Topic Archives: Conference

QSEU14 Breakout: Emotive Wearables

Today’s post comes to us from Rain Ashford. Rain is a PhD student, researcher, and hardware tinkerer who is interested in how personal data can be conveyed in new and meaningful ways. She’s been exploring ideas around wearable data and the hardware that can support it. At the 2014 Quantified Self Europe Conference, Rain led a breakout session on Emotive Wearables during which she introduced her EEG Visualizing Pendant and engaged attendees in a discussion around wearing data and devices. 

Emotive Wearables
By Rain Ashford

It was great to visit Amsterdam again and see friends at the 3rd Quantified Self Europe Conference, previously I have spoken at the conference on Sensing Wearables, in 2011 and Visualising Physiological Data, in 2013.

There were two very prominent topics being discussed at Quantified Self Europe 2014, firstly around the quantifying of grief and secondly on privacy and surveillance. These are two very contrasting and provocative areas for attendees to contemplate, but also very important to all, for they’re very personal areas we can’t avoid having a viewpoint on. My contribution to the conference was to lead a Breakout Session on Emotive Wearables and demonstrated my EEG Visualising Pendant. Breakout Sessions are intended for audience participation and I wanted to use this one-hour session to get feedback on my pendant for its next iteration and also find out what people’s opinions were on emotive wearables generally.

I’ve been making wearable technology for six years and have been a PhD student investigating wearables for three years; during this time I’ve found wearable technology is such a massive field that I have needed to find my own terms to describe the areas I work in, and focus on in my research. Two subsets that I have defined terms for are, responsive wearables: which includes garments, jewellery and accessories that respond to the wearer’s environment, interactivity with technology or physiological signals taken from sensor data worn on or around the body, and emotive wearables: which describes garments, jewellery and accessories that amplify, broadcast and visualise physiological data that is associated with non-verbal communication, for example, the emotions and moods of the wearer. In my PhD research I am looking at whether such wearable devices can used to express non-verbal communication and I wanted to find out what Quantified Self Europe attendees opinions and attitudes would be about such technology, as many attendees are super-users of personal tracking technology and are also developing it.

Demo-ing EEG Visualising Pendant

My EEG Visualising Pendant is an example of my practice that I would describe as an emotive wearable, because it amplifies and broadcasts physiological data of the wearer and may provoke a response from those around the wearer. The pendant visualises the brainwave attention and meditation data of the wearer simultaneously (using data from a Bluetooth NeuroSky MindWave headset), via an LED (Light Emitting Diode) matrix, allowing others to make assumptions and interpretations from the visualizations. For example, whether the person wearing the pendant is paying attention or concentrating on what is going on around them, or is relaxed and not concentrating.

After I demonstrated the EEG Visualising Pendant, I invited attendees of my breakout session to participate in a discussion and paper survey about attitudes to emotive wearables and in particular feedback on the pendant. We had a mixed gender session of various ages and we had a great discussion, which covered areas such as, who would wear this device and other devices that also amplified one’s physiological data? We discussed the appropriateness of such personal technology and also thought in depth about privacy and the ramifications of devices that upload such data to cloud services for processing, plus the positive and the possible negative aspects of data collection. Other issues we discussed included design and aesthetics of prominent devices on the body and where we would be comfortable wearing them.

I am still transcribing the audio from the session and analysing the paper surveys that were completed, overall the feedback was very positive. The data I have gathered will feed into the next iteration of the EEG Visualising Pendantprototype and future devices. It will also feed into my PhD research. Since the Quantified Self Europe Conference, I have run the same focus group three more times with women interested in wearable technology, in London. I will update my blog with my findings from the focus groups and surveys in due course, plus of course information on the EEG Visualising Pendant’s next iteration as it progresses.

A version of this post first appeared on Rain’s personal blog. If you’re interested in discussing emotive wearable we invite you to follow up there, with Rain on Twitter, or here in the comments. 

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Laurie Frick: Experiments in Self-tracking

As much as we talk about self-tracking being about health or fitness. . . I think it’s about identity. I think it’s about us. It’s about seeing something meaningful in who we are.

Laurie Frick is a self-tracker and visual artist. It this unique combination that has led her down a path of learning about herself while using the data she collects to inform her artistic work. What started with time and sleep tracking rapidly expanded to included other types of data. In this short talk, presented at the 2014 Quantified Self Europe Conference, Laurie explains how her past experiences have informed her new way of thinking about data, “Don’t hide. Get more.”

If you’re interested in Laurie’s artistic work I highly recommend spending some time browsing the gallery on her website.

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QSEU14 Breakout: Families and Self-tracking

Today’s post comes to use from our friend and co-organizer of the Bay Area QS meetup group, Rajiv Mehta. Rajiv and Dawn Nafus worked together to lead a breakout session that focused on self-tracking in the family setting at the 2014 Quantified Self Europe Conference. They focused on the role families have in the caregiving process and how self-tracking can be used in caregiving situations. This breakout was especially interesting to us because of the recent research that has shed a light on caregivers and caregiving in the United States. According to research by the Pew Internet and Life Project, “39% of U.S. adults are caregivers and many navigate health care with the help of technology.” Furthermore, caregivers are more likely to track their own health indicators, such as weight, diet and exercise. We invite you to read the description of the breakout session below and then join the conversation on the forum.

Families & Self-Tracking
by Rajiv Mehta

In this breakout session at the Amsterdam conference, we explored self-tracking in the context of family caregiving. In the spirit of QS, we decided to “flip the conversation” — instead of talking about “them”, about how to get elderly family members to use self-tracking technologies and to allow us to see their data, we talked about “us”, about our own self-tracking and the benefits and challenges we have experienced in sharing our data with family and friends. These are the key themes that emerged.

Share But Not Be Judged
Feeling like you’re being judged, and especially misjudged, by someone else seeing your data is a very negative experience. People want to feel supported, not criticized, when they open up. Ironically, people felt that reminders and “encouragement” by an app, knowing that it is based on some impersonal algorithm, was sometimes easier to accept than similar statements from family. The interactions we have with family members aren’t neutral “reminders” to do this or that; they’re loaded with years of history and subtext. One participant commented “What I really want is an app that trains a spouse how not to judge.”

Earn The Right
So much is about learning how to earn the right to say something—that’s an ongoing negotiation, and both people and machines have to earn this. Apps screw it up when they try to be overfamiliar, your “friend.” I recalled a talk from the 2013 QS Amsterdam conference of a person publicly sharing his continuous heart rate monitoring, whose boss had noticed that the person’s heart rate had not gone up and demanded to know why he was not taking a deadline seriously! Such misjudgments can kill one’s enthusiasm for sharing.

Myth Of Self-Empowerment
Just because you’re tracking something, and plan to stick to some regimen or make some behavioral change, doesn’t mean you’re actually empowered to make it so. Family members need to be sensitive to the fact that bad data (undesirable results, lack of entries, etc.) may be a “cry for help” rather than an occasion for nagging.

Facilitating Dialog and Understanding
On the positive side, sharing data can lead to more understanding and richer conversations amongst family members. One participant described his occasional dieting efforts, which he records using MyFitnessPal and shares the information with his mother. This allows her to see how he is able to construct meals that fit the diet parameters (and so learn from his efforts), and also to just know that he is eating okay. I described the situation of a friend with a serious chronic disease who was tracking her energy levels throughout the day. In considering whether or not to share this tracking with her family she realized that they had very little appreciation of how up-and-down each day is for her. So, before she’s going to get benefits from sharing continuous energy data, she’s going to have to help her family understand the realities of her condition.

Sense of Control
Everyone felt that one key issue was that the self-tracker feel that s/he is the one making the decision to share the data, and has control over what to share, when to share, and who to share with.

We hope that before people design and deploy “remote monitoring” or “home tele-health” systems to track “others”, they first take the time to share their own data and see what it feels like.

If you’re interested in reading further about technology and caregiving we suggest the recently published report from the National Alliance for Caregiving, “Catalyzing Technology to Support Family Caregiving” by Richard Adler and Rajiv Mehta.

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Steven Dean: A Quantified Sense of Self

At our 2014 Quantified Self Europe Conference, as with all our events, we sourced all of our content from the attendees. During the lead up were delighted to have some amazing interactions with attendees Alberto Frigo and Danielle Roberts, both of whom have been engaged with long-term tracking projects. This theme of “Tracking Over Time” was nicely rounded out by our longtime friend and New York QS meetup organizer, Steven Dean. Steven has been tracking himself off and on for almost two decades. In the talk below, Steven discusses what led him to self-tracking and how he’s come to internalize data and experiences in order to create his sense of self.

Transcript
Quantified Sense of Self
by Steven Dean

Twenty years ago, I was in grad school getting an MFA. I was making a lot of objects that had very strong autobiographical component to it. Some I understood the source of. Many I did not. Continue reading

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Peter Lewis on Meditation and Brain Function

As a long-time meditator, Peter Lewis had a suspicion that meditation could improve brain function, so he conducted a self-experiment and enlisted a few other individuals to help test his hypothesis. By using an arithmetic testing application, a timed meditation app, and an ABA research design he was find out that there was some support for meditation improving his brain function. However, other participant’s results weren’t as supportive. Watch Peter’s talk, presented at the 2013 Quantified Self Europe Conference, to learn more about his process and hear what he learned by conducting this experiment. We also invite you to read Peter’s excellent write up on Seth Robert’s blog: Journal of Personal Science: Effect of Meditation on Math Speed and the great statistical follow-up by our friend Gwern.

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QSEU14 Breakout: Measuring with Muppets

Today’s post comes to us from June Lee and Jennifer Kotler. June and Jennifer are researchers at Sesame Workshop, where they are conducting work exploring children’s media use. Below you can read their description of a breakout session they led on the topic at the 2014 Quantified Self Europe Conference. If you have ideas about measuring media use or want to continue this conversation we invite you to join the discussion in the forum.

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Measuring with Muppets
by June Lee & Jennifer Kotler

The goal of the session was to exchange ideas for ways to measure and track children’s media use across contexts (which include physical spaces such as work vs. school, and social contexts such as with whom they are using media). An ideal device would be a wearable device that’s a “Shazam meets LENA,” which would identify the media content being used, as well as capture the conversation taking place around media use.

Currently, different technologies approximate what we would like to do. For instance, iBeacon is used in shopping malls to track and deliver messages to shoppers; smartwatches could be good for capturing audio; Bluetooth recognition could identify devices that are nearby and partly capture the social context. Different apps, however, don’t use the same system and are difficult to integrate. The main takeaway from the session is that nothing exists yet that does what we would like to do. We would need different apps and systems.

The session generated other useful ideas, such as the asking what parents would like to track in terms of media and their child, and what parents currently track (if they do). Another suggestion was to look at the rare disease or health care community, which is ahead of the curve in terms of tracking and managing child health; Human-Computer Interaction departments or Interaction Design departments at universities could be another good resource. Many agreed that we could start with simple, low-tech approaches: observations and/or manual paper recording. Or do the research in stages, using technology that does exist. In short, we needed to narrow our research questions because the tool we’re looking for does not (yet) exist.

Editor’s note: While doing some research around measurement and children we stumbled upon this great Sesame Street video. Enjoy Elmo singing about the power of measuring!

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QSEU14 Breakout: The Future of Behavior Change

Today’s post come to us from Lukasz Piwek. Lukasz is a behavioral science researcher at the Bristol Business School, University of West England. We were happy to welcome Lukasz, who led an well attended breakout session at the 2014 Quantified Self Europe Conference where conference attendees discussed current issues and new dimensions of behavior change. We encourage you to read his description below (which first appeared on his cyberjournal, Geek on Acid) and join the conversation in our forum

The Future of Behavior Change
by Lukasz Piwek

I gave a short talk, and moderated a breakout discussion, on the future of behaviour change in the context of quantified self approach. It was an inspiring session for me so I summarised my slides here with the discussion that followed.

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First, I highlighted that behaviour change interventions require multidisciplinary approach in order to target a broad range of behaviours related to health (e.g. healthy eating, alcohol & drug use, stress management), sustainability (e.g. travel habits change, energy saving, recycling) or education.

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Health interventions are good example where behaviour change can enormously benefit from smart technology. Currently we have what we call a “sick care” model: when we notice a specific symptoms of illness we share it with our GP, and we get prescription, or we’re referred for more detailed diagnosis. This classic and dominant “sick care” model focuses on relatively passive way to manage illness “after” it occurs.

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However, in the future we can envision ourselves being empowered by smart devices that track various variables in our daily life (such as heart rate, body temperature, activity levels, mood, diet). This variables will get combined in sophisticated analysis merged with our illness history and DNA screening. This continuously provides us with information about “risk factors” for illnesses, which enables us in turn to act and change our behaviour before the onset of a disease. This is what we call a real “preventive care” model of healthcare. Clearly we’re not there yet.

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The key question we discussed was: “what critical features or solutions we are missing to make a breakthrough in behaviour change interventions with quantified self approach?” I started the discussion with giving two possible answers.

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First, we lack long-term user engagement for smart wearables and self-tracking solutions. A recent study showed that 32% of users stop using wearables after 6 months, and 50% – after just over a year. Similarly, there is a high drop rate amongst smartphone apps users: 26% of apps being used only once and 74% of apps are not used more than 10 times (although discussion pointed out that we might not need long-term engagement for many interventions).

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Second, existing devices for self-tracking lack data validity and reliability. Proprietary closed platforms and limited access APIs make it difficult for us scientists to validate how well self-tracking devices measure what they intend to measure. This is a major problem from the perspective of methodology for behaviour change interventions in clinical context.

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In the discussion that followed my presentation, the major reoccurring theme was a lack of robust and reliable feedback provided to users/clients. We agreed that new model of feedback would incorporate such concepts as: narratives, actionable advices on specific consequences of behaviour, and personalised, rapid, relevant data visualisation.

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Another problem highlighted was related to psychological resistance towards smart technologies in our lives, especially in the groups that are not motivated to use wearables/self-monitoring.

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Finally, it seems clear that we’re currently focusing on “exploratory” side of quantified self, and its important we start moving towards more “explanatory” and predictive approach, like in the healthcare example described above. This requires a development of new methodology for n=1 research and creation of data bank of personal analytics. Such bank would enable better generalisation and evaluation of results for larger-scale interventions.

I’m totally on it.

If you’re interested in the intersection of Quantified Self and behavior change we invite you to join the conversation in our forum.

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Debbie Chaves: A Librarian in Numbers

Debbie Chaves is a science and research librarian at Wilfred Laurier University and was interested in understanding her job and the various demands placed on her time. Using methods she’d employed previously she set about tracking different aspects of her work. The data she gathered allowed her to advocate for new changes and policies within her library. In this video, presented at the 2014 Quantified Self Europe Conference, Debbie explains her tracking, what she found, and what she was able to accomplish.

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QSEU14 Breakout: Best Practices in QS APIs

Today’s post comes to use from Anne Wright and Eric Blue. Both Anne and Eric are longtime contributors to many different QS projects, most recently Anne has been involved with Fluxtream and Eric with Traqs.me. In our work we’ve constantly run into more technical questions and both Anne and Eric has proven to be invaluable resources of knowledge and information about how data flows in and out of the self-tracking systems we all enjoy using. We were happy to have them both at the 2014 Quantified Self Europe Conference where they co-led a breakout session on Best Practices in QS APIs. This discussion is highly important to us and the wider QS community and we invite you to participate on the QS Forum.

Best Practices in QS APIs
Anne Wright 

Before the breakout Eric and I sorted through the existing API forum discussion threads for what issues we should highlight. We found the following three major issues:

  • Account binding/Authorization: OAuth2
  • Time handling: unambiguous, UTC or localtime + TZ for each point
  • Incremental sync support

We started the session by introducing ourselves and having everyone introduce themselves briefly and say if their interest was as an API consumer, producer, or both. We had a good mix of people with interests in each sphere.

After introductions, Eric and I talked a bit about the three main topics: why they’re important, and where we see the current situation. Then we started taking questions and comments from the group. During the discussion we added two more things to the board:

  • The suggestion of encouraging the use of the ISO 8601 with TZ time format
  • The importance of API producers having a good way to notify partners about API changes, and being transparent and consistent in its use

One attendee expressed the desire that the same type of measure from different sources, such as steps, should be comparable via some scaling factor and that we should be told enough to compute that scaling factor. This topic always seems to come up in discussions of APIs and multiple data sources. Eric and I expressed the opinion that that type of expectation is a trap, and there are too many qualitative differences in the behavior of different implementations to pretend they’re comparable. Eric gave the example of a site letting people compare and compete for who walks more in a given group, if this site wants to pretend different data sources are comparable, they would need to consider their own value system in deciding how to weight measures from different devices. I also stressed the importance of maintaining the provenance of where and when data came from when its moved from place to place or compared.

On the topic of maintaining data provenance, which I’d also mentioned in the aggregation breakout: a participant from DLR, the German space agency, came up afterwards and told me that there’s actually a formal community with conferences that cares about this issues. It might be good to get better connections between them and our QS API community.

The topic of background logging on smartphones came up. A attendee from SenseOS said that they’d figured out how to get an app that logs ambient sound levels and other sensor data on iOS through the app store on the second try.

At some point, after it seemed there weren’t any major objections to the main topics written on the board, I asked everyone to raise their right hand, put their left over their heart, and vow that if they’re involved in creating APIs that they’d try hard to do those right, as discussed during the session. They did so vow. :)

After the conference, one of the attendees even contacted me, said he went right to his development team to “spread the religion about UTC, oAuth2 and syncing.” He said they were ok with most of it, but that there was some pushback about OAuth2 based on this post. I told him what I saw happening with OAuth2 and a link to a good rebuttal I found to that post. So, at least our efforts are yielding fruit with at least one of the attendees.

We are thankful to Anne and Eric for leading such a great session at the conference. If you’re interested in taking part in and advancing our discussion around QS APIs and Data Flows we invite you to participate: 

You can sign up for the QS Toolmakers List
You can take part in ongoing discussions in the API Forum Thread .
And lastly, you can comment on this particular breakout discussion here

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Justin Timmer: A Lazy Workout

Justin Timmer is a student in human movement science and a fitness instructor. He was interested in exploring what he could do to increase his strength. Rather then starting with a typical strength training program Justin wanted to test if isometric muscle contraction alone could increase his strength. This type of exercise involves just squeezing the muscles without using any weight. He even went so far as to only target one side of his body so that he could test against his non-squeezing muscle groups. In this talk, presented at the 2014 Quantified Self Europe Conference, Justin explains his process and the results of this 4-week experiment.

What did you do?
For four week, I was “squeezing” (isometric contractions) my muscles four times a day. I trained my right leg, abdominals, and right chest and arm.

How did you do it?
During every quiet moment during the day I contracted my muscles as long and hard as possible. I quantified my progress by completing maximum repetitions on a fitness machine every week.

What did I learn?
I learned that in four weeks I almost doubled my force on the right side of my body. But I also learned that this training was going too fast, I got a lot of issues with little unexplained pains in my legs, and rising fluids whenever I contracted my abdominals. Overall I learnt this was a very effective training that was very easy to implement in my daily life.

You can also view Justin’s slides here.

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