Using Sensors to Study Home Activities

Jiang, Jie and Pozza, Riccardo and Gunnarsdottir, Kristrun and Gilbert, Nigel and Moessner, Klaus (2017) Using Sensors to Study Home Activities. Journal of Sensor and Actuator Networks, 6 (4). ISSN 2224-2708

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Official URL: http://www.mdpi.com/2224-2708/6/4/32

Abstract

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.

Item Type:Article
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 > 5.14 Non-Parametric Approaches
ID Code:4226
Deposited By: NCRM users
Deposited On:22 Aug 2018 14:35
Last Modified:22 Aug 2018 14:35

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