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Open AccessProceedings

In-Home Behavioral Observation Method Employing Internet of Things Sensors for Barrier Investigation of Energy Saving Activities

1
Central Research Institute of Electric Power Industry, 2-6-1 Nagasaka, Yokosuka-shi, Kanagawa 240-0196, Japan
2
IdeaLab Co., Ltd., 3-29-1 Ebisu, Shibuya-ku, Tokyo 150-0013, Japan
*
Author to whom correspondence should be addressed.
Presented at the 13th International Conference on Ubiquitous Computing and Ambient Intelligence UCAmI 2019, Toledo, Spain, 2–5 December 2019.
Proceedings 2019, 31(1), 31; https://doi.org/10.3390/proceedings2019031031
Published: 20 November 2019
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.
Keywords: behavioral observation; IoT; behavioral sensing; in-depth interview; energy saving behavioral observation; IoT; behavioral sensing; in-depth interview; energy saving
MDPI and ACS Style

Hattori, S.; Miura, T.; Ichikawa, R.; Sawai, D. In-Home Behavioral Observation Method Employing Internet of Things Sensors for Barrier Investigation of Energy Saving Activities. Proceedings 2019, 31, 31.

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