Tag Archives: LifeLogging
LifeLogging: Personal Big Data by Cathal Gurrin, Alan Smeaton, and Aiden Doherty. A wonderful overview of the field of lifelogging. Special attention is given to how information retrieval plays a role in how we can understand and use our lifelogs.
What happens when patients know more than their doctors? Experiences of health interactions after diabetes patient education: a qualitative patient-led study by Rosamund Snow, Charlottle Humphrey, and Jane Sandall. In this qualitative study, the authors engaged with 21 patients with type 1 diabetes who had developed expertise about their condition. Some interesting findings about how healthcare providers may be uncomfortable with patient who understand themselves and their condition. (Thanks to Sara Riggare for sharing this article with us!)
Internet of You: Users Become Part of the City-as-a-System by Tracy Huddleson. An good look into how wearables and personal technology might have an impact on the public infrastructure, institutions, and spaces.
Welcome to Dataland by Ian Bogost. Not sure how I missed this one piece from late July, but glad I stumbled across it this week. Ian Bogost takes a tour through the actual and imagine implications of the Disney Magic Band. I especially enjoyed the historical context describing the history of futurism at Disney.
Gary Wolf on Cool Tools Show #15. QS co-founder, Gary Wolf, speaks with Mark Frauenfelder and Kevin Kelly on the Cool Tools Podcast about his favorite self-tracking tools and what he’s learned from using them.
My heart rate during Interstellar (via Basis Peak) by Reddit user javaski. An nice use of the BasisRetreiver tool to download and analyze heart rate data from the new Basis Peak device.
Activity Time vs. Device Wear Time by Shannon Conners. Shannon plotted her actual wear time using the BodyMedia Fit against the activity data to show that low activity numbers are probably caused by hotter summer months when wearing the armband caused unwanted tan lines.
“If I had not explored my activity and usage data first to remind me of this usage pattern, I could have created any number of plausible explanations for why my activity levels were so much lower during the hot North Carolina summer months.”
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.
This is Adam Johnson’s third QS talk. Previously he’s discussed the lifelogging tool he developed and uses and how he re-learned how to type in order to combat RSI. In this talk, Adam gives an update to his self-tracking focused on three areas: tracking an long-distance cycling trip, his streamlined lifelogging process, and how he’s using the Lift app to track his habits.
What Did Adam Do?
In general, Adam is dedicated lifelogger who’s been tracking what he’s doing for over a year. Adam cycled 990 miles from Lands End to John O’Groats with his father and brother over 14 days and tracked it along the way. Because he wasn’t able to “lug around his Mac” to complete his regular lifelogging he decided to update his custom system to accept photos and notes. Lastly, he added habit tracking to his daily lifelogging experience by using the Lift app.
How Did He Do It?
Adam tracked his long distance cycling journey by using Google location history and a Garmin GPS unit. He was able to export data from both services in order to get a clear picture of his route as well as interesting data about the trip.
He also updated his lifelogging software so that it could accept photos and notes he hand enters on his phone. The software, available on GitHub, gives him an easy way to track multiple event such as how often he drinks alcohol and how much he has to use his asthma inhaler.
Lastly, Adam tracked the daily habits he wanted to accomplish such as meditating, reading, making three positive observations, and diet, using Lift.
What Did He Learn?
Everything Adam learned is based on his ability to access and export his data for further analysis. From his cycling trip he was able to make a simple map to showcase how far he traveled based on Google location history (which did have some issues with accuracy). He also was able to see that he traveled 1,004 miles, cycled for 90 hours, burned 52,000 calories, but didn’t lose any weight.
Using his updated lifelogging system, he was able to explore his inhaler use and after a visit to the doctor was able to “find out a boring correlation” that a preventative inhaler works and his exercise induced inhaler usage went to almost zero.
Finally, because Lift supports a robust data export, Adam was able to analyze his habit data and began answering questions he was interested in, but aren’t available in the native app experience. He found that seeing a visualization of his streaks as a cumulative graph was inspiring and motivating. He also explored his failures and found that Saturdays, Sundays, and Mondays were the days he was most likely to fail at completing at least one of his habits.
Slides of this talk are available on Adam’s GitHub page here.
Google Location History, Garmin GPS, Lifelogger, Lift, Photos, Notes
Like many of us, James Norris remembers his first kiss. Unlike many of us, he also knows who it was with, where it was, and his age. How does he know this information? When he was 13, he realized that he forgot some detail about his life that he thought was important. To prevent that from happening again, he decided to carry around sticky notes to record important life events and has been doing it ever since. Fast forward 15 years and James has recorded 1,500 “firsts.” Watch this talk, presented at the Washington DC QS meetup group, to hear James talk about the data he collects, and the lessons he’s learned along the way.
Enjoy this week’s reading list. If you’d like to submit something for future What We’re Reading posts we invite you to get in touch!
Data Journalism Needs to Up Its Own Standards by Alberto Cairo. The influx of new data-based journalistic endeavors seems to grow by the day. In this great piece Alberto Cairo presents four suggestions for those practicing that art and science of data-based reporting.
Big Data Should Not be a Faith-Based Initiative by Cory Doctorow. The idea of “big data” as a miraculous fountain of new knowledge is widespread. In this article Cory Doctorow brings to light some of the major concerns about personal data and the true possibility of de-identification.
Data Privacy, Machine Learning, and the Destruction of Mysterious Humanity by John Foreman. This is a long read, but definitely worth the time. If you’re like me you’ll spend the next few hours (day?) thinking about yourself, the various companies and organizations consuming your data, and how your life may (or may not) be shaped by the information you willingly hand over.
Privacy Behaviors of Lifeloggers using Wearable Cameras [PDF] by Roberto Hoyle, Robert Templeman, Steven Armes et al. This research paper paper offers a good glimpse into the the concerns and real behaviors of people using photo lifelogging systems. This is an area we’ve previously explored (see Kitty Ireland’s great write-up about our lifelogging town hall at QSEU13) and we expect to continue discussing.
Battery Life, 6mo Checkup By James Davenport. It may seem odd to have a post about tracking battery life from a laptop here in the Show&Tell section, but this is a really neat post. As part of tracking his laptop battery he also tracked his usage and led to some interesting data about his sleep. (Don’t forget to check out the post that kicked off his battery tracking.)
Bringing My Data Together by John T. Moore. John is on a journey of improving his health and being more active through self-tracking/monitoring. In this post he pulls together some of his most important data, but I also suggest reading his summary of how he got started with self-tracking.
Seven Days of Carsharing by Density Design. Not exactly personal data here, but some beautiful visualizations based on one week of data from the Enjoy, a carsharing service in Milan.
Lee Rogers’ Annual Reports by Lee Rogers. Lee has been tracking different aspects of his life for more than three years. Since 2011 he’s put together Annual Reports detailing his personal data. You can view his 2011, 2012, and 2013 reports on his website.
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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.
Back in 2012 we first heard about a neat little project developed by Stan James called Lifeslice. It’s a simple application that tracks what you’re doing with your computer by taking a photo of you, a screen capture, and current location (all stored locally on your machine). Stan kept working on the project adding tweaks while continuing to use it to track how he used his computer. At our 2013 Quantified Self Europe Conference he shared some of what he’s learned from the data including how much time he spends with his computer in bed, in coffee shops, and other interesting tidbits.
All our videos from our 2013 Quantified Self Europe Conference are now available.
Rob Shields has been wearing a phone around his neck since 2012 in order to take one photo per minute. This persistent lifelogging has come with some technological and social hurdles. At the 2013 Quantified Self Global Conference, Rob explained some of the issues he’s been running into as he nears 300,000 photos. He also talked about the interesting data he’s been able to gather because of this practice, such as understanding who he meets and how he spends his time. Watch his talk below to learn more about his practice and other interesting insights lifelogging has provided him.
Decades before apps, GPS, and even personal computing, people kept track of their lives by writing things down. Kitty Ireland’s grandmother was one of these people. When Kitty stumbled upon her grandmother’s diaries and started to explore the daily entries, she was struck by similarities with her own life and habits. Kitty is a modern-day lifelogger. She tracks places, events, mood – a variety of different personal data streams. Reading the diaries, Kitty saw that her grandmother used her daily entries as logs – tracking the details of where she went, what she ate, even the boys she kissed. Watch this great talk, filmed at the 2013 Quantified Self Global Conference, to see what Kitty discovered, and the lessons she learned.
We’ll be posting videos from our 2013 Global Conference during the next few months. If you’d like see talks like this in person we invite you to join us in Amsterdam for our 2014 Quantified Self Europe Conference on May 10 and 11th.