Quantified Self

Quantified Self Movement

 Quantified Self is data aggregation of one unit of being (a conscious one). Data can be varied in different forms, such as data extracted from different body organs, or data from your eating habits, activity, etc. Quantifying self isn’t a new thing, since the development of human brain, we used to take data from a sick person, or our children in cave’s walls. Coming little further in history, I would like to use a more precise example, athletes recorded their eating habits and exercises since official sports have been around. But what is new is the technology that enables us to gather data and use it in a much more organized and precise way. This is a really exciting moment to track your different behaviors and observe how they affect your well-being. We are able to track hyper specific behaviors like we never could before.

 The great thing about this is you having access to your own data, not monster corporations. The movement is all about empowering individuals, create and digest data to effect personal change.

 The inputs from you to the data gatherer could be varied. The most popular ones could be the food consumed or your mood states, your blood oxygen levels, or even your mental and physical performance. It has become a very hot season for self-monitoring and self sensing devices that adopt wearability. These devices usually use different techniques which I shall discuss later.

Brief History

This idea of using wearable sensors and wearable computing started with lifelogging. The idea fostered by Steve Mann, PhD, and a professor at the university of Toronto. In early 1980s, he was the the first person to capture continuous physiological data with first person video from a wearable camera which led to wireless webcam. Lifeloggers typically wear computers in order to record their entire life. In 1998, he started a community called Lifeloggers with 20,000 members.

 But the term quantified self does not appear until 2007. The term was proposed by Wired Magazine editors Gary Wolf and Kevin Kelly as a “collaboration of users and tool makers who share an interest in self knowledge through self tracking.” They later established Quantified Self labs based in California, where they created a world wide community and organized conferences and meetings.


5 Pillars

There are five playing elements or pillars to achieve the best results in the scope of self tracking; big data, sensors, data visualization, gamification and mobile apps.

 1. Big data: “big data is the new oil” is a phrase we been hearing a lot in recent years. Data is what constructs and establishes this movement. Individuals are already creating huge amount of data, but what is about to change is the increasingly availability of technology to use the data in a creative and compelling way. The core of this movement is empowering individuals through better acknowledging their self potentials. Here is an example from David El Achkar who gathered data from personal, work and social activities. http://vimeo.com/78020552

 2. Sensors: Tim O’Reilly, the founder of O’reilly Media, the man behind the most important technology publications pinpoints the next biggest thing would be sensors. He anticipated 2013 in a very clever way back in 2009. Sensors getting smarter, more efficient and cheaper and of course tinnier, which makes them adorable. I will discuss sensors more deeply later on this paper. But let me tell you about BioStamp before that. BioStamp is a collection of sensors that can be applied to the skin like a band-Aid. The sensors collect data such as body temperature, heart rate, brain activity, and exposure to ultraviolet radiation. It uses near field communication, a wireless technology that transfers data to nearby devices.

3. Data Visualization: all data gathered by all these technology must be organized and visualized in a compelling way in order to be better understood and digested. If any impact needs to be made, it must occur on this level. As an example, I would like to use Nicolas Felton’s map of his geolocational data through a month.

4. Gamification: gamification is a technique of using game mechanics in a non-game context in order to increase user engagement, return on investment, timeliness and also learning. A really good example of it would be Khan Academy. It’s involving in the real world qualification where it helps to provide user competition, for user who enjoy comparing their results with others and challenge their mates. This will also help users to beat own records and fight for their capability. A good example of it would be Nike+ where you can challenge your friends and family in order to keep yourself motivated.

5. Mobile: mobile is where everything ends through this system. The widespread mobile and tablet UI’s are the end point for delivering all the values and calculations back to user. Mobile apps are being rock stars in this movement.



To better understand the achievements in sensing technology and the bright future of it, I would like to start with an example, the evolution of microphones. Early telephones relied on carbon microphones, invented by Thomas Edison. Today’s smartphones are based on micro electro-mechanical or MEMS mics. They basically do the same thing, which is converting sound waves into electrical signals. But now they are doing much more than that, amazing things from gesture tracking to tuning into signals from inside the body. They can act as versatile sensors.

 For example: StressSense is a software developed by a Cornell University group, works similar to your ears. It analyzes the subtle changes in pitch, amplitude and frequency as well as your speaking rate resulted by stress. It’s an Android based app which gives you stress levels.

EEG Sensors

 There are also different types of sensors, each built for specific data for self-tracking. I will briefly explain the most popular ones along with examples of them. Electroencephalography (EEG) sensors record the electrical activity among the scalp. They measure voltage fluctuations resulting ionic current flows within the neurons. Although they are in their early developmental stages, but they have attracted a lot of enthusiasts. The biggest problem with this technology is writing a precise algorithm to conclude the best results. Another problem in most cases is that users must be trained.

Melon is a good commercial example for it, it’s a headband that taps into your brain’s inner workings to show you how well is your mental focus. It has a trio of electrodes for sensing brainwaves, a NueroSky chip for filtering electrical noise and Bluetooth 4.0 for communication. It also has an iPhone app which gives you the focus graph.



Accelerometers are another type of popular sensors. They measure proper acceleration associated with weight phenomenon. Misfit Shine is an elegant activity tracker that you can sync with your smartphone by placing it on your phone. The algorithm is written in the way to track cycling and swimming in addition to just steps.


LED Projection

LED projection like microphones acting as highly used sensors. AIRO has developed a wristband that can track nutrition, stress, sleep and exercise. The wrist sensor projects light from an LED into the bloodstream to detect metabolites associated with food intakes on their optical signatures and can distinguish between carbohydrates, fats and proteins.


ECG Sensors

For ECG (Electrocardiography) sensors, Omsignal is a good commercial product as an example. ECG is a transthoracic interpretation of the electrical activity of the heart over a period of time. The company is working on a full apparel line that continuously monitors physical activity, ECG data, and breathing patterns, transmitting that data to an accompanying smartphone app. In addition to raw bio data, the user will receive interpretations of tension levels, or “emotive” states, and subsequent prescriptions for how to improve.


GSR Sensors

Galvanic Skin Response (GSR) sensors implemented in some products measure the electrical conductance of the skin, which varies with its moisture. The sweat glands are controlled by the sympathetic nervous system, that’s why they can interpret nervous states. Back to the point BodyMedia’s armband which is the collection of different sensors including GSR is a activity tracker that measures skin temperature, heat flux and overall movement. Jawbone has recently acquired the company for more than $100 million.



There are many other different sensors like muscle/ electromyography sensors that are being used by the industry, but are not as important as the ones we already discussed in this paper.

During my research I found e-health, an amazing shield for Arduino and Rasberry Pi produced by Cooking Hacks. It allows users to perform biometric and medical applications. 10 different sensors come with the pack: pulse, oxygen in blood (SPO2), airflow (breathing), body temperature, electrocardiogram (ECG), glucometer, galvanic skin response (GSR – sweating), blood pressure (sphygmomanometer), patient position (accelerometer) and muscle/eletromyography sensor (EMG). Really cool, right?



The human mind is not powerful enough to keep track of the countless factors that go into producing an emotional state. Timeline graphical analysis can help to identify potential confounding uncontrolled variables that may be contaminating the results of an experiment. Quantified Self technology enables individuals control all these variables and use it for future experiments.

Quantified Self is not limited to biometrics, every data that can make some impact on our living quality worth analyzing. Now social network companies offering users graphical analysis of their online activity like LinkedIn’s Inmaps.

There is a bright future for the industry and an exciting time for industrial and product designers to get involved in the movement. There has been over 30 million wearable, wireless monitoring devices sold in the US last year according to ABI Reseach analyst Jonathan Collin. This number will increase to 160 million by 2017.













Pdf file of my presentation:

Ardavan Mirhosseini – Research Presentation 1

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