Physiological Computing is a term that is used to describe any technological system that incorporates physiological data from humans into its functionality or displays these data at the interface.
First of all, what do we mean by physiological data? Tapping on a keyboard or moving a mouse is fundamentally a physiological act, right? What I’m talking about are computers that record quantitative data directly from the human central nervous system. These data include: brain activity (either vascular or electrocortical), patterns of muscle activation, outputs from the cardiovascular or respiratory systems and changes in the electrical conductivity of the skin (skin conductance or galvanic skin response). We are talking about covert physiological activity within the human body.
One thing that characterises physiological computing systems is that systems directly measure these types of data and this can be achieved by attaching electrodes to the person or remotely monitoring heart rate via a smartphone camera.
Some physiological computing systems are designed to provide an alternate mode of communication or input control. Control of the screen cursor via eye movement represents one example. Brain-computer interfaces are the best-known category of system for direct input control wherein the electrocortical activity of the brain is transformed into a repertoire of commands and controls at the interface. This type of communication system depends on intentionality on the part of the user – in other words, the user wants to achieve a specific selection or outcome. This could be to zoom in on a map or move the cursor to a specific location on the screen. These systems were originally designed as an assistive technology but they also offer a number of advantages to the able-bodied user, such as novelty or increasing the communication bandwidth between human and machine.
Other types of physiological computing systems take a different approach. These systems monitor spontaneous changes in physiological activity as the person is engaged with a task. These data are captured and categorised to represent a number of implicit psychological states, such as being happy or sad or awake versus tired. Other systems might monitor the level of mental workload of an aircraft pilot or the degree of engagement exhibited by a person playing a computer game. This category of system involves covert monitoring of the user in order for the system to create a dynamic representation of the person. It is this dynamic representation that can be used to inform a process of intelligent adaptation. In other words, access to the user state grants the technology with a capacity to adapt in a timely and intuitive fashion to changes in the psychological state of the user. For example, the computer could offer help if the user is angry, adjust the cockpit display if mental workload is high or make the game harder if the player is bored. This is a very different way of interacting with technology than the one we are currently used to.
The grand-parent of this category of technology is a therapeutic approach known as biofeedback. During conventional biofeedback, the person is provided with a display of their covert physiological activity and invited to use this display as a form of self-regulation. For example, if someone is anxious or stressed, they could receive feedback of their heart rate and be invited to relax in order to slow down their heart rate, and in doing so, would reduce their feelings of anxiety. The same basic approach where the user is provided with a visualisation of covert physiology in order to aid self-regulation represents another category of physiological computing system. In this case, the user may receive feedback from the system in a delayed form in order to aid reflective thought and promote positive self-regulation. Lifelogging technologies and systems that monitor health indicators fall into this category.
This is a short intro to the topic of this blog written for a casual reader. For an academic treatment of the topic, here’s some further reading (pdfs available on the Publications page):
Fairclough, S.H. 2017. Physiological Computing and Intelligent Adaptation. In Jeon, M. (Eds.) Emotions And Affect in Human Factors and Human-Computer Interaction. Elsevier. 539-556.
Fairclough, S.H. & Gilleade, K. (Eds). 2014. Advances In Physiological Computing. Springer.
Fairclough, S. H. 2009. Fundamentals of physiological computing. Interacting with Computers, 21, 133-145.