• http://hmi.ewi.utwente.nl/BCIGrandChallenge2012/

Sensors, such as wireless EEG caps, that provide us with information about the brain activity are becoming available for use outside the medical domain. As in the case of physiological sensors information derived from these sensors can be used – as an information source for interpreting the user’s activity and intentions. For example, a user can use his or her brain activity to issue commands by using motor imagery. But this control-oriented interaction is unreliable and inefficient compared to other available interaction modalities. Moreover a user needs to behave as almost paralyzed (sit completely still) to generate artifact-free brain activity which can be recognized by the Brain-Computer Interface (BCI).

Of course BCI systems are improving in various ways; improved sensors, better recognition techniques, software that is more usable, natural, and context aware, hybridization with physiological sensors and other communication systems. New applications arise at the horizon and are explored, such as motor recovery and entertainment. Testing and validation with target users in home settings is becoming more common. These and other developments are making BCIs increasingly practical for conventional users (persons with severe motor disabilities) as well as non-disabled users. But despite this progress BCIs remain, as a control interface, quite limited in real world settings. BCIs are slow and unreliable, particularly over extended periods with target users. BCIs require expert assistance in many ways; a typical end user today needs help to identify, buy, setup, configure, maintain, repair and upgrade the BCI. User-centered design is underappreciated, with BCIs meeting the goals and abilities of the designer rather than user. Integration in the daily lives of people is just beginning. One of the reasons why this integration is problematic is due to view point of BCI as control device; mainly due to the origin of BCI as a control mechanism for severely physical disabled people.

In this challenge (organised within the framework of the Call for Challenges at ICMI 2012), we propose to change this view point and therefore consider BCI as an intelligent sensor, similar to a microphone or camera, which can be used in multimodal interaction. A typical example is the use of BCI in sonification of brain signals is the exposition Staalhemelcreated by Christoph de Boeck. Staalhemel is an interactive installation with 80 steel segments suspended over the visitor’s head as he walks through the space. Tiny hammers tap rhythmic patterns on the steel plates, activated by the brainwaves of the visitor who wears a portable BCI (EEG scanner). Thus, visitors are directly interacting with their surroundings, in this case a artistic installation.

The main challenges to research and develop BCIs as intelligent sensors include but are not limited to:-

• How could BCIs as intelligent sensors be integrated in multimodal HCI, HRI and HHI applications alongside other modes of input control?
• What constitutes appropriate categories of adaptation (to challenge, to help, to promote positive emotion) in response to physiological data?
• What are the added benefits of this approach with respect to user experience of HCI, HRI and HHI with respect to performance, safety and health?
• How to present the state of the user in the context of HCI or HRI (representation to a machine) compared to HHI (representation to the self or another person)?
• How to design systems that promote trust in the system and protect the privacy of the user?
• What constitutes opportune support for BCI based intelligent sensor? In other words, how can the interface adapt to the user information such that the user feels supported rather than distracted?
• What is the user experience of HCI, HRI and HHI enhanced through BCIs as intelligent sensors?
• What are the ethical, legal and societal implications of such technologies? And how can we address these issues timely?

We solicit papers, demonstrators, videos or design descriptions of possible demonstrators that address the above challenges. Demonstrators and videos should be accompanied by a paper explaining the design. Descriptions of possible demonstrators can be presented through a poster.
Accepted papers will be included in the ICMI conference proceedings, which will be published by ACM as part of their series of International Conference Proceedings. As such the ICMI proceedings will have an ISBN number assigned to it and all papers will have a unique DOI and URL assigned to them. Moreover, all accepted papers will be included in the ACM digital library.

Deadline for submission: June 15, 2012
Notification of acceptance: July 7, 2012
Final paper: August 15, 2102

• Femke Nijboer, University of Twente (Researchgroup HMI),The Netherlands
• Mannes Poel, University of Twente (Researchgroup HMI), The Netherlands
• Anton Nijholt, University of Twente (Researchgroup HMI), The Netherlands
• Egon L. van den Broek, TNO (Technical sciences), The Netherlands
• Stephen Fairclough, Liverpool John Moore University, (Research Group Physiological Computing) United Kingdom

Graph 1

It might be interesting to consider that Moodscope is a conscious measure whereas the body blogging system is an unconscious measure. What I mean by this is that emotions are often experienced unconsciously through our psychophysiologicalphysiological systems, such as the sympathetic and parasympathetic nervous system. Emotions may therefore be experienced differently on a physiological level before they become conscious. This does not only introduce a delay in objective and subjective mood but may also change the emotion simply through the process of thinking about it. An emotion may start of as the physical experience of fear (palpitations, increase breathing etc.), but turn into excitement once it is consciously experienced.

There are a number of other incompatibilities between the two tracking systems. Moodscope is a subjective measure that is being used once a day whereas the body blogging system is an arguably objective measure that takes multiple measures in a day. This means that the body blogger is reflecting a wealth of information from different times of the day which is lost when reducing the data to one daily average. The frequency of recording is particularly important in the representation and understanding of the two systems. The continuous recoding of the heart rate introduces a great deal of variation, such as a change in activity between the morning and the afternoon that is not found in Moodscope. This is reflected if we compare the graph 1 above, reflecting a daily average of my heart rate data with the graph 2 below which represents the average heart rate for the hour that I took the Moodscope score. We can see from the two graphs that the second one, graph 2 represents a greater compatibility between my Moodscope score and my heart rate recordings than graph 1 above.

Graph 2

Despite the slightly better match in graph 2, Moodscope and the body blogging system are still not completely compatible. Along with the loss of a lot of heart rate data, through the analysis of only one hour from a full days recording.

The event that appears to be most clearly reflecting something is the 4^th of August where we can see a large spike in the heart rate data and a great dip in the Moodscope recording, but we need to add more meaning to this to see what happened on that day (I will return to this question later). This raises the question of context and how this context is added to the heart rate data. Two ways of doing this is top-down or, bottom-up interpretations. In top-down interpretations we predispose the outcome of the heart rate, i.e. the compatibility with Moodscope; in bottom-up interpretation context is added and build up around the data after the recording has been done. I was using a top-down approach by predisposing my Moodscope score as context for the interpretation for the body blogging data, but the graphs above show that this approach is very limiting. A better way to analyse body blogging data may be a bottom-up approach, such as used in Kiel’s longitudinal recordings that allows adding context to the heart rate after the recordings have been made.

One way of representing this data are,

Heat maps which can become a ‘heart rate skeleton’ to which we can add meaning. Below we see a representation of my data for August as a heat map. The different colours reflect levels of activity in my heart rate. Green reflect low levels of activity, for example when sitting down and reading; yellow reflects lighter activities, such as walking around or having lunch; red reflects higher levels of activity such as high impact exercise. We can see from the heat map below that there is a great deal of variation throughout each day. This richness of experience is lost when we try to reduce the heart rate data to one daily mean score.

If we now look at the 4^th of August we see from the yellow and orange colours that my heart rate was very high, as you would expect if I was out walking, exercising, or doing housework. What’s interesting is that I was asleep at the point of recording and what you see here is my body fighting with the common cold. By looking at unusual events such as this one I can add meaning to the recordings in a bottom-up fashion. This allows me, with time, instant access to these patterns as I become more able to read thesesthese patterns as Ithis one I can add meaning to the recordings in a bottom-up fascion allowing me, with time,.

Using heat maps to represent the continuous stream of body blogging data allows for a greater insight and understanding of changes within the system. Without this the data can become easily boring and tedious. Heat maps enable also a better sharing of experiences with significant others as a mere recording may be seen as meaningless without the knowledge to interpret bpm.

Overall I found that these two systems are not very compatible and that Moodscope did not add to my body blogging experience [1]. I realised that a bottom-up approach to the interpretation of the body blogging data is a lot more fruitful than a to-down approach. I found that the recordings and the sharing of these was meaningless to me as well as others if it was not processed and presented in a more accessible way. But perhaps it’s not a daily recording that we are looking for but the recording of shared experiences that are important and special to us. White-water rafting or New Zealand winning the 2011 world cup could become a new way of sharing an experience with friends, it could also be a new way of recording, and memorising ones experience as it is a trace in the past that informs us of the ups and downs of that particular day. Imagine wearing a sensor on you wedding day… you would be able to capture the butterflies that you experienced on that day in bpm

Many Thanks,

Ute Kreplin

PhD student at Liverpool John Moores University, UK

The work is dated now, but basically I’m drawing a distinction between my understanding of BCI and biocybernetic adaptation. The former is an alternative means of input control within the HCI, the latter can be used to adapt the nature of the HCI. I also argue that BCI is ideally suited certain types of game mechanics because it will not work 100% of the time. I used the TV series “Heroes” to illustrate these kinds of mechanics, which I regret in hindsight, because I totally lost all enthusiasm for that show after series 1.

The original CHI paper for this presentation is available here.

The Body Blogger system as it became known, after a term Steve came up with back in 2009, was only really intended to be used to demonstrate what could be done with the BM-CS5 heart monitors we’d recently purchased. As these devices allowed wireless real-time streaming of multiple heart rate monitors to a single PC there was a number of  interaction projects we wanted to try out and using web services to manage the incoming data and provide a platform for app development seemed the best choice to realise our ideas (see here and here for other stuff we’ve used The Body Blogger engine for).

Having tracked and shared my heart rate for over a year now I’ve pretty much exhausted what I can do with the current implementation of the system which I didn’t spend a whole lot of time developing in the first place (about a day on the core) and so my Amsterdam talk was pretty much a swan song to my experiences. During the summer, I stopped  tracking my heart rate (then fell sick for the first time since wearing the device, go figure I’d of liked to of captured that) so I could work on the next version of the system and loaned out the current one  to Ute who was interested in combining physiological monitoring with a mood tracking service. Ute was also in attendance at Amsterdam to present on her experiences with body blogging and mood tracking (for more information on this see the proposal and first impressions posts).

As with my previous reflections post, I’ve posted rather late so if your interested in what happened at the conference check out the following write-ups: - Guardian, Tom Hume,  and Alexandra Carmichael. The videos of the day I imagine will be online in the coming month or so, so if your interested in the particulars of my talk you’ll need to wait just a little bit longer, though I do cover a few of the topics I talked about during my CHI 2011 (video) talk (on the issues associated with inference and sharing physiological data).

Instead I’m going to briefly cover some of the interesting items I observed during the event, in no particular order they as follows: -

Emergence of Analysis  Middleware

If you’ve  ever bought a self-tracking device such as a pedometer or weight scale in the past year or so it’s probably been tied to a specific online service. As I mentioned in my predictions for 2011 post, self-tracking devices tend to be bound to a singular visualisation and analysis service, typically made by the manufacturer of the device. As such, your rather much dependent on that company remaining interested in servicing your data. And even if they are interested, the visualisations and analyses they produce may not be the ones your particularly interested in using. Hence the importance of such devices supporting data  portability, for without it you can’t explore your own data outside the sandbox you’ve been provided.

As such it was nice to see a range of middleware solutions being demonstrated at the conference which provided visualisation and analysis services. One in particular drew my interest, CommonSense, a data stream management service (e.g. like Pachube) which allows you to build composite sensors from individual data streams as well as provide data analysis to recognise certain states evident in the data streams (e.g.  predicting illnesses from physiological data streams). While I’ve not had time to play around with the platform, I’m very excited about the possibilities it opens for both application developers (R&D prototyping should be a lot easier) and the wider community of self-hackers as it provides a more powerful tool set to explore their data (especially longitudinal).

Glance-ability

This term was brought up during the discussion of the “Personal Data Visualisation” breakout session. How easy is it to observe items of interest in a given data set using a given visualisation format or alternatively what is the glance-ability of the visualisation?

From my own experience in developing visualisations for the body blogging data, drawing any kind of meaning from a surface level inspection of the data is reliant on the format of the visualisation being appropriate for the data set. For example presenting heart rate over a period of seconds, minutes and hours as a time series graph allows all sorts of items of interest to be draw out (e.g. stress responses and  physical activity). However once the period expands into days and months a time series graph falls apart and becomes a convoluted mess. I decided on abstracting my data into a heat map in order to solve this thereby making it easier (at least for me, which I’ll talk about next) to observe longitudinal trends such as my sleep patterns and the effect of life style changes and major events (e.g. Christmas).

I imagine this topic and its many facets has already been covered in depth by the data visualisation community. If I was still working over at Lancaster’s Computing Department I could “ask the audience” for a comment (there were a few  data visualisation experts over there when I was around) but until I know better or find the time to delve into that research space, glance-ability is the new metric with which I’ll be using to evaluate my own visualisations

Being the Expert in Your Self

When designing a visualisation it is important to note that the format will shape the experience of the viewer when they come to inspect the data. I’ve talked about this before with respect to physiological data being used in an interactive setting, where meanings can easily be inferred from even the most basic presentation format (e.g. time series graph) without any meaning actually being there. Take lie-detection games for example. During the conference it was very exciting to see the many different approaches everyone took to visualising their data sets. Unfortunately during several of the talks I found it difficult, if not impossible to see what the presenter saw in their data given the visualisation they had chosen.

When I gave my CHI 2011 talk, it was brought to my attention by an audience member that the heat maps I used, while visually interesting were difficult to interpret and it was only thanks to the spoken narrative I provided that any of it made any sense. For me this raises an interesting question. As I developed the visualisations for my data set, had I become so much of an expert in my data that when I shared my work with others the only person who could actually see anything of interest was indeed myself? I imagine other quantifiers  have run into this problem as well given the ease at which one can slip into over analysing one’s own data.

Essentially the lesson here is when sharing one’s own visualisation take care in explaining the interface as clearly as possible as any trends are likely to be obvious to the creator as they would be the expert in themselves and their interface. Other people on the other hand, such as an audience, need to be guided into the visualisation in order to see as you do. Hopefully I remedied the CHI problem in my talk, which I improved first by introducing my data set and the visualisations I developed in stages (like a tutorial) and by using  a reduced colour palette in order to make areas of interest easier to see.

–

Anyways, all in all I had a great time at the conference. The blend of people from different lines of work and research backgrounds provided for an excellent melting pot of ideas to charge the mind and would highly recommend your attendance even if your only remotely interested in self-tracking; just try to avoid some of the more exotic foods put out for consumption. I’m still regretting trying the weed grass shake.

Last month I gave a presentation at the Annual Meeting of the Human Factors and Ergonomics Society held at Leeds University in the UK.  I stood on the podium and presented the work, but really the people who deserve most of the credit are Marjolein van der Zwaag (from Philips Research Laboratories) and my own PhD student at LJMU Elena Spiridon.

You can watch a podcast of the talk above.  This work was originally conducted as part of the REFLECT project at the end of 2010.  This work was inspired by earlier research on affective computing where the system makes an adaptation to alleviate a negative mood state.  The rationale here is that any such adaptation will have beneficial effects – in terms of reducing duration/intensity of negative mood, and in doing so, will mitigate any undesirable effects on behaviour or the health of the person.

Our study was concerned with the level of anger a person might experience on the road.  We know that anger causes ‘load’ on the cardiovascular system as well as undesirable behaviours associated with aggressive driver.  In our study, we subjected participants to a simulated driving task that was designed to make them angry – this is a protocol that we have developed at LJMU.  Marjolein was interested in the effects of different types of music on the cardiovascular system while the person is experiencing a negative mood state; for our study, she created four categories of music that varied in terms of high/low activation and positive/negative valence.

The study does not represent an investigation into a physiological computing system per se, but is rather a validation study to explore whether an adaptation, such as selecting a certain type of music when a person is angry, can have beneficial effects.  We’re working on a journal paper version at the moment.

Some months ago, I wrote this post about the REFLECT project that we participated in for the last three years.  In short, the REFLECT project was concerned with research and development of three different kinds of biocybernetic loops: (1) detection of emotion, (2) diagnosis of mental workload, and (3) assessment of physical comfort.  Psychophysiological measures were used to assess (1) and (2) whilst physical movement (fidgeting) in a seated position was used for the latter.  And this was integrated into the ‘cockpit’ of a  Ferrari.

The idea behind the emotional loop was to have the music change in response to emotion (to alleviate negative mood states).  The cognitive loop would block incoming calls if the driver was in a state of high mental workload and air-filled bladders in the seat would adjust to promote physical comfort.  You can read all about the project here.  Above you’ll find a promotional video that I’ve only just discovered – the reason for my delayed response in posting this is probably vanity, the filming was over before I got to the Ferrari site in Maranello.  The upside of my absence is that you can watch the much more articulate and handsome Dick de Waard explain about the cognitive loop in the film, which was our main involvement in the project.

Way back in June, I planned to write a post prompted by Kevin Kelly’s talk at the Quantified Self conference in May and a new word I’d heard in an interview with David Brin.  Between then and now, the summer months have whipped by, so please excuse the backtracking – those of you who have seen the site before will have heard of our bodyblogger project, where physiological data is collected on a continuous basis and shared with others via social media sites or directly on the internet.  For instance, most of the time, the colour scheme for this website responds to heart rate changes of one of our bodybloggers (green = normal, yellow = higher than normal, red = much higher than normal – see this for full details).  This colour scheme can be mapped over several days, weeks and months to create a colour chart representation of heart rate data – the one at the top of this post shows a month’s worth of data (white spaces = missing data).

In this context, I was very interested by Kevin Kelly’s concept of Lifestreams as presented at the QS conference.

“What emerges in this new model are Lifestreams. That’s what we curate in the age of the quantified self. We head upstream, and we leave a wake of data behind us. Lifeloggers, who log everything they do, are pioneers in this space… take these exercises to an extreme, and they’re sharing it, as part of the shift from me to we.”

This quote is taken from Ethan Zuckerman’s liveblog of Kelly’s talk, you can read his full transcript here.

The bodyblogger project is a very potent demonstration of a Lifestream in action.  It’s continuously available, always changing and open to interested observers. To date, we have focused on different use cases for the bodyblogger system, such as: out-patients communicating health status to medical staff or family members, sharing physiological experiences such as fitness programmes etc.  However, as Kevin Kelly points out as we are fast approaching a period where monitoring technology is mobile and pervasive, so there will never be a moment when we are not logging activity or interacting with devices that can monitor activity.  Instead of looking for use cases to somehow justify the need for pervasive monitoring, we should perhaps think about collecting and sharing physiological data for its own sake and let the use cases develop from that in a bottom-up fashion.  Back to Kevin Kelly’s talk, quotes from the liveblog:

“These lifestreams intersect with each other and are, in a way, creating a new media. If we organize computation around lifestreams, an intersection between our lifestreams is a communication, an event of some sort. The media we are in is these streams of data… This environment, with data streams and life streams, is the space where we’ll do the work of the quantified self.”

This first sentence seems especially prescient in my opinion.  Bodyblogging and related data (GPS coordinates, physical activity sensors) are a new form of media; like existing media forms, the main purpose is to communicate, unlike existing forms of media, it has the potential to communicate as much information to the individual bodyblogger or lifestreamer as to an audience of interested spectators.  Allow me to elaborate -if the biofeedback paradigm teaches us anything, it is that many physiological processes are not directly available to conscious inspection and making those processes open to inspection changes the relationship between the person and his or her body.  This earlier post from Ute reflected on this kind of mechanic based on her experiences with moodscope and bodyblogging; I also wrote this post about the potential downside of this development.  If you are inside this media or stream of data, your level of self-awareness is enhanced which is one route to augmented self-regulation, whether it be anger management, dieting or reducing your experience of stress.

If your data stream is opened up to other technological systems (your phone, your electronic diary, your car, your house), there is enormous potential for implicit monitoring to inform a plethora of so-called ‘smart’ technology, as they adapt themselves to this data stream.  Your mobile could switch to silent mode if you’re deep in conversation, your stream of emails could be delayed for five minutes if you are highly engaged with document editing, your music player could select relaxing music if you seem particularly agitated.  These systems represent an intersection between bodyblogging and biocybernetic adaptation (i.e. implicit adaptation of software in response to physiology)  - in fact, it posits that biocybernetic adaptation will become the dominant paradigm in HCI provided that sensors and associated software become pervasive in modern life.

We’re currently working on a project to detect interest levels via psychophysiology.  The rationale for this work is based around Herbert Simon’s idea that the “cost” of information provision is attention.  Given that selective attention is finite but the capacity for technology to present information is almost infinite, it stands to reason that a pre-conscious filter is necessary to shield us from multiple distractions and the threat of perpetual information overload.  If we can achieve that function via a bodyblogging system that monitors in order to learn about preferences, and on that basis, to edit your exposure to information on a pre-emptive basis, this is a worthwhile endeavour.  In addition, it would yield a record of this process via the bodyblogging data stream that the user can peruse to their heart’s content.

It is with some reluctance that I wish to draw an analogy between the common garden snail and adopters of this nascent technology (especially as several are good colleagues), but I think we can compare bodyblogs and lifestreams to the trail of slime left by a snail as it passes through the world.  The slime secreted by the snail acts as a record of the journey, it is also a means of transportation for the animal.  Data streams of the physiological day also function as a record of the day, quantifying our physical movements as well as fluctuations in psychological states.  In a future world, where information technology is constantly crying out for attention, it may also ease our path through the information landscape of virtual distractors without losing mental focus.  Given that the original human-tech hybrid, the cyborg, was designed to live in the hostile environmental conditions of space – one wonders if we will require technological augmentation of this kind simply to function in the digital space of the future.

As a second point, bodyblogging and lifestreams are extrapolated in terms of sharing information to others or as Kevin Kelly says facilitating the shift from me into we.  But of course, it doesn’t have to be that way.  Bodybloggers may be motivated to quantify the self as a form of private self-knowledge, not to be shared with other people.  If you wanted to test whether a family member or even a spouse caused your stress levels to increase, you may want to collect that data privately.  Similarly, if bodybloggers are self-monitoring to deal with personal health problems, like incontinence or erectile dysfunction, they would probably not want to post that data to a Facebook page nor would many thank them for doing so.  The point is that bodyblogging doesn’t have to be about sharing data in a public or semi-private domain, the concepts works as a private activity.  But if we do decide to share our data, how does that work?  Kevin Kelly envisions data functioning as an overlay – again this quote is from liveblog of his QS talk:

“What we’ll see very soon is spectacles and glasses that will let us see that data world. It might be a screen we hold up, but we’ll be able to see this overlay of the digital world embedded in the material world. There’s thinking that the digital life is disembodied bits… but it’s really about bits embedded in the physical world.”

With reference to bodyblogging and physiological datastreams, we’re talking about digital embodiment as an overlay on physical reality.  There are two levels to this type of data depending upon whether we draw physical or psychological inferences from the physiological data.  With respect to location and physical movement, there is a ‘bread-crumb’ model here where people could elect to anonymously dump data as they pass through a physical location.  For instance, I could wear my digital glasses and see an accumulated record of every person’s heart rate who had walked up my street or the steps up to my office.  There is no reason why I would want to do that as an individual, but for architects or others with an interest in planning public spaces, they may find a use for these data.  Similarly, with an eye on research into intelligent cities, it would be interesting to obtain dynamic data patterns from groups of people in order to adapt technology on the basis of populations rather than individuals.  As an aside, here is a link to a slideshow from Matt Jones at BERG about people as walking architectures, which touches on this kind of concept, but not for physiological data as such.

A potential strength of representing bodyblogging data as a crowd-sourced aggregation is that this method protects the identity of the individual, provided that the way in which it has been aggregated cannot be reverse-engineered.  In addition, like the slime-trails of snails, these data streams can have limited availability and disappear over a period of time, further protecting the privacy of the individual bodyblogger.

The notion of sharing physiological data always raises the issue of privacy.  I read David Brin’s book “The Transparent Society” some time ago and enjoyed his take on surveillance and privacy, so I was interested by the interview with him here where he talks about the notion of sousveillance, where everyone (citizens, government) is capable of monitoring everyone else.  His primary motivation in putting this idea forward is to counteract the use of surveillance technology by government agencies (and now, at least in the UK, journalists) by making the same technology available to ordinary citizens – so in principle, we could eavesdrop on the eavesdroppers.  This seems to me to be an exercise in damage limitation rather than a genuine need, i.e. the more citizens know about government, the less potential for government to abuse their powers.  With respect to bodyblogging, if we have a scenario where data is being shared in public spaces, we have a sousveillance situation but with the strong positive that nobody can be disadvantaged by the availability of these data if it is impossible to trace it back to the individual.  We use a similar system in many psychology experiments where participants are identified by a number that cannot be traced to any record of their name or contact details.  The primary directive (it seems to me at least) for data sharing should be that the data cannot be used to exploit an individual and of course that the individual maintains full control and consent over the parameters of data sharing (i.e. sharing is not the default position, it must be selected by the user who gives full consent).

Now, how about the second way in bodyblogging can be shared where we are able to infer psychological meaning from the signals?  Here we enter a strange, new world in this case, where digital content equates to a record of our inner life.  For many of us, our inner life of thoughts or feelings is something closely guarded and only shared with friends and family members.  There may be occasions when we can share these data in order to enhance a shared experience, e.g. white-water canoeing, scary fairground rides, bungee-jumping, networked computer games.  These data opens up a parallel world of digital feelings and digital memories, which may diverge in some respects from our literal experiences and recollections.  Again, we can hide our data into crowd-sourced databases in order to protect identities or encrypt the data for sharing with a select few.  The problem is that interpretation of the signals is bound up with our own identities and the context of our relationships with others.  The concept of sousveillance doesn’t provide a strong fit with this case, the potential gain of sharing these data does not seem to be there.  It also seems to me that bodyblogging for psychological insight is not just a private activity, it is also a retrospective one as exemplified by the affective diary project.  One can only imagine the disruptive effect of conducting a conversation with a person whilst viewing a real-time feed of their physiological responses to your own words – fun in a gameplay scenario, not so great if you were asking someone out on a date.

Lifestreaming and bodyblogging are a new form of media that can represent private or shared experiences, the use of this technology has the potential to be beneficial for individuals and groups, regardless of whether users use the resulting data to focus on me or we.

The survey is now CLOSED. Thank you to all our participants.

We invite you to take part in the ARtSENSE Visual Aesthetic Interest Survey. The survey asks you to give subjective ratings, i.e. your thoughts and feelings, towards artworks on a number of scales. The survey is part of the ARtSENSE project which investigates augmented reality supported adaptive and personalized experience in a museum based on processing real-time sensor events.

ARtSENSE tackles a very important problem in the modern usage of ICT in cultural heritage domain. It aims to bridge the gap between the digital world with the physical in a highly flexible way in order to enable a novel and adaptive cultural experience.

You can complete the study online and it shouldn’t take you more than 20 minutes. You will be given feedback about the picture you have rated as most interesting and how it compares to that others have rated most interesting. You can also enter a prize draw for some Amazon vouchers!

To take part in this study (you have to be at least 18) and for further details go to:

http://physiologicalcomputing.net/isurvey/

Many Thanks,

Ute Kreplin

PhD student at Liverpool John Moores University, UK