My last post introduced my PhD project; perhaps it’s useful to look at the central question in a bit more detail.
Can we understand interactions and experiences in museums by looking at information captured via wearable sensors?
On reading that, your first question might well be: why bother? Well, in recent years, the role of emotion in everyday life has developed as a fertile area of study. The shift towards understanding emotion and the role it plays in forming, transforming and framing the relationships between people and places has led to the emergence of critical perspectives collectively known as emotional geographies. In my research, I explore the potential of wearable sensor technology to inform this area, with a specific focus on the museum and its surrounding urban environment. My aim is to develop and test an approach that will explore the potential use of data to understand subjective emotion in real world situations.
My research explores the potential of wearable sensor technology to inform this area, with a specific focus on the museum and its surrounding urban environment Broadening the study to include places outside the museum allows comparisons of how we feel within the museum and other areas of the city. In turn, this allows me to develop and test an approach that will explore the potential use of data to interpret emotion across a range of real world situations.
Having started with the museum, it quickly became apparent that I needed to test across a range of different environments. I’m currently using an Empatica E4 wristband to gather data from across city spaces. Here’s an overview of the device:
My aim for the next few months is to determine how the data collected through this device might be useful for creating a greater understanding how we feel in different places. At this stage, I have three main problems challenges to work on. These are:
What do I mean by ‘emotion’?
There are many different approaches to this, some more contested than others. Traditionally, emotion has traditionally been defined as ‘interrelated, synchronized changes in response to the evaluation of an external or internal stimulus’ (Scherer, 2001, p 7.). This seems like a fairly useful starting point, as it clearly considers the relationships between us, our emotions, places, and environments. However, it’s an approach based on an understanding of emotion as hard-wired, automatic responses that are universally recognisable. This is a view that increasingly coming under fire. Prof. Lisa Feldman Barrett has argued that emotions are not triggered, instead, they are more spontaneous, individualised and driven by our personal experiences. As I continue my testing, I’m aiming to get a better sense of what emotion means in the context of both my project and the debates that surround it.
What is the method for capturing and understanding emotion?
Wearing a wristband is only the first step. I need to interpret that data and map it onto my movements and notes about how I was feeling at a particular time. The next few months is about testing exactly this… I’m trying to get a handle on what the relationship between the data and experienced emotion might be. In this initial stage, I am my own guinea pig. This allows me to test something called ‘intra-subject reliability’ – that’s how reliable the technology is and what patterns emerge when you hone in on one person’s data over a set period of time.
How valid are these methods?
It’s important to form a nuanced understanding of this type of technology and its efficacy. A number of studies have reported issues with signal noise, emotional valence, and the value of physiological data. Whilst studying the use of biometric sensors for monitoring user emotions during educational games, Conati et al. concluded that it is ‘not clear how effectively the sensors can detect emotions that may be expressed more subtly’ (Conati, 2003, p. 1). It’s very likely that I’ll need to address this, but it might also provide the opportunity to show how my personal reflections on experienced emotions (my qualitative data) might reveal some of the shortcomings of the physiological data.
Wrestling with these challenges will enable me to shape my research design, but that’s it for now. I’ll share my progress in a few months time.
Jess Hoare 