Accuracy is fundamental to the process of scientific measurement, we expect our gizmos and sensors to deliver data that is both robust and precise. If accurate data are available, reliable inferences can be made about whatever you happen to be measuring, these inferences inform understanding and prediction of future events. But absence of accuracy is disastrous, if we cannot trust the data then the rug is pulled out from under the scientific method.
Having worked as a psychophysiologist for longer than I care to remember, I’m acutely aware of this particular house of cards. Even if your ECG or SCL sensor is working perfectly, there are always artefacts that can affect data in a profound way: this participant had a double-espresso before they came to the lab, another is persistently and repeatedly scratching their nose. Psychophysiologists have to pay attention to data quality because the act of psychophysiological inference is far from straightforward*. In a laboratory where conditions are carefully controlled, these unwelcome interventions from the real-world are handled by a double strategy – first of all, participants are asked to sit still and refrain from excessive caffeine consumption etc., and if that doesn’t work, we can remove the artefacts from the data record by employing various forms of post-hoc analyses.
Working with physiological measures under real-world conditions, where people can drink coffee and dance around the table if they wish, presents a significant challenge for all the reasons just mentioned. So, why would anyone even want to do it? For the applied researcher, it’s a risk worth taking in order to get a genuine snapshot of human behaviour away from the artificialities of the laboratory. For people like myself, who are interested in physiological computing and using these data as inputs to technological systems, the challenge of accurate data capture in the real world is a fundamental issue. People don’t use technology in a laboratory, they use it out there in offices and cars and cafes and trains – and if we can’t get physiological computing systems to work ‘out there’ then one must question whether this form of technology is really feasible.
A couple of years ago we organised this CHI workshop on meaningful interaction in physiological computing. As much as I felt this was an important area for investigation, I also found the topic very hard to get a handle on. I recently revisited this problem in working on a co-authored book chapter with Kiel on our forthcoming collection for Springer entitled ‘Advances in Physiological Computing’ due out next May.
On reflection, much of my difficulty revolved around the complexity of defining meaningful interaction in context. For systems like BCI or ocular control, where input control is the key function, the meaningfulness of the HCI is self-evident. If I want an avatar to move forward, I expect my BCI to translate that intention into analogous action at the interface. But biocybernetic systems, where spontaneous psychophysiology is monitored, analysed and classified, are a different story. The goal of this system is to adapt in a timely and appropriate fashion and evaluating the literal meaning of that kind of interaction is complex for a host of reasons.
[iframe width=”400″ height=”300″ src=”http://player.vimeo.com/video/25081038″]
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.
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.