University roles and responsibilities
- HomeSense (ESRC) Project Manager
- CANDID (H2020-ICT) Principle Investigator
The Archive Saga: Shepherds of data, documents and code, and their will to order.
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.