If there are two truisms in the area of physiological computing, they are: (1) people will always produce physiological data and (2) these data are continuously available. The passive nature of physiological monitoring and the relatively high fidelity of data that can be obtained is one reason why we’re seeing physiology and psychophysiology as candidates for Big Data collection and analysis (see my last post on the same theme). It is easy to see the appeal of physiological data in this context, to borrow a quote from Jaron Lanier’s new book “information is people in disguise” and we all have the possibility of gaining insight from the data we generate as we move through the world.
If I collect physiological data about myself, as Kiel did during the bodyblogger project, it is clear that I own that data. After all, the original ECG was generated by me and I went to the trouble of populating a database for personal use, so I don’t just own the data, I own a particular representation of the data. But if I granted a large company or government access to my data stream, who would own the data?
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 last week’s excellent Bad Science article from The Guardian, Ben Goldacre puts his finger on a topic that I think is particularly relevant for physiological computing systems. He quotes press reports about MRI research into “hypoactive sexual desire response” – no, I hadn’t heard of it either, it’s a condition where the person has low libido. In this study women with the condition and ‘normals’ viewed erotic imagery in the scanner. A full article on the study from the Mail can be found here but what caught the attention of Bad Science is this interesting quote from one of the researchers involved: “Being able to identify physiological changes, to me provides significant evidence that it’s a true disorder as opposed to a societal construct.”
I always harbored two assumptions about the development of physiological computing systems that have only become apparent (to me at least) as technological innovation seems to contradict them. First of all, I thought nascent forms of physiological computing systems would be developed for desktop system where the user stays in a stationary and more-or-less sedentary position, thus minimising the probability of movement artifacts. Also, I assumed that physiological computing devices would only ever be achieved as coordinated holistic systems. In other words, specific sensors linked to a dedicated controller that provides input to adaptive software, all designed as a seamless chain of information flow.
I recently read a paper by Rosalind Picard entitled “emotion research for the people, by the people.” In this article, Prof. Picard has some fun contrasting engineering and psychological perspectives on the measurement of emotion. Perhaps I’m being defensive but she seemed to have more fun poking fun at the psychologists than the engineers, but the central impasse that she identified goes something like this: engineers develop sensor apparatus that can deliver a whole range of objective data whilst psychologists have decades of experience with theoretical concepts related to emotion, so why haven’t people really benefited from their union through the field of affective computing. Prof. Picard correctly identifies a reluctance on the part of the psychologists to define concepts with sufficient precision to aid the work of the engineers. What I felt was glossed over in the paper was the other side of the problem, namely the willingness of engineers to attach emotional labels to almost any piece of psychophysiological data, usually in the context of badly-designed experiments (apologies to any engineers reading this, but I wanted to add a little balance to the debate).
I just watched a TEDMED talk about the iBrain device via this link on the excellent Medgadget resource. The iBrain is a single-channel EEG recording collected via ‘dry’ electrodes where the data is stored in a conventional handheld device such as a cellphone. In my opinion, the clever part of this technology is the application of mathematics to wring detailed information out of a limited data set – it’s a very efficient strategy.
The hardware looks to be fairly standard – a wireless EEG link to a mobile device. But its simplicity provides an indication of where this kind of physiological computing application could be going in the future – mobile monitoring for early detection of medical problems piggy-backing onto conventional technology. If physiological computing applications become widespread, this kind of proactive medical monitoring could become standard. And the main barrier to that is non-intrusive, non-medicalised sensor development.
In the meantime, Neurovigil, the company behind the product, recently announced a partnership with Swiss pharmaceutical giants Roche who want to apply this technology to clinical drug trials. I guess the methodology focuses the drug companies to consider covert changes in physiology as a sensitive marker of drug efficacy or side-effects.
I like the simplicity of the iBrain (1 channel of EEG) but speaker make some big claims for their analysis, the implicit ones deal with the potential of EEG to identify neuropathologies. That may be possible but I’m sceptical about whether 1 channel is sufficient. The company have obviously applied their pared-down analysis to sleep stages with some success but I was left wondering what added value the device provided compared to less-intrusive movement sensors used to analyse sleep behaviour, e.g. the Actiwatch
This article in New Scientist prompts a short follow-up to my posts on body-blogging. The article describes a camera worn around the neck that takes a photograph every 30sec. The potential for this device to help people suffering from dementia and related problems is huge. At perhaps a more trivial level, the camera would be a useful addition to wearable physiological sensors (see previous posts on quantifying the self). If physiological data could be captured and averaged over 30 sec intervals, these data could be paired with a still image and presented as a visual timeline. This would save the body blogger from having to manually tag everything; the image also provides a nice visual recall prompt for memory and the person can speculate on how their location/activity/interactions caused changes in the body. Of course it would work as a great tool for research also – particularly for stress research in the field.
I just watched this cool presentation about blogging self-report data on mood/lifestyle and looking at the relationship with health. My interest in this topic is tied up in the concept of body-blogging (i.e. recording physiological data using ambulatory systems) – see earlier post. What’s nice about the idea of body-blogging is that it’s implicit and doesn’t require you to do anything extra, such as completing mood ratings or other self-reports. The fairly major downside to this approach comes in two varieties: (1) the technology to do it easily is still fairly expensive and associated software is cumbersome to use (not that it’s bad software, it’s just designed for medical or research purposes), and (2) continuous physiology generates a huge amount of data.
For the individual, this concept of self-tracking and self-quantifying is linked to increased self-awareness (to learn how your body is influenced by everyday events), and with self-awareness comes new strategies for self-regulation to minimise negative or harmful changes. My feeling is that there are certain times in your life (e.g. following a serious illness or medical procedure) when we have a strong motivation to quantify and monitor our physiological patterns. However, I see a risk of that strategy tipping a person over into hypochondria if they feel particularly vulnerable.
At the level of the group, it’s fascinating to see the seeds of a crowdsourcing idea in the above presentation. Therefore, people self-log over a period and share this information anonymously on the web. This activity creates a database that everyone can access and analyse, participants and researchers alike. I wonder if people would be as comfortable sharing heart rate or blood pressure data – provided it was submitted anonymously, I don’t see why not.
There’s enormous potential here for wearable physiological sensors to be combined with self-reported logging and both data sets to be combined online. Obviously there is a fidelity mismatch here; physiological data can be recorded in milliseconds whilst self-report data is recorded in hours. But some clever software could be constructed in order to aggregate the physiology and put both data-sets on the same time frame. The benefit of doing this for both researcher and participant is to explore the connections between (previously) unseen patterns of physiological responses and the experience of the individual/group/population.
For anyone who’s interested, here’s a link to another blog site containing a report from an event that focused on self-tracking technologies.
I’ve just returned from a summer school on pervasive adaptation organised under the PERADA project. As preparation for my talk, I was asked to identify some future applications for physiological computing. I drew from an idea first articulated by Ros Picard that exposure to quantifiable, objective feedback about emotional states could serve an educational purpose – to aid awareness and self-regulation. Thinking about a future time when wearable sensors are standard and wirelessly connected to phones/PDAs/laptops, I came up with the idea of body blogging. The basic notion here is that you can review a physiological data set collected over a period of time, perhaps synchronised with a diary, and identify trends that might be of interest.
The big changes, such as sleep/wake cycles, are sort of interesting (did you really have a bad night’s sleep?). If you take regular exercise, you might like to know how your body responded to that session at the gym or how many calories you burned during a run. Changes in physiology that relate to health, such as blood pressure, would be very interesting because hypertension tends to be essentially symptom-free, so the technology is providing a window on a hidden aspect of life. Perhaps I’m a little too curious about this stuff, but I’d like to know what kind of activities or contact with people tended to increase physiological markers of stress.
The central concept is to use a monitoring technology as a tool to extend self-awareness and to make changes (in lifestyle or attitude) that counteract those negative influences that are part-and-parcel of everyday life. When I proudly presented the idea, it struck me as a little “niche” and perhaps a little strange – an impression confirmed by the general apathy of the audience. On the next day, I checked my RSS to Wired and came across this article by Gary Wolf who obviously has thought much more about this kind of stuff than me. He even runs a blog in conjunction with Kevin Kelly dedicated to the topic. Encouraged by this apparent serendipity, I brought up the prospect of body blogging again during my second talk of the summer school – but my audience remained distinctly underwhelmed, even though I sensed a small number thought the term ‘body blogging’ was neat.
As part of the health psychology module I teach, I’ve come across research on allostatic load (AL). This is a concept from stress research developed by Bruce McEwen among others; in essence, AL represents the temporal characteristics of how the body responds to a stressor (i.e. the magnitude of the response, recovery time). As you may imagine, high stress reactivity with a slow recovery rate is bad for health. In fact, McEwen and Seeman linked AL to the concept of biological aging – people with higher AL have bodies that age at a faster rate than their chronological rate (and tend to suffer from poor health as a direct consequence). Here’s an article explaining the application of this approach to the effects of socioeconomic status on health. There are several markers of AL including: blood pressure, hip:waist ratio, the hormone cortisol, ratio of high-to-low density lipids (see previous link for more examples).
Which is an extremely long-winded way of wondering if body blogging could help people to track their AL and biological age – and to allow them to develop strategies and habits that minimise the impact of everyday stress on health. The current conception of AL relies heavily on measures taken from plasma samples, so perhaps that is a limiting factor. On the other hand, one problem with trying to sustain healthy lifestyle choices is the absence of clear, unequivocable feedback – so perhaps there is some hope for the concept of body blogging after all.