exercise tool. It whips up an exercise routine based on what you want to do at the moment — “I’ve got 15 minutes, I’m in a hotel room with no equipment … what can I do that will still help me in my weight loss goals?” — and helps you track your workouts.

 

Founder and CEO Nicholas Gammell explains what led to its creation and the impact it has had.

Q: How do you describe GAIN Fitness? What is it?
Gammell: GAIN Fitness is like a digital personal trainer in your pocket. It allows you to design customized, personal-trainer quality workouts based on your real-time goals and constraints – e.g. fitness level, time and equipment available, desired intensity, etc. You then “play” each customized workout and a series of timers, instructional images and tracking tools will push you efficiently through your workout session. The underlying recommendation algorithms were developed in consultation with certified personal trainers and can produce literally millions of uniquely tailored workouts in a matter of seconds.

Q: What’s the back story? What led to it?
Gammell: I’ve always been a pretty serious exerciser, having grown up playing multiple competitive sports (football, baseball, basketball) where training mattered. I started lifting weights when I was 12 years old and the family got a Soloflex for Christmas. Meanwhile, I always read Men’s Health, where I learned a variety of different training techniques and the basics of exercise science. Ultimately, I went on to play college football at Carnegie Mellon (a school known more for its tech geeks than for its varsity athletics), and I trained hard in the offseason with lifting coaches and teammates.

When I started working as a traveling consultant, my first job out of college, I faced a difficult challenge — how to keep a steady, progressive fitness schedule despite long, unpredictable hours and intermittent gym access. I knew it was completely possible. Whether you have 45 minutes at the gym or 15 minutes in a hotel room, a challenging routine can be designed. It was merely an information problem, and I hacked together a rudimentary Excel model that helped offload some of the thinking/planning aspects to designing situated-adapted workouts on the fly.

Q: What impact has it had?
Gammell: Personally, I’m in better shape now than I’ve been in the past 10 years, and I’m spending about 40% less time working out. I do 3 or 4 GAIN workouts a week, about 20-45 minutes each, plus a cardio activity like basketball or running once a week and a little yoga/stretching in the mornings. I can rep out about 20 pull-ups and I hopped a mountain bike the other day and road 55 miles without much trouble. I do this all while working 70-80 hours per week, so I think anyone can find 2-3 hours a week to get their fitness to a pretty decent level.

We’ve heard many great things from our users – some have told us they’re working out regularly again for the first time in years, others say they’ve lost significant weight and their friends have taken notice. We haven’t had the resources to pull together any before/after pics or transformation stories yet, but plan to do so in the near future.

Q: What makes it different, sets it apart?
Gammell: It’s really the algorithmic, data-driven approach that sets GAIN apart from other fitness apps. We viewed the problem as a big data problem from the outset, and designed a system from the ground up to eliminate as many friction points as possible, providing users with real-time, personally tailored workouts at their command. Most other fitness apps leave you with a bunch of off-the-shelf workout programs to pick through and, at the end of the day, aren’t really that customized. Or they require oodles of manual data entry up front before they do much.

We don’t want you to spend time researching workouts, thinking about what you should do, or entering lots of data. We provide users with an instant action plan so they can stop mulling over “what should I do…” and get right to it.

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

Gammell: We’re really just in the first quarter. We’re building a platform for fitness experts with different specialties to scale their programs to mass audiences instantaneously. We call this concept “iTunes for Fitness.” Want to maximize your performance next ski season? Want to build muscle but have some rotator cuff issues to work around? These are some of the goals and issues people face in creating a personalized fitness program, and we want to help top fitness experts in various niches turn their expertise into scalable algorithms so users can access workouts precisely tailored to their needs. We’re starting out by launching a few new “fitness packs” that contain workout protocols and exercises designed by top-notch fitness experts.

Q: Anything else you’d like to say?

Gammell: Just that we’re really excited to be here, to help out lots of people look and feel better, and we want to hear your feedback so we can continue to design our personalized training apps to better meet your needs and remove friction from your journey to gain fitness.

Product: Gain Fitness
Website: http://gainfitness.com
Platform: iPhone & Web
Price: free

(If you are a “toolmaker” and want to participate in this series, contact rajivzume@gmail.com)

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Toolmaker Talk: Robin Barooah (Equanimity)

This is the first post in a new series of “Toolmaker Talks” we’re starting on the Quantified Self blog. There are many conducting personal QS projects, and much of what is featured on the QS blog is about: what did they do? how did they do it? and what have they learned?  Now, we want to also hear from those closely observing all this QS activity and developing appropriate tools: what needs have they observed? what tools have they developed in response? and what have they learned from users’ experiences?

Equanimity, an iPhone app, is a beautiful timer and journal for meditation. Its functionality (timers, logs, charts) and design support your meditation practice in an appropriately non-intrusive way. As one reviewer noted: “Meditating is all about letting go of your frustrations and achieving peace of mind. … [Equanimity] is easy to use and everything about it is focused on offering you a calm experience.”

Developer Robin Barooah explains what led to its creation and the impact it has had.

Q: How do you describe Equanimity? What is it?
Barooah: In the most basic sense, Equanimity is an iPhone app that I designed to help me meditate regularly.  It does this in two ways. First, by providing a timer that’s easy to use and not distracting.  That helps with the meditation sessions themselves because it provides a well-defined end time so I don’t have to worry about going on for too long and disturbing my daily routine.

Secondly, and to me more importantly, Equanimity keeps a log of the meditations it has timed, and provides clear graphical feedback on how frequently I meditate, and how long and how consistently I’ve maintained my practice for.  It also provides a gentle reminder in the form of an indicator that shows whether I’ve meditated yet that day.  The idea behind these features is that they provide an honest reflection of my meditation practice, and that this reflection influences my behavior.

Before I used Equanimity, I found that I would meet resistance in my practice and have an inaccurate perception of how much I was meditating.  I found it easy to think I was meditating every other day, though actually only doing it twice a week, if I didn’t keep a record.  I’ve found it’s even possible to forget during the day whether I’d done it or not.  Since I do actually want to meditate each day, this kind of gentle feedback is enough to help me keep on track in a way I found very hard before.  It’s basically an antidote to self-deceptive or inaccurate thoughts.

Q: What’s the back story? What led to it?
Barooah: I had gone through a particularly stressful couple of years and even though the stress was over, I found that I was experiencing anxiety and lowered concentration. Meditation is associated with spiritual benefits and self-knowledge too, but at the beginning of the project I was just looking to recover.  I had previously meditated in various classes and knew that meditation could help me, but I hadn’t managed to establish a practice outside of a class.  I knew that I wasn’t the only person who had trouble making meditation part of their routine, so I thought that if I could solve the problem for myself, my solution would be useful for others too.

I’d experimented with keeping track on paper and using a coffee timer in the past, without success.  That would often break down because I wouldn’t have the paper and timer with me when I thought of meditating.  I experimented with building a web application, but it became clear that an iPhone app had the potential to be much more personal, and was more likely to be with me when I needed it.  Also, having a computer sitting in the background didn’t feel right.

Q: What impact has it had?
Barooah: I think I can now say that I meditate every day.  It took much longer for me to get to that point than I anticipated, though — something like 18 months.  Over that time, by looking at my meditation history I was able to learn about things that disrupted my practice and make adjustments.  Doing meditation early in my day is much more reliable than later, for example.  More interestingly, I could see from the annual chart that things like traveling, illness, and minor depressions all had the potential to significantly disrupt my practice.  They still do have an effect but now typically only for a day at most, because I understand what’s happening and can adapt my routine accordingly.

I think it’s also helped me grow significantly in patience with myself, by revealing what I would probably have thought of as a series of independent failures to be a slow learning process leading to success.

As far as other people go, it’s a little harder to say. I don’t collect user data because I think that would interfere with the sense of meditation being a private experience.  There are thousands of users, though, and I have heard from many people who also say that it’s helped with their practice. There are also regular meditators who had no trouble practicing regularly before, but use Equanimity because they just like the design.

At some point I would like to ask people to sign up for a study so I can learn more about the range of experiences, but I never feel good about  software that persuades people to give up personal information, so that will be a separate project that people can volunteer for.

Q: What makes it different, sets it apart?
There are a few other well-produced meditation apps available for the iPhone.  Each has a different focus.  I think Equanimity is unique in being directly focused on solving the problem of cultivating a daily practice.

I use it myself every day, so I’ve removed all the friction I can from the daily meditation process.  The feedback charts are carefully designed to provide information that is useful at different stages in the process of developing a practice without needing any work.  For most people it’s self-explanatory and doesn’t need any setting up.  The more advanced features only come into view when you need them.  As I learn more, I’m steadily developing the app while maintaining its simplicity.

Q: Anything else you’d like to say?

Thanks for asking me about this project!  It’s nice to have a chance to reflect on it.  I think that now that we have truly personal computing devices we are starting to learn how to use them to learn more about ourselves as human beings.  To me, this presents genuinely new and optimistic possibilities for improving our lives.  I’m looking forward to learning more about the stories behind other projects as you continue this series.

Product: Equanimity
Website: http://meditate.mx/iphone
Platform: iPhone
Price: $4.99

(If you are a “toolmaker” and want to participate in this series, contact rajivzume@gmail.com)

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Technology Review Explores the Self-Tracking Movement

Emily Singer, a journalist with MIT’s Technology Review, has an extensive series of articles and interviews on “The Measured Life“. She was at the Quantified Self Conference a month ago, seems to have talked with everyone, and has since been writing up a storm.

The July issue of the magazine has a cool cover, the featured article is on The Measured Life, and there are highlights of many of the popular Tools for Quantifying Yourself.

There’s much, much more on the web.

Emily’s been tracking her own life, and reports on her overall experience, on physical movement, on sleep, and on blood pressure. Also very interesting are the failures, and technology troubles.

There are also video interviews. Kyle Machulis describes his efforts to hack tracking devices so everyone can access their own data. David Marvit talks about Fujitsu’s Sprout project and the importance of obtaining biometrics in real-world conditions. And Rajiv Mehta talks about the potential for personal science to make a significant impact on healthcare and medical science, and demos Tonic.

And there are posts on social networking and games in self-tracking technologies, on astronauts measuring sleep, a physician’s perspective, the new Health Graph effort, and a wristwatch that continuously monitors blood pressure.

 

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Rajiv Mehta on Tonic and Experimentation

In this video, Rajiv Mehta talks about the importance of remembering for good experimentation — carrying out the experiment as planned and capturing the results properly — and the difficulty of doing this well. He described a new app, Tonic, that helps people remember and keep track of their health activities, and shared examples of people using Tonic to learn about and better manage their health. (Filmed at the Bay Area QS Show&Tell meetup on 3/24/11 at TechShop).

Rajiv Mehta – Remembering & Enabling Experimentation from Gary Wolf on Vimeo.

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A Billion Little Experiments

I have been participating in the QS Show & Tell meetings since they started.  What those of us in the QS are working on today, and the interest we take in tracking and analyzing all aspects of our lives, is not quite mainstream yet. But many of us feel that like those pioneers in the Homebrew Computer Club what we’re doing will eventually have a huge impact on everyone’s lives. I have written a paper “A Billion Little Experiments” on the potential to dramatically improve healthcare and medicine by harnessing petabytes of data from individuals taking care of their own health, and on the necessary shifts in mindset required. 
In summary, the paper argues that we can and must:
  • Enable self-care success with great personal health management tools.
  • Empower self-experimentation through education, encouragement, and assistance.
  • Exploit the data from these experiments to advance health care and science.
The gist of the story is …
Optimal health practices will vary, at least somewhat, across individuals and circumstances.  Today’s health science is too limited to be able to say exactly what is the best health practice for a particular person in his specific circumstance.  Optimal health practices will also vary over time  Therefore health requires continuous optimization, and constant experimentation to identify changes and seek optimal practices.
People are in fact constantly experimenting, trying new foods, activities, medications, etc.  We could theoretically benefit tremendously from this experimentation.  Health science would benefit if good data was collected from thousands, even millions, of people and properly analyzed.  Today we don’t do this, especially because few people are collecting and/or providing good health data.  This is an opportunity wasted.
How can we collect this data?  How can we get people to track their health, when we know from long experience that adherence is poor?  I propose it demands a fundamental re-examination of the problem of non-adherence, and a focus on supporting people’s desires rather than telling them what to do.
Poor adherence is labeled as a problem of poor motivation and discipline.  This is wrong.  Adherence is poor because it is nearly impossible, in the context of daily living where health is just one of many competing priorities.  People need help, need much better tools, to improve adherence.  Such tools are possible, but most of the tools offered to-date are simply inadequate and inappropriate.  Far better personal health management tools need to be developed.  This is an issue that must be addressed — good experimentation is impossible without the ability to carry out the experiments properly.
Just as important is recognizing that people’s priorities, appropriately, are on living rather than on health.  No matter how valuable their health data could be to the advancement of health science, people cannot be expected to take on the chore of tracking.  We, who want the data, must focus on addressing their need — to live better today to make life easier today– and do it in a way that will provide the data we want.  That’s our design challenge, a burden we must bear, not one to impose on people.
We can do this.  We can develop personal health management tools that make people’s day-to-day lives easier, that provide them with help they want, and provide the data that will help us advance health science.  We must do this.  Without rich data from a broad swath of people, advancement in health science will continue to be hampered by a dearth of data.   We must change our mindsets and provide the product & services to inspire and harness a billion little experiments.
Rajiv Mehta is founder and CEO of Zume Life, and also consults on driving radical innovation.

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