Abstract
This paper proposes a new methodology for collecting visitors’ behavior and their emotions in town by using smartphones.
Existing social information services, such as Facebook and Twitter, are expanding to attach location data to users’ content. To capture situations of town, such as events what happens there or how people feel, the author believes that it’s not enough to collect tweets and behavior logs of locations in the town, because in fact the number of geotagged tweets is limited. Especially for microscopic analysis of town situations in small resolution of time and space, more information sources reflecting strollers’ behaviors and emotions are needed.
The paper proposed a function of LBS smartphone application to collect users’ behavior and emotions. When a user installs and uses an application with the function in town, the function records and transmits not only his/her locations but also his/her facial expressions by using front-facing camera.
An experiment was made in the beginning of November 2013. 55 subjects participated in the experiment. In addition to using the application in town, subjects were requested to provide correct data of facial expressions in 9 classes such as excited, fun and tired.
The function extracts 66 feature points of face by using Saragih’s model. As a quick result, the overall precision of 9 class-classification is 91.1% at 10-fold cross validation. The author believes that the result supports that the proposed application can collect facial expressions of not only active users who post microblogs but also read-only users.
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Aihara, K. (2014). Collecting Behavior Logs with Emotions in Town. In: Streitz, N., Markopoulos, P. (eds) Distributed, Ambient, and Pervasive Interactions. DAPI 2014. Lecture Notes in Computer Science, vol 8530. Springer, Cham. https://doi.org/10.1007/978-3-319-07788-8_22
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DOI: https://doi.org/10.1007/978-3-319-07788-8_22
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