Using Sensors to Study Home Activities

Abstract

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.

MakeSense

“…Comparisons of the recordings obtained from the time-use diaries and the knowledge extracted from the sensor-generated data not only help the researchers gain an in-depth understanding of the underlying environments where different activities were carried out but also help them validate their reasoning of whether certain activities occurred and/or how the activities occurred. Please refer to Jiang et al [2017] for a more detailed illustration of home activity inference based on sensor-generated data.…”

Using Sensors in Organizational Research—Clarifying Rationales and Validation Challenges for Mixed Methods

“…Sensors are increasingly pervasive in all aspects of human life. They are widely used in health related applications as already mentioned (rehab, fitness, elderly care, occupational safety) as well as in “smart” cities and homes, tracking of consumer behavior (retail, tourism) or in the social signal processing community (Vinciarelli et al, 2009; Imani et al, 2016; Alcaraz et al, 2017; Goonawardene et al, 2017; Jiang et al, 2017; Oosterlinck et al, 2017). Out of the considerable variety of sensor types and their divergent application fields, the present article concentrates on a relatively well-defined sub-set, namely wearable sensors used in organizational research.…”

RadIoT: Radio Communications Intrusion Detection for IoT - A Protocol Independent Approach

“…features). For the results presented in the following, we use an autoencoder neural network that provided satisfactory results in similar situations [21]. The comparative analysis of different machine learning algorithms is planned for future work.…”