Recognising Activities at Home: Digital and Human Sensors

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

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Official URL: https://dl.acm.org/citation.cfm?id=3102321

Abstract

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 energy related 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 di�fferent features for recognising di�fferent types of activities.

Item Type:Conference or Workshop Item (Paper)
Subjects:2. Data Collection > 2.4 Diary Methods
2. Data Collection > 2.12 Data Collection (other)
3. Data Quality and Data Management > 3.3 Quality in Quantitative Research
5. Quantitative Data Handling and Data Analysis > 5.2 Statistical Theory and Methods of Inference
5. Quantitative Data Handling and Data Analysis > 5.11 Time Series Analysis
5. Quantitative Data Handling and Data Analysis > 5.14 Non-Parametric Approaches
ID Code:4225
Deposited By: NCRM users
Deposited On:22 Aug 2018 14:21
Last Modified:22 Aug 2018 14:21

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