Tag Archives: Toolmaker Talk

Toolmaker Talk: Hind Hobeika (Butterfleye)

At a recent QS-themed event at Stanford, 3-time Tour de France winner Greg LeMond described the constant stream of new technologies that make bicycles lighter and more streamlined and that provide ever more detailed monitoring of the cyclists. In contrast, innovation in swimming seems limited to controversial bathing suits. Competitive swimmer Hind Hobeika aims to change that with Butterfleye, as she describes below and in her talk in Amsterdam last fall. She is also inspiring tech entrepreneurship in Lebanon, and is the organizer of the Beirut QS meetup group.

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

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

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

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

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

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

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

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

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

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

Q: What makes it different, sets it apart?

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

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

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

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

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

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

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

Q: Anything else you’d like to say?

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

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

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

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

This is the 16th post in the “Toolmaker Talks” series. The QS blog features intrepid self-quantifiers and their stories: what did they do? how did they do it? and what have they learned?  In Toolmaker Talks we hear from QS enablers, those observing this QS activity and developing self-quantifying tools: what needs have they observed? what tools have they developed in response? and what have they learned from users’ experiences? If you are a “toolmaker” and want to participate in this series, contact Rajiv Mehta at rajivzume@gmail.com.

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Toolmaker Talk: Michael Forrest (Happiness)

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

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

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

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

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

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

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

Q: What makes it different, sets it apart?

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

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

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

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

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

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

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

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

Q: Anything else you’d like to say?

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

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

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

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Toolmaker Talk: Vaibhav Bhandari (Enabling Programmable Self with HealthVault)

A few years ago, there was a lot of hoopla about PHRs (Personal Health Records), and the idea that all of one’s health records would be easily accessible in one place. Things haven’t turned out as rosy, and one major player, Google Health, shut down. However, Microsoft continues to persevere with its version, HealthVault, and Vaibhav Bhandari has written a book explaining how self-trackers can take advantage. Is a book a “tool”?! Surely a book that helps you use a tool qualifies for this series.

Q: How do you summarize Enabling Programmable Self with HealthVault? What is it about?

Bhandari: Enabling Programmable Self with HealthVault is a concise book explaining how Microsoft HealthVault can be used for self-tracking and behavior change. It shows how users can enable automatic updates from well-known fitness devices like Fitbit; how they can collect and analyze their health data; and how application developers can help them with mobile or web-based applications.

The book appeals to a broad set of readers from novice health hackers to professional programmers. It walks the reader through showing how they can easily download information from HealthVault in spreadsheets and track and visualize disparate health data to show interesting health trends about themselves. It outlines the details of the powerful data ecosystem of HealthVault and then shows how to write mobile and web applications using HealthVault APIs.

Microsoft HealthVault is the most prominent example of a personally controlled health record. With its open API, flexibility and connections with multiple health care providers and health & fitness devices, it gives people interested in monitoring their own health an unprecedented opportunity to do their own research on their own data. The other part of the title, “Programmable Self” is a term coined by Fred Trotter, and refers to a combination of Quantified Self and Motivational Hacks.

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

Bhandari: For the past three and a half years, I had been part of the HealthVault engineering team. I guided partners and developers building HealthVault applications, and curated an open source community around HealthVault and its client libraries. For this I created a lot of content and code examples, and it became clear that a book explaining HealthVault and its client libraries would be helpful to many.

Over the same time period Quantified Self, Personal Informatics and Motivational Hacks have seen an uptrend. During high-school and college I used to track a lot of factors like time, work-outs, and expenses on a daily basis.  Through collaborators and colleagues like Fred Trotter I recently got reintroduced to self-tracking. I learned to appreciate the value of tracking and make it more meaningful by associating goals and self experiments and evaluating it in a qualitative context.

I realized these trends very squarely represent the usage scenarios for HealthVault. HealthVault is a great open health platform to aggregate self-quantification data from health & fitness devices and from connected medical institutions via standards like CCD & Blue Button. It does have limitations. There is minimal graphing and statistical capability; however one can export data and use a spreadsheet. And while it has a good input editor for standard data formats, for anything else you must use the programming interface or a spreadsheet.

Q: What impact has your book and HealthVault had for self-trackers? What have you heard from readers and users?

Bhandari: The book was released about a month ago. The feedback I have received in that short time has been quite varied.

One reader noticed a strange correlation between dental visits (data entered automatically through his healthcare provider) and sleep cycle disruption (data entered automatically through Fitbit). Understanding that sleeplessness was caused by anxiety about his frequent dental visits allowed him to curtail the anxiety. Another reader tracking weight, using the Withings scale, and carbohydrate intake and alcohol consumption spotted correlations that has helped him manage his diet to be competitive in national and international triathlons.

In last few weeks I have also received emails from readers who found the book to be a great aid in helping to design clinical trial experiments for graduate research.

Q: What makes the book different, sets it apart?

Bhandari: Currently, Enabling Programmable Self with HealthVault is the only technical book covering Microsoft HealthVault.

Q: What are you doing next? How are you advancing these ideas?

Bhandari: I’m encouraging readers to contribute sharable spreadsheets on the companion website of the book, http://www.enablingprogrammableself.com. One common denominator among health hackers is use of spreadsheets, be it Google spreadsheet or Microsoft Excel. The kind of data being tracked is of long tail nature and no software does a really good job of presenting an interface which can handle and visualize it. Spreadsheets are a useful tool to extend and visualize the varied data involved. Through www.enablingprogrammableself.com, I want readers to be able to share their Health tracking experiences and perhaps create an Open-Source ecosystem of spreadsheets where members of the community can start with a new tracking methodology easily and see some sample data and visualizations of what has worked or not worked for the community members.

Q: Anything else you’d like to say?

Bhandari: Self-quantifiers are mavens of personal informatics, justifying and promoting citizen empowerment with their Healthcare data. We need to promote communities and tools which put the patient in control of their healthcare. Hopefully, Enabling Programmable Self with HealthVault will add a drop to to the ocean by spreading ideas and tools for toolmakers to empower and motivate citizens to be more involved in their day to day health.

Product: Enabling Programmable Self with HealthVault
Website: http://www.enablingprogrammableself.com
Price: $14.99

This is the 14th post in the “Toolmaker Talks” series. The QS blog features intrepid self-quantifiers and their stories: what did they do? how did they do it? and what have they learned?  In Toolmaker Talks we hear from QS enablers, those observing this QS activity and developing self-quantifying tools: what needs have they observed? what tools have they developed in response? and what have they learned from users’ experiences? If you are a “toolmaker” and want to participate in this series, contact Rajiv Mehta at rajivzume@gmail.com.

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Toolmaker Talk: Yoni Donner (Quantified Mind)

There are ever more widgets to measure our physical selves, but how can we measure how well we’re thinking? Yoni Donner is trying to address this need with Quantified Mind. At a recent Bay Area QS meetup he told us how he used his tool to discover that fasting reduced his mental acuity, which was the opposite of what he had expected. Here he tells us what led to his developing Quantified Mind, and the the difficulties of creating such a tool.

Q: How do you describe Quantified Mind? What is it?

Donner: Quantified Mind is a web application that allows users to track the variation in their cognitive functions under different conditions, using cognitive tests that are based on long-standing principles from psychology, but adapted to be repeatable, short, engaging, automatic and adaptive.

The goal is to make cognitive optimization an exact science instead of relying on subjective feelings, which can be deceiving or so subtle that they are hard to interpret. Quantified Mind allows fun and easy self-experimentation and data analysis that can lead to actionable conclusions.

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

Donner: 2-3 years ago I started a discussion group dedicated to meta-optimization. Quickly many suggestions for cognitive improvement came up, and it also became clear that we need to test the hypotheses scientifically to make sense of this huge domain. I then did over a year of pure study of the previous work in measuring cognitive abilities.

I realized that while the existing tests are useful for identifying interindividual differences and detecting pathologies, no solution exists for repeatedly testing the same individual under different conditions, and that I need to collect the psychometric principles that were already established and adapt the tests to the requirements of the new goal: tracking within-person variation in multiple cognitive abilities.

Then there came a long design and planning stage which eventually led me to write a prototype in Python that ran locally. After meeting Nick Winter the real work on making the web application started.

There were many challenges in designing the tests so that they are repeatable and efficient, and trying to minimize practice effects. Much of early stage of the project was spent reading papers and books to identify where I could adapt established tests to my different goals. There was no single formula but one principle that comes up a lot is to change the difficulty of the test dynamically based on the user’s accuracy, to reach a steady state of some fixed accuracy, and apply Bayesian estimation to the parameters of interest. For example, in Digit Span we estimate the level in which the user would get exactly 50% of the trials correct. The reason that our verbal learning test doesn’t use a fixed number of items is that some people would find 10 items too hard and others would find 30 too easy, so any fixed number would waste a lot of their time testing them at an inappropriate level.

We haven’t established validity yet independently from the tests we are based on. This is something that I would very much like to do, but need many test subjects for. In fact, not much is known about the extent to which the intra-individual variance structure resembles the inter-individual structure that has been studied so much. With enough data, we can learn so much!

Now we are at the point where everything is functional, though the UI clearly still needs work. We’ve been live and collecting data for about two months now.

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

Donner: People had far more positive reactions than what I dared hope for. I was afraid that people would say it’s too much work because it’s a kind of tracking where you actually need to spend some time on the tracking itself.

We have over 200 users now and almost 100 hours of testing time, though only a small fraction (about 10) are consistently using the site for self-tracking. Feedback was very constructive and I love it when people just share with me interesting things they learned about themselves.

For example, some things people shared with me: butter seems to be individual since one user had a very significant negative effect from just butter, but another had a pretty big positive effect from butter+coffee; piracetam had a small positive effect; 50gr of 85% dark chocolate increased number of errors; lactose and gluten had small negative effects. I love these individual stories but I think that organizing controlled trials will tell us much more. In any case this is just the beginning – we launched very recently, and don’t have much data yet.

Q: What makes it different, sets it apart?

Donner: It is the only cognitive measurement tool that is designed completely for repeated testing and tracking variation over time. It has more tests (over 25 now) than other cognitive testing sites and covers many cognitive domains (processing speed, motor function, inhibition, context switching, attention, verbal and visuospatial learning and working memory, visual and auditory perception and more coming). The data is collected such that everything is stored, not just aggregate statistics, so we can analyze new questions using existing data. We allow queries and statistical analysis of your results through the site itself, and plan to improve these features even more.

I think this combination makes Quantified Mind unique: (1) careful adaptations of many well-known tests and principles from psychological research; (2) multiple domains covered by tests designed to be repeatable, short, adaptive, efficient and reasonably fun; (3) emphasis placed on data collection and analysis.

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

Donner: I think most people think it’s cool but the barrier to starting your own experiments is high. The main insight from users is that I should probably make it even easier to figure out how to use Quantified Mind to quickly get benefits. I want to add more content like suggested experiments, documentation of what other people did and what they learned, and the science behind all of it, and most of these ideas came from users. Aside from that, there are many features to add such as better UI, more tests (I am working on mood detection now), better tools to access and analyze data.

At a higher level, I want to go forward and develop a science of cognitive optimization. There are many interventions to test and I want to study as many of them as possible using rigorous controlled studies and publish the results. It’s time for cognitive improvement to take a step forward from being astrology-like to being a proper science.

Q: Anything else you’d like to say?

Donner: Thanks for doing this! The QS community is wonderful and I think the future for taking care of our own health, brains and general well-being looks bright – but of course we should measure that, too.

I am always looking for people who share the vision. If you are interested in helping develop Quantified Mind further or helping run experiments, contact me (yonidonner@gmail.com).

Product: Quantified Mind
Website: www.quantified-mind.com
Platform: web
Price: free

This is the 13th post in the “Toolmaker Talks” series. The QS blog features intrepid self-quantifiers and their stories: what did they do? how did they do it? and what have they learned?  In Toolmaker Talks we hear from QS enablers, those observing this QS activity and developing self-quantifying tools: what needs have they observed? what tools have they developed in response? and what have they learned from users’ experiences? If you are a “toolmaker” and want to participate in this series, contact Rajiv Mehta at rajivzume@gmail.com.

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Toolmaker Talk: Caspar Addyman (Boozerlyzer)

Our QS Conferences are organized to maximize discovery and serendipity. The entire program results from us inviting attendees to present and participate. You’re never quite sure what you’ll get, but it’s hardly ever boring! I didn’t know what to expect when Caspar Addyman took the stage in Amsterdam to talk about “Tracking your brain on booze”, but he very quickly grabbed my attention. His talk reminded me that, as Malcolm Gladwell once reported, “How much people drink may matter less than how they drink it.

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

Addyman: The Boozerlyzer is a drinks-tracking app for Android phones. It lets you count your drinks and their calories and tells you your current blood alcohol. Crucially, it also lets you record your mood and play a range of simple games that measure your coordination, reaction time, memory and judgment.

What Boozerlyzer explicitly does not do is tell people how much to drink. We think people would find it patronizing and off-putting. Rather we hope that it will help people get better insight into how drinking affects them.

In addition, if users agree, their data is sent to our servers to contribute to our research on how drink affects people. I’m a researcher with the Center for Brain and Cognitive Development, Birkbeck College, University of London, and this project was started as a way to collect data beyond the artificial setting of a laboratory.

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

Addyman: I originally had the idea back in 2003 while doing my undergraduate psychology degree. I was interested in how to study the affects of recreational drugs. The web technology of the time couldn’t be used when people were out at the pub or club so I didn’t pursue it.

In summer of 2010 I took part in a science & technology hack day in London and the idea occurred to me again, this time using smartphones. So I told a few friends about it. Mark Carrigan, a sociologist at Warwick University, opened my eyes to the more sociological types of data that we could gather. This broadened the aims from my initial very cognitive focus to think about the emotional and social experiences involved with drugs and alcohol. That was at the end of 2010. All that remained then was to invent the app. I’m not really a developer and have been working on this in my spare time so it has taken longer than I’d expected.

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

Addyman: I have been using the app myself for 6 months now and the thing that has surprised me the most is how rapidly the drinks accumulate if I’m out with friends. A few drinks early in an evening, then a couple of glasses of wine with a meal and then more drinks all through the night. Over a particularly sociable weekend I find myself drinking a disturbing amount even though it doesn’t seem that way at the time.

We started our first public beta in December 2011 and have a hundred or so users. I still have to analyse the first batch of data and usage statistics. But, a first look at the data from December and January showed something surprising: the Christmas season seems to ratchet up drinking levels, normalising heavy drinking on into January.  Unfortunately, I don’t think I’ve got enough data to tell if this is real trend.

In terms of direct feedback from users, generally, we’ve had positive reaction to the idea but there are plenty of things we can improve. One of the biggest problems with the enterprise is that our users forget to actually use the app when in the bar, or when they’ve stopped drinking. Also, people are willing to track their drinks and their mood as they go along, as that takes very little time. But at the moment the games take a little too long to play, and the game feedback is a bit too abstract. We aren’t yet giving estimates of drunkeness based on game performance. Here we are in a bit of Catch 22: more compelling feedback ought to be possible once we’ve got a reasonable base set of group data to run some regression analysis but without interesting feedback we have trouble getting people to play the game in the first place.

Q: What makes it different, sets it apart?

Addyman: One big difference between our app and many tools in the personal health world is that our focus is not on behavior change, but instead on data for scientific research and self-learning.

Also, this is an academic, non-commercial project. Our app will always be free. We will never collected any data that could directly identify you nor will we sell any of the data we collect. We believe in open systems, open data and open minds. The code we write is open sourced. The data we collect will be available to anyone that wants to study it.

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

Addyman: The Boozerlyzer is our first app and there are still plenty of improvements to make to it. But, in addition, we want to broaden our scope and apply the same principle to recreational drugs and the effects of various medications.

As an example, I met Sara Riggare Sara Riggare from the Parkinson’s Movement at the Amsterdam QS conference. She pointed out that a version of Boozerlyzer could help Parkinson’s patients track their medication intake and quantify the effects of the medications on mood, coordination, memory, etc. We are starting a collaboration to redesign the app for this purpose.

Meanwhile, my own motivation for starting this project was always to be able to do better research into recreational drugs. This has never been a more pressing concern, and I am hoping that a drugs tracker app can help. Obviously, this is fraught with legal and ethical difficulties so we are having to tread carefully. See here and here for more background on this.

Q: Anything else you’d like to say?

Addyman: We have already benefited greatly from our contact with QS community. The conference was a great inspiration and I wish could get to more of the lively London meet ups. If anyone out there would like to get involved with our project, we’d love to hear from you. Any advice or experience you could lend us would be greatly appreciated. Our project is both open source and open science. We believe in the power of collaboration and so would love to hear from anyone with similar projects in mind.

Product: Boozerlyzer
Website: http://boozerlyzer.net and http://yourbrainondrugs.net
Platform: Android
Price: Free

This is the 12th post in the “Toolmaker Talks” series. The QS blog features intrepid self-quantifiers and their stories: what did they do? how did they do it? and what have they learned?  In Toolmaker Talks we hear from QS enablers, those observing this QS activity and developing self-quantifying tools: what needs have they observed? what tools have they developed in response? and what have they learned from users’ experiences? If you are a “toolmaker” and want to participate in this series, contact Rajiv Mehta at rajivzume@gmail.com.

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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 agrey@somaxis.com

Product: MyoLink platform: MyoBeat (heart) and MyoFit (muscle)
Website: www.somaxis.com (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 rajivzume@gmail.com.

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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 http://www.facebook.com/myfitnesspal or follow us on Twitter at http://twitter.com/myfitnesspal.

Product: MyFitnessPal
Website: http://www.myfitnesspal.com
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 rajivzume@gmail.com.

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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: MoodPanda.com 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
Website: http://moodpanda.com
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 rajivzume@gmail.com 

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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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