I am very interested in the Quantified Self conference to be held in Mountain View, California, May 28 and 29.  While I have attended just a few of the in-person meet-ups, which were engaging and intriguing events, I have followed the blog and tweet stream closely.  These events feature short presentations about practices, prototypes, and products that record information about our own behavior and activity.  It is great to hear that, as the meet-ups grew to become very large, a conference has been organized to accommodate the demand and growing interest in the intersection of sensors and other devices with medical and personal self-monitoring.  Using a variety of devices, our lives can now create detailed inscriptions that illuminate our behavior and patterns with novel clarity and detail. (See: http://quantifiedself.com/conference/)

I plan to attend.  I will speak on Sunday morning at a 10:30 breakout session on using social media network analysis to map personal and collective social media spaces.

Quantifying the “Quantified Self” discussion in Twitter: Here is a map of the connections among the people who recently tweeted the string “quantified self” on May 25th, 2011.

Click for larger image.

The top most between participants in this graph were:

@timoreilly
@quantifiedself (-1)
@harscoat (-)
@edyson
@qsparis
@egadenne (-4)
@agaricus (-)
@brunoaziza
@adriana872
@chloester

Bolded users also appeared in a previous map made earlier in the year, on January 20, 2011, which looked like this:

This is the list of the most “between” users in this network on January 20th, 2011:

The most between participants in this graph are: @quantifiedself, @egadenne, @harscoat, @genomera, @neufit, @jxa, @agaricus, @emergentorder, @bulletproofexec, @2healthguru.

The topics discussed in the quantifiedself tweet stream can be rendered as a network graph based on words that co-occur:

Program on Networked Governance CCCSN Seminar 2009-03-02: Tanzeem Choudhury from Networked Governance.

This is a very informative lecture at Harvard from Tanzeem describing her Dartmouth group’s work to create, deploy and interpret the data from mobile inter-personal sensors.

After a lovely dinner you order a third glass of wine, take a sip and your mobile phone rings.

Its your health insurance company’s computer. You take the call.

“We’re delighted to see you are enjoying yourself this evening!” the cheerful voice synthesis exclaims.

“Please note, however, that you are now at the policy limit for blood alcohol level. To purchase a 24 hour waiver that will allow you to exceed this limit, press 1. A fee of $20 will be charged to your monthly co-payment invoice. To decline waiver coverage, press 2. Please note that if you decline coverage your blood alcohol level must drop under the policy limit within 45 minutes. Continued elevated blood alcohol above your policy limit will constitute breach of your coverage agreement and you may loose all coverage for alcohol related illnesses including injuries received from accidents that may be caused by impairment.”

With a paid-for glass of wine on the table, many people may just go along with the offer (their judgment may already be somewhat impaired).

Real-time health monitoring is an application that is rapidly developing.  Our mobile devices are likely to play a central role in enabling a wide scale real-time continuous monitoring of health related data.  The Quantified Self meet-up is exploring many aspects of this space, but discussion of ways large institutions could use this data has not been a large focus so far.

Medical compliance is a huge issue: most of us do not take prescribed medicines the way we are “supposed to” – skipping doses, doubling up, or quitting a series of medications once symptoms subside.  A real-time assessment of our blood (among other measures) would go a long way to reducing the costs of illness and treatment.  In a world of scarce medical resources, can competitive societies allow their members to be slothful and subsequentially expensive to maintain?

The Intel Health Guide is a good example of this emerging trend, although still in a “desktop” form factor.

Intel Health Guide Device

Other examples can be found in the iPhones app store where health and navigation and travel categories of applications are starting to collide.