Next Article in Journal / Special Issue
Development of Intelligent Core Network for Tactile Internet and Future Smart Systems
Previous Article in Journal
Addressing the Issue of Routing Unfairness in Opportunistic Backhaul Networks for Collecting Sensed Data
Previous Article in Special Issue
Extended Batches Petri Nets Based System for Road Traffic Management in WSNs
Open AccessArticle

Using Sensors to Study Home Activities

Centre for Research in Social Simulation, University of Surrey, Guildford GU2 7XH, UK
5G Innovation Centre, University of Surrey, Guildford GU2 7XH, UK
Author to whom correspondence should be addressed.
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.
J. Sens. Actuator Netw. 2017, 6(4), 32;
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)
PDF [1741 KB, uploaded 18 December 2017]


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

Figure 1

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).
Printed Edition Available!
A printed edition of this Special Issue is available here.

Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
J. Sens. Actuator Netw. EISSN 2224-2708 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top