Tag Archives: narrative
The 2015 Quantified Self Europe Conference will commence in less than four weeks, bringing together the QS community to share what they’ve been learning with personal data.
Anyone who engages in any sort of self-tracking discovers that the data collected is not a mere recording of some aspect of your life. Rather, engaging with and reflecting on that data can change the way that you relate to an aspect of yourself. Something as simple as getting on a scale each morning can change the way you think about weight. Morris Villarroel has discovered a novel way that this relationship can develop. At this year’s conference, Morris will talk about how using a Narrative camera to keep a visual record of his days, along with detailed notes, has changed his subjective experience of time, “bringing it closer to the present.”
I experienced something similar when I used a spaced repetition system to memorize entries from my daybook. Frequently recalling recent events kept the past distinct and novel. When a month passed, it no longer seemed like a blur, but a container filled with distinct experiences that differentiated itself from any other month.
You can find out more about how Morris gleans value from his lifelog at the 2015 QS Europe Conference. In addition to his show&tell talk, Morris will be leading a breakout discussion on how we can learn more from our lifelogs. We invite you to join us in Amsterdam on September 18th & 19th for two full days of talks, breakout discussions, and working sessions! Early bird tickets are still on sale. Register today for only €149!
Cathal Gurrin is a researcher at Dublin City University and the University of Tsukuba. He’s also an expert in the field of visual and data-driven lifelogging. Since 2006 he’s collected over 14 million passively collected images from different wearable cameras. Add his other sensors and he’s nearing over 1TB per year of self-tracking data. In this talk, presented at our 2014 Quantified Self Europe Conference, Cathal describes what he’s learned over the last eight years and what he’s working on in his research group including search engines for lifelogging as well as privacy and storage issues.
We’re back after missing last week (sorry!) with a bit longer list than usual. Enjoy!
Thoughts on Quantified Self for Modifying Long Term Life Goals by Mark Krynsky. Mark, a member of our QS Los Angeles meetup group, is consistently putting together interesting ideas in the QS space. In this short post he explore how QS tools might be used to understand long-term life goals.
Open Data for Open Lands by Alyssa Ravasio. The value of data isn’t confined to what we can understand about ourselves. There is so much beneficial information out there, especially when it comes to public data. In this post, Alyssa makes the case for protecting and promoting open data ideas and concepts regarding out most precious public spaces – the national parks system.
Art at the Edge of Tomorrow: Lillian Schwartz at Bell Labs by Jer Thorpe. A wonderful biographical piece about Lillian Schwartz, a pioneer in the field of computational art and exploration.
Terms of Service by Michael Kelller and Josh Neufeld. A reporter and nonfiction cartoonist team up to use a comic to tell us about the new world of data and privacy we currently inhabit. Interesting format and compelling content!
Narrative Camera by Morris Villarroel. Morris has been wearing a Narrative personal camera for six months. In this short post he explains what he’s learned and experienced over that time.
Where my 90 Hours of Mobile Screen Time in September Went by Bob Stanke. Bob used an app (Trackify) on his Android phone to track how much time he was spending on his phone and what apps he used the most.
Quitting Caffeine by Andrei-Adnan Ismail. Andrei wasn’t happy with his relationship with coffee and caffeine so he he decide to try and quit. Using tracking and really interesting use of “sprints” to gradually reduce his consumption, Andrei was able to quit. Great post here describing his process and the data he gathered along the way (including how his change affected his sleep).
Twitter Pop-up Analytics by Myles Harrison. Myles takes us through the process of downloading, visualizing, and analyzing personal data from Twitter.
Seven Months of Sleep by Eric Boam. A bit of an old one here, but beautiful and informative nonetheless. Make sure to read the accompanying piece by Eric. (I’m also looking forward to seeing more about this dataviz of his Reporter app data soon.)
My latest effort to visualize my calorie intake and weight loss by reddit user bozackDK. Using data collected from MyFitness pal, bozackDK has created this great visualization of his data. I asked what was learned from making this graph and received this wonderful response:
“I make graphs like these to keep myself going. I need some kind of proof that I’m doing alright, in order to keep myself wanting to go on – and a graph showing that I can (somewhat) stay within my set limits, and at the same time showing that it actually works on my weight, is just perfect.”
Today’s post comes to us from Cathal Gurrin, Rami Albatal, Dan Berglund, and Daniel Hamngren. Cathal and Rami are researchers at Dublin City University and Daniel and Dan work at Narrative, makers of a small lifelogging camera and application. Together, they led an interesting breakout session at the 2014 Quantified Self Europe Conference on photo lifelogging and how to use analytics and computer vision techniques to make sense of vast amounts of photos. You’re invited to read their description of the session below and then join the discussion on the QS Forum.
Photo Lifelogging as Context for QS Practice
by Cathal Gurrin, Rami Albatal, Dan Berglund, and Daniel Hamngren
Thank you to those that came to the breakout session! It was a lively and excellent session with plenty of audience interaction. There were about fifteen participants who had an interest in photo lifelogging.
The session started with a presentation by the session chairs. The Narrative representatives discussed the Narrative clip and their plans for supporting photo lifelogging. This was followed by the DCU team giving an overview of what is possible with photo lifelogging, covering the technical possibilities of what is reasonable to achieve today.
What came across from these presentations is that photo lifelogging is not difficult, but the computer analytics to mine and extract meaning and knowledge is certainly challenging and even state-of-the-art computer vision analytics techniques can often fail to identify the valuable content of photos.
There were a number of core points of discussion, and these were:
Food and Diet. It seemed to the panel and the audience that food and diet monitoring was a key requirement for photo lifelogging and should be the key challenge to be addressed. It was accepted that this is challenging to do, but it was pointed out that recent academic findings suggest that indeed this is possible to achieve in some circumstances. It was pointed out that the most promising technologies to achieve this required a significant investment in time to label food eating photos and there were a number of willing volunteers to help with this activity. If it is possible to release a dataset of food eating photos, then the QS community will be able to help to label the data and build a large amount of training material for machine learning and AI techniques to utilise to build better food and diet monitoring tools. The organisers have taken this point on-board and will return at the next global meet-up with a plan.
Behavior / Lifestyle. Analysing the behaviour of the individual was discussed in terms of data correlations over time and visual day logs. Visual day logs, being the easiest to achieve today is available from the current generation of lifelogging tools, so this is available to anyone to begin to manually explore today. The extraction automatically of temporal patterns of behaviour was suggested as a valuable tool to begin this analysis.
Media Consumption Analytics. It was suggested that analysing the media that a lifelogger consumed could be very valuable both for organisations and as a context source for better quality search. Once again, the discussion came to the conclusion that this was also difficult to achieve, but that it is a worthy goal for the research teams.
Other discussion points included support for and appropriateness of sharing in real-time. Past experiences were shared of when this can work and when it can go wrong. It was also suggested that a ‘loved-one’ reminder tool could be developed as a form of ‘remembering future intentions’, which was pointed out in the lifelogging talk earlier that day as one of the five use-cases for photo lifelogging.
The session ended with the organisers thanking the attendees and the post-session discussions began and continued for thirty minutes, with some continuing to this day. In summary we found out that both food / diet and behaviour / lifestyle were the most important QS-based automatic monitoring tools that should be refined and made available to the QS community.
If you’re interested in photo lifelogging we invite you to join the discussion on the QS Forum.