In-home Behavioral Observation Employing IoT Sensors

Abstract

This paper reports on the result of in-home behavioral observation employing Internet of Things (IoT) sensors. Behavioral observation is a qualitative research method used to understand the users' reality of daily activities and the usage of services or products. This method has been mainly utilized in public spaces such as schools and commercial facilities to develop the creative processes. In contrast, this method is difficult to utilize at a household level because problems with existing methods include costs, privacy implications, and the other complications regarding the specific behaviors of the person being observed. An in-home behavioral observation employing IoT sensors is therefore an effective approach to both reduce costs and alleviate the privacy impact on user's in-home activities. The use of sensor-based observation presents several relevant advantages. For example, the cost of sensor-based observation is relatively cheap compared to human-based approaches. In addition, it employs a minimum number of necessary sensors and has a relatively small impact on privacy and personal activities. These advantages imply that this approach could allow long-term observations targeting a number of households, thus enabling exhaustive investigations. Sensory-based observation approaches are applied to investigations of the barriers to in-home energy-saving activities with a goal of improving relevant behavioral change programs. The results showed that the in-home activities of the twenty target households were successfully observed for six weeks with various barriers having been extracted and organized.