This paper proposes an in-home behavioral observation method employing Internet of Things (IoT) sensors. Behavioral change programs based on information provision approaches have begun to be employed in the reduction of carbon dioxide emissions in the residential sector. To improve efforts to save energy, a behavioral observation method that aims to understand the reality of users’ daily activities could be an effective approach. However, problems with existing methods include observations costs, privacy implications and the other complications regarding the specific behaviors of the person being observed. An in-home behavioral observation method employing IoT sensors is therefore proposed 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 the proposed method 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.
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