Short course ‘Using sensors in social research’

Register online or contact Kristrún Gunnarsdóttir.

The Centre for Research in Social Simulation (CRESS) at the University of Surrey has announced the short course, Using sensors in social research, Monday 10 and Tuesday 11  September 2018. Hosted at the University on Stag Hill in Guildford (see Google map), this will be an opportunity for research practitioners to learn about the use of digital sensors from members of the ESRC/NCRM funded HomeSense project.

Using sensors in social research will mix short presentations, interactive hands-on and exploratory sessions, group work and discussions for participants to obtain a good understanding of the technologies and operating processes required for effective inclusion and management of this method.

It will also enable researchers to ask ‘how’ and ‘if’ sensors could be used in their own research, and how to address the ethical, consent, data security, confidentiality and other issues involved. Participants will receive a certificate of attendance of this NCRM methods course.

The lessons learned and inside knowledge from the HomeSense field trial will also be presented, explaining and demonstrating the analytic tools and techniques required for visualising, interpreting and understanding household activities based largely on sensor-generated data.

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Demystifying the technology important for ensuring the confidences of field trial participants

Six months into the HomeSense project, Prof. Nigel Gilbert outlined the “interesting ethics issues” that required attention before the field trial could responsibly get underway in volunteer households.

Nigel Gilbert at ESRC RMF 2016
Nigel Gilbert at ESRC RMF 2016

In his presentation, “The Ethics of Sensors” at the 2016 ESRC Research Methods Festival in Bath, Nigel explained the ambition of HomeSense to enable social researchers to use digital sensors alongside self-reported methods and observations. The project is also assessing the extent to which householders might accept sensors in their homes for research, and the final output will be a set of guidelines for use in such studies.

More refined or automated applications of digital sensors in social research could, it’s hoped, lead to more effective assisted living and telecare services, or more efficient use of energy. But before experimenting with sensors in people’s households, some obvious, and less obvious, questions of ethics ought to be addressed. Continue reading “Demystifying the technology important for ensuring the confidences of field trial participants”

Recognising activities at home with digital sensors and time-use diaries

HomeSense is a methodological research project focused on the use of digital sensors in social research.  One of the key issues is finding meaningful information in sensor-generated data, which was the focus of a paper presented by Jie Jiang at the International Conference on Future Networks and Distributed Systems (ICFNDS’17) in Cambridge in July this year.

The paper now published in the conference proceedings, covers data analysis from the early stages of the HomeSense field trial.  It reports on applying machine learning methods to interpret sensor-generated data, and discusses a method for identifying features of various types of activity and evaluating the agreement between sensor-generated data and self-reported data from time-use diaries.

Since one of the implications of using sensors for social research is that, in due course, activities could be recognised automatically, this study also proposes a method for modelling a range of activities recorded by sensors.

The same approach has been continued by the team in an extended version of the study with data from more households, but the publication of the conference paper in the Proceedings of ICFNDS’17 provided an opportunity for some of its authors – Jie Jiang, Riccardo Pozza, Kristrún Gunnarsdóttir – to sit down and discuss the implications of this work, and where it sits within the project as a whole.

The extended study, “Using Sensors to Study Home Activities“, is now published in the Journal of Sensors and Actuator Networks 6(4): 32.

Journal of Sensors and Actuator Networks 6(4): 32.
Journal of Sensors and Actuator Networks 6(4): 32.

Continue reading “Recognising activities at home with digital sensors and time-use diaries”