Tag Archives: breakout discussion

QSEU14 Breakout: An Imaging Mind

Today’s post comes to us from Floris van Eck. At the 2014 Quantified Self Europe Conference Floris led a breakout session on a project he’s been working on, The Imaging Mind. As imaging data become more prevalent it is becoming increasingly important to discuss the social and ethical considerations that arise when your image it stored and used, sometimes without your permission. As Floris described the session,

The amount of data is growing and with it we’re trying to find context. Every attempt to gain more context seems to generate even more imagery and thus data. How can we combine surveillance and
sousveillance to improve our personal and collective well-being and safety?

We invite you to read Floris’ great description of the session and the conversation that occurred around this topic then join the the discussion on our forum.

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Imaging Mind QSEU Breakout Session
by Floris Van Eck

Imaging Mind Introduction
Imaging is becoming ubiquitous and pervasive next to being augmented. This artificial way of seeing things is quickly becoming our ‘third eye’. Just like our own eyes view and build an image and its context through our minds, so too does this ‘third eye’ create additional context while building a augmented view through an external mind powered by an intelligent grid of sensors and data. This forms an imaging mind. And it is also what we are chasing at Imaging Mind. All the roads, all the routes, all the shortcuts (and the marshes, bogs and sandpits) that lead to finding this imaging mind. To understand the imaging mind, is to understand the future. And to get there we need to do a lot of exploring.

The amount of available imagery is growing and alongside that growth we try to find context. Every attempt to gain more context, seems to generate even more imagery and thus data. We are watching each other while being watched. How can we combine surveillance and sousveillance to improve our personal and collective wellbeing and safety? And what consequences will this generate for privacy?

Quantified Selfie
With about 15 people in our break-out session it started with a brief presentation about the first findings of the Imaging Mind project (see slides below). As an introduction, everyone in the group was then asked to take a selfie and use it to quickly introduce themselves. One person didn’t take a selfie as he absolutely loathed them. Funnily enough, the person next to him included him on his selfie anyway. It neatly illustrated the challenge for people that want to keep tabs on online shared pictures; it will become increasingly difficult to keep yourself offline. This leads us to the first question: What information can be derived from your pictures now (i.e. from the selfies we started with)? If combined and analyzed, what knowledge could be discovered about our group? This was the starting point for our group discussion.

Who owns the data
Images carry a lot of metadata and additional metadata can be derived by intelligent imaging algorithms. As those algorithms get better in the future, a new context can be derived from them. Will we be haunted by our pictures as they document more than intended? This lead to the question “who uses this data?” People in the group were most afraid of abuse by governments and less so by corporations, although that was still a concern for many.

People carrying a wearable camera gather data of other people without their consent. Someone remarked that this is the first time that the outside world is affected. Wearable cameras that are used in public are not about the Quantified Self, but about the ‘Quantified Us’. They are therefore not only about self-surveillance, but they can be part of a larger surveillance system. The PRISM revelations by Edward Snowden are an example of how this data can be mined by governments and corporations.

Context
How are wearable cameras different from omnipresent surveillance cameras? The general consensus here was that security cameras are mostly sandboxed and controlled by one organisation. The chance that its imagery ends up on Facebook is very small. With wearable devices, people are more afraid that people will publish pictures on which they appear without their consent. This can be very confronting if combined with face recognition and tagging.

One of the things that everyone agreed on, is that pictures often give a limited or skewed context. Let’s say you point at something and that moment is captured by a wearable device. Depending on the angle and perspective, it could look like you were physically touching someone which could look very compromising when not placed in the right context. Devices that take 2,000 pictures a day greatly increase the odds that this will happen.

New social norms
One of the participants asked me about my Narrative camera. I wasn’t the only one wearing it, as the Narrative team was also in the break-out session. Did we ask the group for permission to take pictures of them? In public spaces this wouldn’t be an issue but we were in a private conference setting. Some people were bothered by it. I mentioned that I could take it off if people asked me, as stated by Gary in the opening of the Quantified Self Conference. This lead to discussing social norms. Everyone agreed that the advent of wearable cameras asks for new social norms. But which social norms do we need? This is a topic we would like to discuss further with the Quantified Self Community in the online forum and at meetups.

Capturing vs. Experiencing
We briefly talked about events like music concerts. A lot of people in the group said that they were personally annoyed by the fact that a lot of people are occupied by ‘capturing the moment’ with low quality imaging devices like smartphones and pocket cameras instead of dancing and ‘experiencing the moment’. Could wearable imaging devices be the perfect solution for this problem? The group thought some people enjoy taking pictures as an action itself, so for them nothing will change.

Visual Memory
Wearable cameras create some sort of ‘visual memory’ that can be very helpful for people with memory problems like Alzheimer or dementia. An image or piece of music often triggers a memory that could otherwise not be retrieved. This is one of the positive applications of wearable imaging technology. The Narrative team has received some customer feedback that seems to confirm this.

Combining Imaging Data Sets
How to combine multiple imaging data sets without hurting privacy of any or all subjects? We talked for a long time about this question. Most people have big problems with mass surveillance and agree that permanently combining imaging data sets is not desirable. But what about temporarily? Someone in the group mentioned that the Boston marathon bombers were identified using footage submitted by people on the street. Are we willing to sacrifice some privacy for the greater good? More debate is needed here and I hope the Quantified Self community can tune in and share their vision.

Quantified Future
One interesting project I mentioned at the end of the session is called called “Gorillas In The Cloud” by Dutch research institute TNO. The goal of the “Gorillas in the Cloud” is a first step to bring people in richer and closer contact with the astonishing world of wildlife. The Apenheul Zoo wants to create a richer visitors’ experience. But it also offers unprecedented possibilities for international behaviour ecology research by providing on-line and non-intrusive monitoring of the Apenheul Gorilla community in a contemporary, innovative way. “Gorillas in the Cloud” provides an exciting environment to innovate with sensor network technology (electronic eyes, ears and nose) in a practical way. Are the these gorillas the first primates to experience the internet of things, surveillance and the quantified self in its full force?

We invite you to continue the discussion on our forum.

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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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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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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.

social1

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.

social2

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.

social3

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.

social4

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.

social5
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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).

social7

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.

social8

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.

social9

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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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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QSEU14 Breakout: Open Privacy

Today’s post comes to us from Laurie Frick. Laurie led a breakout session at the 2014 Quantified Self Europe Conference that opened up a discussion about what it would mean to be able to access all the data being gathered about yourself and then open that up for full transparency. In the summary below, Laurie describes that discussion and her ideas around the idea of living an open and transparent life. If you’re interested in these ideas and what it might mean to live an open and transparent life we invite you to join the conversation on our forum.  

LFrick

Open Privacy
by Laurie Frick

Fear of surveillance is high, but what if societies with the most openness develop faster culturally, creatively and technically?

Open-privacy turns out to an incredibly loaded term, something closer to data transparency seems to create less consternation. We opened the discussion with the idea, “What if in the future we had access to all the data collected about us, and sharing that data openly was the norm?”

Would that level of transparency gain an advantage for that society or that country? What would it take to get to there? For me personally, I want access to ALL the data gathered about me, and would be willing to share lots of it; especially to enable new apps, new insights, new research, and new ideas.

In our breakout, with an international group of about 21 progressive self-trackers in the Quantified Selfc community, I was curious to hear how this conversation would go. In the US, data privacy always gets hung-up on the paranoia for denial of health-care coverage, and with a heavy EU group all covered with socialized-medicine, would the health issue fall away?

Turns out in our discussion, health coverage was barely mentioned, but paranoia over ‘big-brother’ remained. The shift seemed to focus the fear toward not-to-be-trusted corporations instead of government. The conversation was about 18 against and 3 for transparency. An attorney from Denmark suggested that the only way to manage that amount of personal data was to open everything, and simply enforce penalizing misuse. All the schemes for authorizing use of data one-at-a-time are non-starters.

“Wasn’t it time for fear of privacy to flip?” I asked everyone, and recalled the famous Warren Buffet line “…be fearful when others are greedy and greedy when others are fearful”. It’s just about to tip the other way, I suggested. Some very progressive scientists like John Wilbanks at the non-profit Sage Bionetworks are activists for open sharing of health data for research. Respected researchers like Dana Boyd, and the smart folks at the Berkman Center for Internet and Society at Harvard are pushing on this topic, and the Futures Company consultancy writes “it’s time to rebalance the one-sided handshake” and describes the risk of hardening of public attitudes as a result of the imbalance.

Once you start listing the types of personal data that are realistically gathered and known about each of us TODAY, the topic of open transparency gets very tricky.

  • Time online
  • Online clicks, search
  • Physical location, where have you been
  • Money spent on anything, anywhere
  • Credit history
  • Net-worth
  • Do you exercise
  • What you eat
  • Sex partners
  • Bio markers, biometrics
  • Health history
  • DNA
  • School grades/IQ
  • Driving patterns, citations
  • Criminal behavior

For those at the forefront of open privacy and data transparency it’s better to frame it as a social construct rather than a ‘right’. It’s not something that can be legislated, but rather an exchange between people and organizations with agreed upon rules. It’s also not the raw data that’s valuable – but the analysis of patterns of human data.

I’m imagining one country or society will lead the way, and it will be evident that an ecosystem of researchers and apps can innovate given access to pools of cheap data. I don’t expect this research will lessen the value to the big-corporate data gatherers, and companies will continue to invest. A place to start is to have individuals the right to access, download, view, correct and delete data about them. In the meantime I’m sticking with my motto: “Don’t hide, get more”.

If you’re interested in the idea of open privacy, data access, and transparency please  join the conversation on our forum or here in the comments. 

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QSEU14 Breakout: Mapping Data Access

Today’s post comes to us from Dawn Nafus and Robin Barooah. Together they led an amazing breakout session at the 2014 Quantified Self Europe Conference on the topic of understanding and mapping data access. We have a longstanding interest in observing and communicating how data moves in and out of the self-tracking systems we use every day. That interest, and support from partners like Intel and the Robert Wood Johnson Foundation, has helped us start to explore different methods of describing how data flows. We’re grateful to Dawn and Robin for taking this important topic on at the conference, and to all the breakout attendees who contributed their thoughts and ideas. If mapping data access is of interest to you we suggest you join the conversation on the forum or get in touch with us directly.

Mapping Data Access
By Dawn Nafus and Robin Barooah

One of the great pleasures of the QS community is that there is no shortage of smart, engaged self-trackers who have plenty to say. The Mapping Data Access session was no different, but before we can tell you about what actually happened, we need to explain a little about how the session came to being.

Within QS, there has been a longstanding conversation about open data. Self-trackers have not been shy to raise complaints about closed systems! Some conversations take the form of “how can I get a download of my own data?” while other conversations ask us to imagine what could be done with more data interoperability, and clear ownership over one’s own data, so that people (and not just companies) can make use of it. One of the things we noticed about these conversations is that when they start from a notion of openness as a Generally Good Thing, they sometimes become constrained by their own generality. It becomes impossible not to imagine a big pot of data in the sky. It becomes impossible not to wonder about where the one single unifying standard is going to come from that would glue all this data together in a sensible way. If only the world looked something like this…

data_access

We don’t have a big pot of data in the sky, and yet data does, more or less, move around one way or another. If you ask where data comes from, the answer is “it depends.” Some data come to us via just a few noise-reducing hops away from the sensors from which they came, while others are shipped around through multiple services, making their provenance more difficult to track. Some points of data access come with terms and conditions attached, and others less so. The system we have looks less like a lot and more like this…

mapping_access

… a heterogeneous system where some things connect, but others don’t. Before the breakout session, QS Labs had already begun a project [1] to map the current system of data access through APIs and data downloads. It was an experiment to see if having a more concrete sense of where data actually comes from could help improve data flows. These maps were drawn from what information was publicly available, and our own sense of the systems that self-trackers are likely to encounter.

Any map has to make choices about what to represent and what to leave out, and this was no different. The more we pursued them, there more it became clear that one map was not going to be able to answer every single question about the data ecosystem, and that the choices about what to keep in, and what to edit out, would have to reflect how people in the community would want to use the map. Hence, the breakout session: what we wanted to know was, what questions did self-trackers and toolmakers have that could be answered with a map of data access points? Given those questions, what kind of a map should it be?

Participants in the breakout session were very clear about the questions they needed answers to. Here are some of the main issues that participants thought a mapping exercise could tackle:

Tool development: If a tool developer is planning to build an app, and that app cannot generate all the data it needs on its own, it is a non-trivial task to find out where to get what kind of data, and whether the frequency of data collection suits the purposes, whether the API is stable enough, etc.. A map can ease this process.

Making good choices as consumers: Many people thought they could use a map to better understand whether the services they currently used cohered with their own sense of ‘fair dealings.’ This took a variety of forms. Some people wanted to know the difference between what a company might be capable of knowing about them versus the data they actually get back from the service. Others wanted a map that would explicitly highlight where companies were charging for data export, or the differences between what you can get as a developer working through an API and what you can get as an end user downloading his or her own data. Others still would have the map clustered around which services are easy/difficult to get data out of at all, for the reason that (to paraphrase one participant) “you don’t want to end up in a data roach motel. People often don’t know beforehand whether they can export their own data, or even that that’s something they should care about, and then they commit to a service. Then they find they need the export function, but can’t leave.” People also wanted the ability to see clearly the business relationships in the ecosystem so they could identify the opposite of the ‘roach motel’—“I want a list of all the third party apps that rely on a particular data source, because I want to see the range of possible places it could go.”

Locating where data is processed: Many participants care deeply about the quality of the data they rely on, and need a way of interpreting the kinds of signals they are actually getting. What does the data look like when it comes off the sensor, as opposed to what you see on the service’s dashboard, as opposed to what you see when you access it through an API or export feature? Some participants have had frustrating conversations with companies about what data could fairly be treated as ‘raw’ versus where the company had cleaned it, filtered it, or even created its own metric that they found difficult to interpret without knowing what, exactly, goes into it. While some participants did indeed want a universally-applicable ‘quality assessment,’ as conveners, we would point out that ‘quality’ is never absolute—noisy data at a high sample rate can be more useful for some purposes than, say, less noisy but infrequently collected data. We interpreted the discussion to be, at minimum, a call for greater transparency in how data is processed, so that self-trackers can have a basis on which to draw their own conclusions about what it means.

Supporting policymaking: Some participants had a sense that maps which highlighted the legal terms of data access, including the privacy policies of service use, could support the analysis of how the technology industry is handling digital rights in practice, and that such an analysis could have public policy implications. Sometimes this idea didn’t take the form of a map, but rather a chart that would make the various features of the terms of service comparable. The list mentioned earlier of which devices and services rely on which other services was important not just to be able to assess the extent of data portability, but also to assess what systems represent more risk of data leaking from one company to another without the person’s knowledge or consent. As part of the breakout, the group drew their own maps—maps that either they would like to exist in the world even if they didn’t have all the details, or maps of what they thought happened to their own data. One person, who drew a map of where she thought her own data goes, commented (again, a paraphrase) “All I found on this map was question marks, as I tried to imagine how data moves from one place to the next. And each of those question marks appeared to me to be an opportunity for surveillance.”

What next for mapping?

If you are a participant, and you drew a map, it would help continue the discussion if you talked a little more about what you drew on the breakout forum page. If you would like to get involved in the effort, please do chime in on the forum, too.

Clearly, these ecosystems are liable to change more rapidly than they can be mapped. But given the decentralized nature of the current system (which many of us see as a good thing) we left the breakout with the sense that some significant social and commercial challenges could in fact be solved with a better sense of the contours and tendencies of the data ecosystem as it works in practice.

[1] This work was supported by Intel Labs and the Robert Wood Johnson Foundation. One of us (Dawn) was involved in organizing support for this work, and the other (Robin) worked on the project. We are biased accordingly.

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QSEU14 Breakout: What is Productivity and Why Are We Tracking It?

Today’s post comes to us from Brian Crain. Brian has been testing different productivity methods for over three years. After his great show&tell talk desrcribing how he tracks his own productivity he led a breakout session on the topic. This led to interesting dicussion around how people tracked themselves and what they wish they could track better. You’re invited to read Brian’s description of the session below and then join the discussion on the QS Forum.

Productivity Breakout Session
By Brian Crain

The idea of the productivity breakout session was to discuss three questions that I thought were central to tracking productivity in a systematic way:

  • How do you define productivity?
  • What metrics represent productivity best?
  • How can you use those metrics to track what you actually care about?

These were difficult questions to answer for me and the same turned out to be true for the other participants in the session. It seems, while many people are interested in productivity tracking, few have clearly defined what they mean with the term in the first place. And even when there is a definition, upon closer inspection it is only loosely related to the thing we track.

After the session a participant made the following comment to me, “It’s fascinating to dive into this big unknown.” This was a great summary of our session, but also astonishing in a way. When compared to those new, exciting Quantified Self pursuits be it lifelogging or tracking hormones, productivity tracking seems like a quaint discipline with a long history. Yet, we seem far away from any definite answers.

There was, however, one topic that kept coming up during the discussion: many participants mentioned that, for them, being in a flow state or a state of high cognitive performance was productivity. Strictly speaking, this is nonsensical. Cognitive state might be what makes productivity possible, but surely it is not productivity itself?

Admittedly, since many methods of ‘productivity tracking’ are indirect, focusing on cognitive state might not be so unreasonable. Unfortunately, it seemed that no one had been tracking his cognitive state, so we’ll have to wait for a show&tell to see the promise of this approach. At least, based on my small unscientific sample, tracking flow states might be a great self-tracking project for those hoping to make a big splash at QSEU15.

If someone has ideas about how best to approach a project like that, please get in touch on our forum discussion!

For those who are interested in reading more about the topic, here are some books and resources mentioned during the session:

If you’re interested in discussing productivity tracking we invite you to continue the conversation on the QS Forum.

 

 

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QSEU14 Breakout: Cartographies of Rest

Today’s post comes to us from Josh Berson. Josh is a anthropologist and researcher at the Max Plank Institute. In the fall of 2014 Josh and his colleagues will be embarking on an ambitious research program to explore how we understand and engage in activity and rest. This research project, housed at the Hub at Wellcome Collection, is still in it’s early stages and Josh sought to discuss and gain insight into how people think about measuring activity and rest, as well as how they perceive participatory research projects. You’re invited to read Josh’s description of the session below and then join the discussion on the QS Forum.

jb_qseu14

Cartographies of Rest
by Josh Berson

In the Cartographies of Rest breakout, we were looking for practical guidance on setting up a large study using self-tracking technologies to elicit a synoptic picture of contemporary rhythms of rest and activity in densely inhabited urban spaces. The response was great. We had participants with backgrounds in voice analysis, geovisualization, and the design of participatory public health interventions. Some of these conversations will probably lead to formal working relationships with the Hub at Wellcome Collection, the umbrella project under which Cartographies of Rest is being carried out.

But the key insight we came away with had little to do with the technical apparatus or tracking channels for the study. Rather, it was that we would do well to present the demands we’re making of our research participants not as an inconvenience but as an opportunity to be part of an innovative collective approach to generating knowledge about how we move (and stop moving) through shared space. We need to make sure the design of the study, and the way we communicate the study’s aims to participants, reflects our core conceit, to wit, that activity and rest are social phenomena, and their physiological and behavioral correlates are conditioned by social context, not the other way around. In contrast to previous “reality mining” studies, we’re not looking to study the evolution of a preexisting social network, and in fact we’d been thinking recruitment would need to stratify against too many preexisting relationships among participants, since that might confound the rest:activity timelines they generate as individuals. We were also concerned with how to control for the fact that participating in the study would lead participants to change their behavior.

But if, instead, we focus on creating a community among the participants, and on letting the fact that participating in the study will inevitably change their behavior (through new relationships, through new concern, individually, for how active and restful they’re being) be part of what we’re looking at, we will end up with results that have greater translational value and are truer to the aspirations to self- (as well as social) improvement out of which self-tracking methods originate

If you’re interested in exploring tracking activity and rest we invite you to head to our forum to join the discussion on the QS Forum.

Image by Ian Forrester

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