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.
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.