Tag Archives: breakout sessions
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.
At its core, Quantified Self is a community-driven effort to extract personal meaning from personal data. Our conferences reflect that by providing opportunities to learn what others are doing in their Quantified Self practice. Through our Show & Tell presentations you get to see first-hand accounts of how data is being collected and put to use in order to understand and investigate personal phenomena, but that’s not all our conference have to offer. In the spirit of collaborative learning we also schedule “Breakout Sessions” alongside our wonderful Show & Tell talks. These sessions, like all our conference programming, are developed and and facilitated by our wonderful attendees. Here’s a preview of just a few of the many fantastic Breakouts we have scheduled.
Title: The Self in Data
Breakout Leader: Sara Watson
Description: In my research on the QS community, I’ve found that we talk a lot about our technical requirements of data, and about how we want to use data. What we don’t often talk about is what it means to know ourselves through data. This breakout is an opportunity to discuss what data tells us about ourselves and how we relate to our data.
Title: On Sleep Tracking
Breakout Leader: Christel De Maeyer
Description: Does self-monitoring with devices like myZeo, Body Media create enough awareness and persuasion to change behavior and to maintain new habits? We would like to use this session to learn and share our experiences.
Title: Tracking breathing as a Unifying Experience
Breakout Leader: Danielle Roberts
Description: During this session we can exchange experiences on the tracking of respiration and tracking and visualising of life group data in general. You’ll have the opportunity to take part in a demo using custom breath tracking wearables and real time visualisation of breath data.
Title: Activity trackers
Breakout Leader: Michael Kazarnowicz
Description: We’ll take a look at the most common activity trackers on the market today. We will look at the trackers (maybe even play around with them hands-on) and compare the functions and the data you can get from them.
Title: QS as a Catalyst for Learning?
Breakout Leader: Hans de Zwart
Description: In this session we will explore whether quantifying yourself can act as a catalyst for learning. Can it speed up the learning process? Can it help us in achieving the holy grail of learning, a personalized tutor? What perverse effects might it have in the context of learning?
The Quantified Self European Conference will be held in Amsterdam on May 11th & 12th. Registration is now open. As with all our conferences our speakers are members of the community. We hope to see you there!