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J. Sens. Actuator Netw. 2017, 6(4), 32; doi:10.3390/jsan6040032

Using Sensors to Study Home Activities

1
Centre for Research in Social Simulation, University of Surrey, Guildford GU2 7XH, UK
2
5G Innovation Centre, University of Surrey, Guildford GU2 7XH, UK
This paper is an extended version of Jiang, J.; Pozza, R.; Gunnarsdóttir, K.; Gilbert, N.; Moessner, K. Recognising Activities at Home: Digital and Human Sensors. In Proceedings of the International Conference on Future Networks and Distributed Systems, Cambridge, UK, 19–20 July 2017; ACM: New York, NY, USA, 2017; ICFNDS’17, pp. 17:1–17:11.
*
Author to whom correspondence should be addressed.
Received: 1 November 2017 / Revised: 3 December 2017 / Accepted: 13 December 2017 / Published: 16 December 2017
(This article belongs to the Special Issue Sensors and Actuators in Smart Cities)
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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. View Full-Text
Keywords: sensors; time use diaries; activity recognition; time series; Internet of Things; social research sensors; time use diaries; activity recognition; time series; Internet of Things; social research
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Jiang, J.; Pozza, R.; Gunnarsdóttir, K.; Gilbert, N.; Moessner, K. Using Sensors to Study Home Activities. J. Sens. Actuator Netw. 2017, 6, 32.

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