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Recognising Activities at Home: Digital and Human Sensors

Jiang, Jie, Pozza, Riccardo, Gunnarsdottir, Kristrun, Gilbert, Geoffrey and Moessner, Klaus (2017) Recognising Activities at Home: Digital and Human Sensors In: International Conference on Future Networks and Distributed Systems (ICFNDS) 2017, 19-20 Jul 2017, Cambridge, UK.

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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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Arts and Social Sciences > Department of Sociology
Authors :
Date : 20 July 2017
DOI : 10.1145/3102304.3102321
Copyright Disclaimer : © 2017 ACM
Uncontrolled Keywords : Sensors; Time use diaries; Activity recognition; Time series; Internet of things; Social research
Depositing User : Clive Harris
Date Deposited : 11 Jul 2017 14:59
Last Modified : 16 Jan 2019 18:54

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