Topic Archives: Toolmaker Talks

Toolmaker Talk: Alexander Grey (Somaxis)

The first speaker at last week’s QS meetup in San Francisco was Alexander Grey. He told us about the muscle-activity sensor he had developed and the fascinating things he had learned about himself from using it. The result of many years of thinking and work, he’s now eager to find collaborators, so he jumped at my suggestion to participate in this series.

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

Grey: We have developed a small, wireless sensor for measuring muscle electrical output. The sensors stick onto the body adhesively (like Band-Aids) and transmit data to our smartphone app. One version “MyoBeat” uses a well established heart metric to provide continuous heart rate measurement (like a “chest strap” style sensor). A second version “MyoFit” uses proprietary algorithms to measures the energy output of other muscles. For instance, one on your quads while running can give you insight into how warmed up you are, how much work you are doing, fatigue, endurance, and recovery level. If you use two at the same time, it can show you your muscle symmetry (when asymmetry develops during exercise like running or bicycling, it can indicate the onset of an injury). Our goal is to get people excited about understanding how their bodies work.

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

Grey: My parents used to run a clinic that used muscle energy technology (sEMG) along with a special training method called Muscle Learning Therapy to cure people with RSI (Repetitive Strain Injury) and other work-related upper extremity disorders involving chronic pain. Each sEMG device they bought cost them $10K. I started to develop early symptoms of TMD (Temporomandibular Joint Disorder) when I was only 10, and my father used sEMG to teach me how to control and reduce my muscles’ overuse. The training worked, and I still have it under control today.

Years later, I decided to start a company to develop and commercialize  more accessible / less expensive sEMG technology, with my mom as my investor. (My father has passed away, but I think he would have supported the idea.)  At first we were going after a workplace safety service — I developed an algorithm that quantified people’s likelihood of developing an RSI injury in the future, and envisioned a prevention-based screening/monitoring service to offer to progressive companies. The feedback I got from VCs was that we needed to start with a bigger market. So we redesigned the product to make it small, cheap, and completely wireless. I also started working on a new set of sports-related algorithms to interpret muscle use into useful metrics.

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

Grey: Having this new kind of tool at my disposal has really been a lot of fun, and has allowed me to run some new kinds of experiments that haven’t really been practical before.

For example, I wondered: for a given running speed, what cadence or stride rate would use the least energy, and so delay the onset of fatigue? I put sensors on my both quads, hamstrings, and calves. I created an audio track that increased from 120 – 170 bpm in increments of 5pm, 15 seconds on each. I kept my treadmill locked at 6.5 mph (my “comfortable pace”). By adding up the work done by all 6 muscles in the legs, I got a snapshot of the energy expenditure at each stride rate / cadence. The resulting curve [see graph above] answered my question: for me, at 6.5 mph, 130 bpm is my “sweet spot” that minimizes energy expenditure. It also showed a second trough in the graph, not as low as 130, but still pretty low, at 155 bpm. So if I need to run uphill or downhill, and want to keep the same speed but take shorter steps and still try to minimize energy burn as much as possible, I should shoot for 155 bpm.

Another test that these tools allow us to do is to figure out how recovered someone is from exercise. I did a test where I ran at a fixed speed every 24 hours (that’s not enough recovery time for me – I’m not in good shape). The first day, the muscle amplitude was about 1000 uV RMS (microvolts, amplitude). The second day, the amplitude started out at 500 uV and decreased from there. So the lack of sufficient recovery showed up in the data, which was quite interesting to see.

Whenever we have volunteers in the lab offering to help out (runners, usually) they geek out over these devices and the insight that they can get into the muscles of their bodies for the first time. We’ve had about 40 volunteers help out with muscle data gathering, and about 60 with heart rate testing.

Q: What makes it different, sets it apart?

Grey: Our design goals for our sensors are “good enough” data, wireless, long battery life, and comfort (wearability). Key to this is using a low-power, low-bandwidth radio. The trade-off is a much lower sample rate and a/d resolution than medical-grade sensors. Our sensor transmits processed data, not the raw data. However, our data is good enough for sports and fitness, where you want to see some predigested metrics and not raw graphs or frequency analysis. The benefit is that our battery life is 100 hours, and our sensor is small and light enough to attach using an adhesive patch. The up-side of an adhesive-based solution is that one-size fits all, it’s very comfortable, and there is no tight and annoying strap around your chest.

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

Grey: We are mainly focusing on improving the physical sensor itself: rechargeable battery, completely waterproof (current version is water resistant), and a smaller size. And maybe a medical-grade version with much higher sample rate and a/d resolution.

We also want to open up the hardware platform so that others can develop applications for it. For example, maybe someone wants to develop software for Yoga that uses muscle isolation to help do poses correctly. Or perhaps someone wants to focus on a weight-lifting application that assesses power and work done during lifting. We can envision many possibilities for sports, gaming, physical therapy, and health.

Q: Anything else you’d like to say?

Grey: I would love to hear from anybody who has ideas about potential uses of our technology! Also, we are fairly early-stage, so if anyone wants to work with us (individuals) or partner with us (companies) we definitely want to hear from you. You can reach me at

Product: MyoLink platform: MyoBeat (heart) and MyoFit (muscle)
Website: (coming soon – there’s nothing there right now, but check back again soon)
Platform: Sensors stream data to an iPhone app (Android under development) and certain sports watches (Garmin, etc.)
Price: $25 for a starter set of 1 Module (MyoBeat or MyoFit) and 4 adhesive patches. Or you can buy 1, 2 or 3 Modules, with a one-year supply of patches, for $75, $125, or $170, respectively.

This is the 11th 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

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Toolmaker Talk: Mike Lee (MyFitnessPal)

At a QS Meetup in San Francisco about a year ago, I ran into a friend I hadn’t seen in over 15 years. Imagine my surprise when I discovered that he had quietly built one of the most widely used weight loss tools: MyFitnessPal (with 170,000 ratings on the AppStore, mostly 5s!). Mike Lee explains the focus, passion and patience it has taken to do this.

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

Lee: MyFitnessPal is a calorie counter that allows you to easily track your diet and exercise to learn more about what you are eating and how many calories you are consuming and burning. We have a website as well as mobile apps on every major platform, all of which seamlessly sync with one another so you can log at your computer or on your phone, whichever is most convenient. We also provide a variety of social networking tools so that you can easily motivate and receive support from friends and family, as well as stay informed of each other’s progress.

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

Lee: In 2005 my wife and I wanted to lose weight before our wedding. We went to see a trainer at 24 Hour Fitness, and he suggested that we count calories. He gave us a small book that had calorie counts for about 3,000 foods in it, and told us to write down everything that we ate. Being a tech guy, there was no way I was going to do this on paper, so I immediately threw the book away and looked for an online solution. There were already tons of online calorie counters available — I probably tried at least 15 myself — but to my amazement, none of them worked the way I thought they should work. They were all incredibly hard to use; I actually found it easier to track on paper than online. I was looking for a new project to work on, so I decided to write my own calorie counter — that’s how MyFitnessPal was born.

Soon my brother joined me. We’ve kept the team very small, while slowly building up a loyal following. We passed a million users a few years ago, and are still growing very rapidly.

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

Lee: One of the best parts about working at MyFitnessPal is the messages we get from our users. I’d estimate that anywhere from 30-50% of the emails that we get are from people simply telling us how much they love the app, and how much it’s helped them lead a healthier life. People write in telling us that they’ve been trying to lose weight for 20 years, but nothing had worked until they tried MyFitnessPal. We hear from people who’ve been able to cancel surgeries, stop taking medications, fit into jeans they haven’t worn in years, or even things as simple as just being able to stand up without using their arms to push themselves up. We have thousands and thousands of members who’ve lost 100 pounds or more. We’ve even had people get married after meeting on MyFitnessPal.

It’s hard to generalize users’ experiences because we have so many users. And they vary widely: there are people who’ve never exercised, who would find a 15 minute walk difficult, and we have professional body builders.

Still, one thing stands out, which is that the biggest benefit is education. It’s amazing how little most people know about what they eat or the activities they perform, and once they start using the app, it’s eye-opening. They discover what they eat, how much, how often, the nutritional content of the food, and the impact of physical activity. They build up knowledge that stays with them even if they stop logging their foods. With this knowledge they can make their own decisions about what to change in their lives, what trade-offs are best for them. It’s not following some diet fad, but discovering what works for you.

Q: What makes it different, sets it apart?

Lee: We really pay little attention to other apps or the media. Rather we’re fanatically focused on our own users. We listen deeply to user feedback, but we don’t just do what they ask for. Instead we try to understand their real problem, and focus our work on the things that we’ve discovered really matter for losing weight.

We know losing weight is really hard and that tracking is a pain-in-the-neck. So, we really work hard to make our site and our app as easy to use as possible. We know that the easier and faster we can make logging your foods, the more likely you are to stick with it, and consequently, the more likely you are to reach your goals. As a tool maker, it’s our job to help make that process as easy as possible and remove every barrier we can to your success. I can’t really point to anything in particular about ease-of-use; it’s just something we focus on relentlessly and something that the team is just good at.

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

Lee: Over the past year, we’ve worked hard on expanding the number of platforms on which MyFItnessPal is available. We’ve released apps for Blackberry, Windows Phone 7, and iPad. Though they are similar, the interfaces are tailored for each platform. Now that we’re available on most major platforms, we’ll be spending more time on improving our core logging tools. We’ve got a ton of ideas on how we can make calorie counting even faster and easier, so hopefully you’ll be seeing a lot of improvements in that area from us in 2012.

Q: Anything else you’d like to say?

Lee: If you’d like to keep up to date on the latest happenings on MyFitnessPal, you can like us on Facebook at or follow us on Twitter at

Product: MyFitnessPal
Platform: web, iPhone, iPad, Android, Blackberry, Windows Phone 7
Price: Free

This is the 10th 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

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Toolmaker Talk: Ross Larter (MoodPanda)

About three years ago, Gary Wolf wrote a detailed post on Measuring Mood — some tools are complicated enough to get you grouchy! Gallup goes through a lot of trouble to gauge the US happiness level on a daily basis. Others take a simple approach, such as Eric Kennedy’s recent talk at the Seattle QS meetup on Tracking Happiness.

Ross Larter believes an emphasis on simplicity and community (especially of people who you don’t know elsewhere) has been key to broad acceptance of his happiness-tracking MoodPanda.

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

Larter: is a mood tracking website and iphone app. Tracking is very simple: you rate your happiness on a 0-10 scale, and optionally add a brief twitter-like comment on what’s influencing your mood.

MoodPanda is also a large community of friendly people, sharing their moods, celebrating each others’ happiness, and supporting each other when they’re down.

People post many times a day – some tracking their mood from the moment they wake to the point their head hits the pillow at night! We organize people’s posts into their personal mood diary where they can view it many different ways: graphically, as a mood feed, broken down by metrics and even location based on a map.

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

Larter: MoodPanda got started in a pub in Bristol, England. A friend was asking people round the table how their day was and somebody replied with a 10/10.  My response was if today was the best day ever what happens if tomorrow is the same as today but then something else amazing happens (I think it included the “pussy cat dolls”), and we chatted for a while on this. The next day I started thinking about the question and told Jake (Co-Founder) about the idea and it went from there. We both work in software development so building the site was not an issue.

We are on MoodPanda version 3 at the moment. For the first 2 versions of the site we built it to track just your own mood. It was only once we added commenting and “hugs” to the current version that we realised that people wanted the interaction with each other. This is when our user based really started to grow.

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

Larter: Since the iPhone app has gone live it is growing quickly with many thousands of new user every month, over 60% now come from the Apple app store. We’re seeing about 1000 active user ratings a day. Hugs are a very popular feature. Panda users give out hundreds a day.

One thing we’ve learned is that there seems to be a strong demand for a place online where people can share their feelings with others who don’t know them in “real life”, people who won’t judge them. We see this in the data: only about 35% of mood ratings are passed through to Facebook and only 2% to Twitter. And we’ve heard this directly from users who have posted that its nice to talk to people that are interested in mood and wellbeing and don’t judge them.

Feedback from users has been fantastic, and in some cases very heartwarming. We’ve even had users tell us that they’ve “lived with years of hurt until they discovered MoodPanda”.

We’ve now got so many users in the UK that our mood map is pretty representative. Our UK live mood map was quite similar to the UK Government official one from last year. We also put together a nice infographic of all of our data from 2011.

We are always trying out new ideas, and some have not been well received. We had done some complicated graphs and visualization in the past, and we’ve learned that keeping it simple is the key to moodpanda.

I also never quite realised how much time is needed after all the technical work is done. I spend a ton of time talking on the radio, public speaking, blogging, twittering, etc. about MoodPanda.

Q: What makes it different, sets it apart?

Larter: What makes MoodPanda stand apart are its simplicity and community. Other mood tracking apps are very clinical and can often be intimidating to people first trying to track their mood. We keep it simple: rate your happiness from 0-10 and, if you want, say a few words about what is influencing your mood. The design and ethos of MoodPanda has been carefully cultivated to create a friendly, open and easy first step into happiness tracking.

The large community of “moody pandas” is the other major feature, as other mood tracking apps (like our first 2 versions) are private. We of course have users who want to remain private, but 92% of our users are posting as part of of the community. We have people giving “panda hugs” and commenting with help and advice constantly in the site and genuine caring friendships are being formed constantly. We’re working hard to understand what helps this community aspect of MoodPanda and build on it.

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

Larter: We recently started tracking hashtags so we could do stats on the sentiment of people’s comments that linked to the mood ratings. We’ve found that #coffee, #friends, and #food are associated with more happiness, and #sick and #work with less. We’re wondering whether we will learn whether some brands are strongly associated with mood (for example whether new #coke is good or bad) in ways that you can’t learn from normal brand sentiment tools.

We are working on the android app, and we’ve got a lot of ideas in the development pipeline involving more community features and technologies like an API.

Jake and I still have to go to work at our day jobs, but MoodPanda is a project that we both care deeply about. We’ve set a budget of $100 a month to spend on MoodPanda, so we do everything ourselves and get as creative as we can.

Q: Anything else you’d like to say?

Larter: Just a big thanks to you guys and girls at quantified self, its nice to talk to others that are as excited and interested in QS, if people continue to use moodpanda it to make themselves happier, we know we have done a good job!

Product: MoodPanda
Platform: web, iPhone
Price: free

This is the ninth 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 

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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
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 

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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:

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: and
Platform: Android (now); iPhone (soon)
Price: free

(If you are a “toolmaker” and want to participate in this series, contact Rajiv Mehta at 

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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:

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 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
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 

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Toolmaker Talk: Rich Rifredi (BAM Labs)

This is the fourth 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?

Studying sleep — the impact of sleep on health, and of various activities on sleep — is a popular topic for many self-trackers. People have videotaped themselves sleeping; spent time in sleep labs; kept sleep diaries; and used a variety of gadgets to measure brainwaves and motion. BAM Labs offers yet another approach.

Founder Rich Rifredi explains what led to its creation and the impact it has had.

Q: How do you describe BAM Labs? What is it?

Rifredi: BAM Labs turns any bed into a smart bed to help people manage their health and the health of those in their care.  Our Touch-free Life Care (TLC) system is completely non intrusive and features an under the mattress biometric sensor that collects heart rate, breathing rate, motion and bed presence without attaching anything to your body.   Our cloud services take these health data and turn them into applications for senior care, sleep analysis and overall wellness tracking. Our system works for single beds as well as Queen and King size beds with two sleepers.

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

Rifredi: In 2001 my son was born 12 weeks premature and came home with an infant apnea monitor.  The monitor had three wires that were attached to him to monitor his breathing.   The device constantly false alarmed as the wires lost contact.   After 4 weeks of dealing with meaningless alarms, we gave up on the monitor and slept in 4-hour shifts with one of us just watching our son to make sure everything was OK.   I approached my friend Steve Young , who had designed some of the original Apple PowerBooks as well as Cepheid’s Smart Cycler for rapid DNA analysis, and challenged him to come up with a way to monitor someone’s health without the wires and with enough accuracy to be meaningful.

In 2006, Steve and I started working full time on solving this problem and went through several design iterations, but finally came up with a design that was completely non intrusive, easy to set-up and maintain, accurate and affordable.  Our original market case was to help parents monitor their children.  However, as we started talking to potential users, it was clear everyone could benefit from monitoring their health and enabling others to participate in the care continuum.  We shifted our focus to providing tools to help caregivers.  Whether the caregiver is a mother of an infant child, a person helping take care of their elderly parent or a professional caregiver in a senior housing community.

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

Rifredi: We have had a real impact on the quality of life for seniors living in senior care communities and improved the quality of care they receive.  We have had more than one caregiver document that they have reduced falls and pressure sores for people being monitored with our system. Caregivers receive notification when someone gets out of bed, so the caregiver can provide assistance and reduce falls.  The system also provides alerts to caregivers when a resident, who is at risk for a pressure sore, has not moved for more than two hours. While that may not sound too exciting, a fall or pressure sore for an elderly person can be life threatening (Christopher Reeves died of complications from a pressure sore) and in most cases results in significant co-morbidity issues.

Other people use the system to track wellness through sleep quality.  It has become a tool to help them objectively quantify the impact of diet, exercise and stress on their sleep and health.  For example, we track your heart rate over the night, and in the morning you can get immediate feedback on how alcohol impacts your heart rate.  People are amazed to see how a couple glasses of wine can make their resting heart rate jump by 10-20% or more and how their sleep quality declined—even when they feel like they had a good night sleep.

Q: What makes it different, sets it apart?

Rifredi: Our secret sauce is in the design of the system—making it completely touch-free and simple to use. There are no straps, headbands, or patches to wear, and you don’t have to remember to turn anything on. Also, your personal health data is continuously and automatically sent your secure web account so that you can build a health profile every night without have to do anything.  You can then access the information from anywhere on any Internet device.

Finally, our clinical studies show that while we provide a superior user experience by removing the need to attach anything to your body, our system is highly accurate for medical applications.  The system is registered as an FDA class I device and we will seek a 510(K) Class II certification in 2012.

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

Rifredi: Today we are focused on helping caregivers and seniors in senior residential communities manage their health.  Over time we will develop fitness and wellness applications, which will integrate more sensors into our platform.  Our vision is to turn every bed into a smart bed, so that we can all take more control over managing our health.

Q: Anything else you’d like to say?

Rifredi: Someday our kids are going to say, “you slept in a bed for 1/3 of you life and it didn’t tell you anything about your health?  And you called that the information age?”   While Internet enabling beds with biometric sensors may seem odd today, at BAM, we firmly believe that smart beds are going to be as pervasive as smart phones.

Product: Touch-free Life Care (TLC) system
Platform: web
Price: Consumer pricing to be announced.

(If you are a “toolmaker” and want to participate in this series, contact Rajiv Mehta at 

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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
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

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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
Platform: iPhone & Web
Price: free

(If you are a “toolmaker” and want to participate in this series, contact

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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
Platform: iPhone
Price: $4.99

(If you are a “toolmaker” and want to participate in this series, contact

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