Tag Archives: devices
A month ago we showed you what we thought was the quintessential example of how Quantified Self is becoming more of a mainstream activity. During a trip to the Apple store we identified over 20 different Quantified Self devices. Another outing led me into one of the largest consumer electronics stores in the US: Best Buy.
Here, I counted over 25 different tracking devices on the shelves. I’ve split them into three categories here so you can get a sense of just how many different devices are available. With a bit of internet sleuthing I also found that additional devices are available at different stores so you might see something different in your local Best Buy.
Another breakout session preview for the upcoming QS conference: feel free to connect with the leaders in the comments!
Measuring cognitive functions is difficult but provides a much richer understanding of ourselves compared to single-dimension measurements (such as steps taken, heart-rate and weight) that have been the primary focus of the QS community.
One approach to measuring cognitive functions is behavioral: inferring cognitive state from our actions and our ability to respond to stimuli. This lies at the heart of traditional psychometrics, the field of psychology concerned with such measurements. Unfortunately, traditional psychometrics mostly focused on measuring differences between individuals, treating a person as a single data point and comparing them to the general population. In QS, we care about within-person variation: how do our cognitive functions vary at different times and how does this variance relate to our actions? This kind of knowledge can lead us to choose actions that lead to desired cognitive outcomes.
Quantified Mind is a tool designed specifically for measuring within-person variation in cognitive abilities and learning which actions we can take to influence our cognitive functions. In other words, what makes you smarter? It uses short and engaging cognitive tests that are based on many years of academic research but modified to be short, repeatable and adaptive. Quantified Mind can be used by any individual to learn about their own brains, and also invites users to participate in structured experiments that examine common factors such as diet, exercise and sleep.
In the session we will also briefly discuss the ‘Smartphone brain scanner’ — a low-cost portable cognitive measuring device that can be used to continuously monitor and record the electrical activity (EEG) along the scalp in order to determine different states of brain activity in everyday natural settings. The system uses an off-the- shelf low-cost wireless Emotiv EPOC neuroheadset with 14 electrodes, which is connected wirelessly to a smartphone. The smartphone receives the EEG data with a sampling rate of 128 Hz and software on the smartphone then performs a complex real-time analysis in order to do brain state decoding.
Please join us to discuss these topics, and bring your questions and engaged minds!
Mike Winter does a lot of crazy research projects, including building an autonomous motorcycle. But when his daughter was in a bicycle accident a couple of years ago, he started thinking about bike safety. Specifically, he built a device with an Arduino CPU and a few sensors that attaches to your bike and connects with your smartphone. Mike’s invention will let bikes and cars be more aware of each others’ presence, track close calls, and alert the cyclist to any upcoming hazards. In this entertaining video below, Mike shows off the device as well as a pair of his own home-brewed Google goggles. (Filmed by the Bay Area QS Show&Tell meetup group.)
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.
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 firstname.lastname@example.org.
Jakob Larsen and his team at the Mobile Informatics Lab at the Technical University of Denmark have developed a way to build a real-time 3-D model of your brain using a smartphone and the Emotiv EPOC game controller headset. In the Ignite talk below, Jakob describes how the fourteen sensors in this mobile EEG device rival a traditional lab EEG setup, and where he sees this inspiring project going. (Filmed at the QS Europe conference in Amsterdam.)
Hind Hobeika is a swimmer and an engineer from Beirut. She wanted to monitor her heart rate while she was swimming, so she built goggles that sense and display her heart rate in real time. It’s called the Butterfleye Project. In this great talk below, she describes how she designed and assembled the goggles, the challenges she faced, and future prospects for the project.
Ted Punt talks about a device developed by TNO (Dutch Institute for Applied Science) to measure vital signs from people at a distance of up to 10 meters. Heart rate, body motion, and respiration are measured continuously and wirelessly with this device, which should be on the market within a year. He goes into some technical detail and shows prototype video clips in the talk below. (Filmed at Amsterdam QS Show&Tell #3.)
A few months ago I was fatigued and decided to try a more rigorous sleep hygiene routine to see if it would help (it did). To make the experiment fun I thought I’d look for a nifty iPhone app to track the data. After a fairly extensive search I noticed that most of the tools were either highly specialized to a domain (e.g., Sleep On It), or more general purpose (e.g., iLogger). This got me wondering about why there isn’t a universal self-tracking gadget, and what one might look like.
Below I sketch some ideas on what such a beast would need to do to support any data-driven effort. I’d love to know if this makes sense to you, and what you think. (Note: I’m excluding memories for life applications such as Gordon Bell’s MyLifeBits.)
Regardless of the particular domain – sleep, exercise, mood, sex, reading, etc. – is there a set of common tools and sensors that could satisfy the majority of data-driven activities? Overall the goal would be to help answer the types of questions we ask when self-experimenting. That is, to help us discover useful patterns. The kinds of things it would need to “know” include:
- Physiological state: Physical context like pulse and temperature. (What’s going on in your body?)
- Mental state: Cognitive context like thinking patterns, mood, and happiness. (What are you thinking? How do you feel?)
- Location: Spatial context like transitions, surroundings, environment, and activity. (Where are you? What’s going on around you? Where are you going?)
- Incidents: Temporal context like performing exercise, taking medication, attending an event, or eating. (What did you just do?)
- People: Social context (Who are you with? What interactions are you having?)
How would it collect these things? I don’t have all the answers, but I’m thinking of three sources: Direct measurement, inference, and self-reporting. The first category, direct measurement, clearly is collected by sensors, and there is exciting progress on this front. See Measuring Vital Signs From 40 Feet Away or NASA Adapts iPhone to Detect Chemicals, for example.
I’m less sure about the second category, inference, but I’m thinking of tools that deduce some of the above, such as “You’re asleep” (zeo), “You’re at work” (Skyhook), “You’re at a party” (iCal), or “You’re around someone interesting” (MeetMoi).
The final category is the most applicable to self-tracking, but also the most problematic. The closest concept I could find was Wikipedia’s Self-report inventory entry, but the gist is there’s a lot we have to report explicitly. Think of anything you’ve tracked in the above contexts and you’ll come up with plenty of examples, such as “I feel great,” “I drank a beer,” or “I just had an argument with my spouse.” This category is problematic because self-reporting is biased, and because it requires manual input (see Gary’s Which is Better: Automated or Manual?).
I think it’s this last category of data capture that’s generally applicable to most self-tracking needs. Putting on my computer science hat, it seems there’s a fixed set of data types that we’d need. The typical ones include itemized lists (mood from 1 to 5 stars, or yes/no), counts (number of push ups), durations (minutes of exercise), number (weight in pounds), and text notes. All would be time-stamped, of course.
Pros and cons of specialized vs. general
Nothing comes for free, so what would be the trade offs of using a general-purpose data capture device? The pros are that there’d be no reinventing the wheel, everyone would know how to use it, and manufacturing economies of scale would be possible. Also, if we assume a open data access API then any site could use the data, enabling custom uses, novel visualizations, and social applications.
For cons, just look at Alex’s roundup series of “vertical” tracking tools: food, location, fitness, and mood. Because these are specialized to their domain they offer benefits like precise language, customized input (such as eCBT Mood), inferred measurements, and inbuilt information such as a food/calorie database.
Workarounds are possible and would be driven by an experimental design perspective. Self-trackers would set up their experiments by specifying types of measurements, units, frequency of capture (including reminders), and measurement groupings. (An example of the latter is needing to capture a set of daily mood chart data in one shot, like exercise, medications, menses, energy, and agitation level.) By making the gadget’s UI “skinnable” we could generate interfaces automatically for each experiment.
Usage characteristics (or Why your phone should be a Tricorder)
So what would the thing actually look like? In addition to the physical sensors, there are characteristics required for a universal data-tracker to be usable. What comes to mind are ubiquitous availability, rapid manual entry, and notifications to the senses (“What’s that smell? Oh, it’s time to check if I’m procrastinating.”)
Fortunately we have a classic model to start with – the venerable Star Trek Tricorder. It was portable, had powerful recording and analysis capabilities, and could measure things like environmental make-up, life forms, and power sources. Combining the general-purpose and medical variants into your cell phone (the de facto does-it-all device), and adding additional sensors and controls (real buttons, please – much faster than touch screens), wouldn’t we have something that self-trackers would love?
A catalyst for citizen science?
Inspired by Kevin’s conclusion in A Web Page For Every Species, I wonder if having a universal device for self-experimentation could launch self-tracking for all.
As he puts it,
When anyone can buy a hand held species identifier, an amazing transformation will take place: everyone will become a taxonomist.
Could this be true for individual experimentation? Would everyone become a personal scientist? It’s exciting to imagine this kicking off a widespread movement to fulfill the promise of citizen science and social self-improvement. What would be the result, and how might that change how we interact with ourselves, each other, and the world?
What do you think?
- Is such a gadget possible?
- Would it apply to most self-tracking apps, or would it be too general?
- Do you use a general purpose app? How has it worked for you?
- Do you see it drawing people into the experiment-driven life?
(Matt is a terminally curious ex-NASA engineer and avid self-experimenter. His projects include developing the Think, Try, Learn philosophy, creating the Edison experimenter’s journal, and writing at his blog, The Experiment-Driven Life. Give him a holler at email@example.com)
Our first Quantified Self Show and Tell in Amsterdam took place on September 20 at Het Volkskrantgebouw. More than sixty people showed up to attend and some even came from Germany and France! Sebastiaan ter Burg kindly provided us help with the video and photos. All the videos can be found on Vimeo and all photos on Flickr.
Concentration and meditation van be measured with electrodes. Beer van Geer gave a presentation on how he designed an application based on the Neurosky platform, a portable brain interface controllable by meditation.