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Kristrún Gunnarsdóttir


Research Fellow
PhD, MSc, BFA, BA

Academic and research departments

Department of Sociology.

University roles and responsibilities

  • HomeSense (ESRC) Project Manager
  • CANDID (H2020-ICT) Principle Investigator

My qualifications

2010
PhD in Sociology
The Archive Saga: Shepherds of data, documents and code, and their will to order.
Cardiff University, School of Social Science
2005
MSc in Social Science
Cardiff University, School of Social Science
2000
BA in Philosophy
University of Iceland

Research projects

My publications

Publications

Jiang J, Pozza R, Gunnarsdottir K, Gilbert GN, Moessner K (2017) Recognising Activities at Home: Digital and Human Sensors, Proceedings of ICFNDS ?17, Cambridge, United Kingdom, July 19-20, 2017 ACM, the Association for Computing Machinery
What activities take place at home? When do they occur, for how
long do they last and who is involved? Asking such questions is
important in social research on households, e.g., to study energyrelated
practices, assisted living arrangements and various aspects
of family and home life. Common ways of seeking the answers
rest on self-reporting which is provoked by researchers (interviews,
questionnaires, surveys) or non-provoked (time use diaries). Longitudinal
observations are also common, but all of these methods
are expensive and time-consuming for both the participants and
the researchers. The advances of digital sensors may provide an
alternative. For example, temperature, humidity and light sensors
report on the physical environment where activities occur, while
energy monitors report information on the electrical devices that
are used to assist the activities. Using sensor-generated data for
the purposes of activity recognition is potentially a very powerful
means to study activities at home. However, how can we quantify
the agreement between what we detect in sensor-generated
data and what we know from self-reported data, especially nonprovoked
data? To give a partial answer, we conduct a trial in a
household in which we collect data from a suite of sensors, as well
as from a time use diary completed by one of the two occupants.
For activity recognition using sensor-generated data, we investigate
the application of mean shift clustering and change points
detection for constructing features that are used to train a Hidden
Markov Model. Furthermore, we propose a method for agreement
evaluation between the activities detected in the sensor data and
that reported by the participants based on the Levenshtein distance.
Finally, we analyse the use of different features for recognising
different types of activities.
Jiang J, Pozza R, Gunnarsdottir K, Gilbert G, Moessner K (2017) Using Sensors to Study Home Activities, Journal of Sensor and Actuator Networks 6 (4) MDPI
Understanding home activities is important in social research to study aspects of home life, e.g., energy-related practices and assisted living arrangements. Common approaches to identifying which activities are being carried out in the home rely on self-reporting, either retrospectively (e.g., interviews, questionnaires, and surveys) or at the time of the activity (e.g., time use diaries). The use of digital sensors may provide an alternative means of observing activities in the home. For example, temperature, humidity and light sensors can report on the physical environment where activities occur, while energy monitors can report information on the electrical devices that are used to assist the activities. One may then be able to infer from the sensor data which activities are taking place. However, it is first necessary to calibrate the sensor data by matching it to activities identified from self-reports. The calibration involves identifying the features in the sensor data that correlate best with the self-reported activities. This in turn requires a good measure of the agreement between the activities detected from sensor-generated data and those recorded in self-reported data. To illustrate how this can be done, we conducted a trial in three single-occupancy households from which we collected data from a suite of sensors and from time use diaries completed by the occupants. For sensor-based activity recognition, we demonstrate the application of Hidden Markov Models with features extracted from mean-shift clustering and change points analysis. A correlation-based feature selection is also applied to reduce the computational cost. A method based on Levenshtein distance for measuring the agreement between the activities detected in the sensor data and that reported by the participants is demonstrated. We then discuss how the features derived from sensor data can be used in activity recognition and how they relate to activities recorded in time use diaries.