Visualising Group Physiology

Hi all,

New to the forums but not to QS (The Body Blogger). Thought I would share some recent work I’ve been doing on visualising group physiology which may be of interest.

Last year I did some consultancy work on a project interested in visualising the biological rhythms of employees at different workplaces based at Liverpool. The project was interested in contrasting the physiological activity of different workplaces for an audience as part of an upcoming art exhibit.

Physiological data (heartbeat rate and motion) was collected from 10 employees, 5 from a local hairdressers and 5 from a game testing lab, for a period of 24 hours. The original exhibit was designed to show the immediate physiological activity of each workplace. Using the original data I’ve created new visualisations which I’ve included below which use aspects of my previous work on visualising physiology to convey workplace activity.

This visualisation organises employees according to their workplace and shows immediate activity as well as each groups past activity .

This visualisation shows long term and short term cardiac activity for all employees.

This visualisation shows long term and short term cardiac activity for a single employee.

As each workplace was radically different from the other, it provided both interesting QS challenges to tackle (e.g. game testers are not allowed mobile phones at work which restricts use of wearable sensors) and for interesting visualisations (e.g. observe the 2 night shift workers at the game tester labs).

While fun to produce, a big issue I found with the visualising this data set was how to convey the rhythm of each workplace which could be readily be interpreted by a non-expert. With the Body Blogger project, I had longitudinal data to provide context for the data (e.g. identifying certain activity patterns such as sleep), allowing me to create more meaningful visualisations, however, here, the nature of the data collected doesn’t provide for much.

In my next attempt at visualising this data set I’ll be looking at combining the signals we collected (heartbeat rate and motion) which will hopefully provide a more meaningful visualisation (e.g. hairdressing involves more physical activity than game testing during work hours, this should show up in the visualisation).

Hope you enjoyed the visualisations, any comments or suggestions please let me know.

  • Kiel

For more info see

I think these are interesting for a number of reasons. Just to pick out one of special interest to us at QS Labs… we’re in a lot of conversations about what putting personal data “in context.” Sometimes the questions that drive these conversations are a bit frustrating. There is a persistent conviction that the most meaningful context for our own data is population data that is taken to contain, at least implicitly, the standard by which individual experience should be evaluated. There are many assumptions in this conviction that begin to look dubious with a bit of attention, but at the end of these conversations we are still left with an interesting question: what ARE the methods of contextualization for our data? If such methods don’t involve referencing a standard in some sort of explicit or implicit lookup table, what are the alternatives? I think these dynamic visualizations of individual data in the context of groups, where the groups have a context that can be named and interrogated, are great to see and think about. Thanks Kiel!

I’d argue that the most meaningful context for our own data is our own data.

Comparisons across people can often be made more meaningful after using context to filter and normalize the data. For example, one could visualize the percentage of the HRreserve for each person, instead of the absolute HR.

Kiel, any chance this data could be made available?

Sometimes I find, putting data in the right context is like being a story teller. Especially this type of data where there isn’t really an objective context to be working with (i.e. work rhythm). Outside a health or fitness context, I find myself asking what am I trying to convey to the viewer.

While, often the aim is to get the viewer to associate with the data that doesn’t really help one design a useful visualisation, and so I find narratives help somewhat. Hence my previous use of associating predefined heartbeat ranges with certain colours, with colours indicating meaningful types of physical activity.

I’d highly recommend reading,

Ståhl, A., Höök, K., Svensson, M., Taylor, A.S., Combetto, M.: Experiencing the Affective Diary. Personal and Ubiquitous Computing. 13, 365–378 (2008).

It details the development of an affective diary which incorporates the owner’s physiology into journal entries. The process of developing a meaningful interface for physiological data makes for a rather interesting read.

@ejain, I’m not sure, I would need to ask. I have permission to re-use this data not to distribute it. Do you have any particular ideas you would like to try?

Kiel and Eric,

I looked at the paper, I would to read it but don’t have a subscription to this journal. I wonder if we could get the authors to come to Amsterdam in May?


Hi Gary,

I’ve sent a copy via e-mail.

  • Kiel

No, just curious about the form of the data.

You have already distributed their data, just in a format that makes further re-use inconvenient :slight_smile:

I don’t fancy breaking out Paint’s colour picker for that job :slight_smile: