EmoTour: Estimating Emotion and Satisfaction of Users Based on Behavioral Cues and Audiovisual Data

With the spread of smart devices, people may obtain a variety of information on their surrounding environment thanks to sensing technologies. To design more context-aware systems, psychological user context (e.g., emotional status) is a substantial factor for providing useful information in an appro...

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Main Authors: Yuki Matsuda, Dmitrii Fedotov, Yuta Takahashi, Yutaka Arakawa, Keiichi Yasumoto, Wolfgang Minker
Format: Article
Language:English
Published: MDPI AG 2018-11-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/18/11/3978
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spelling doaj-d9f606fdd96f4accbd73758b4ec4f1d02020-11-25T02:24:34ZengMDPI AGSensors1424-82202018-11-011811397810.3390/s18113978s18113978EmoTour: Estimating Emotion and Satisfaction of Users Based on Behavioral Cues and Audiovisual DataYuki Matsuda0Dmitrii Fedotov1Yuta Takahashi2Yutaka Arakawa3Keiichi Yasumoto4Wolfgang Minker5Graduate School of Information Science, Nara Institute of Science and Technology, Nara 630-0192, JapanInstitute of Communications Engineering, Ulm University, 89081 Ulm, GermanyGraduate School of Information Science, Nara Institute of Science and Technology, Nara 630-0192, JapanGraduate School of Information Science, Nara Institute of Science and Technology, Nara 630-0192, JapanGraduate School of Information Science, Nara Institute of Science and Technology, Nara 630-0192, JapanInstitute of Communications Engineering, Ulm University, 89081 Ulm, GermanyWith the spread of smart devices, people may obtain a variety of information on their surrounding environment thanks to sensing technologies. To design more context-aware systems, psychological user context (e.g., emotional status) is a substantial factor for providing useful information in an appropriate timing. As a typical use case that has a high demand for context awareness but is not tackled widely yet, we focus on the tourism domain. In this study, we aim to estimate the emotional status and satisfaction level of tourists during sightseeing by using unconscious and natural tourist actions. As tourist actions, behavioral cues (eye and head/body movement) and audiovisual data (facial/vocal expressions) were collected during sightseeing using an eye-gaze tracker, physical-activity sensors, and a smartphone. Then, we derived high-level features, e.g., head tilt and footsteps, from behavioral cues. We also used existing databases of emotionally rich interactions to train emotion-recognition models and apply them in a cross-corpus fashion to generate emotional-state prediction for the audiovisual data. Finally, the features from several modalities are fused to estimate the emotion of tourists during sightseeing. To evaluate our system, we conducted experiments with 22 tourists in two different touristic areas located in Germany and Japan. As a result, we confirmed the feasibility of estimating both the emotional status and satisfaction level of tourists. In addition, we found that effective features used for emotion and satisfaction estimation are different among tourists with different cultural backgrounds.https://www.mdpi.com/1424-8220/18/11/3978ubiquitous computingemotion recognitionsatisfaction estimationwearable computingdialogue systemssmart tourismsmart cities
collection DOAJ
language English
format Article
sources DOAJ
author Yuki Matsuda
Dmitrii Fedotov
Yuta Takahashi
Yutaka Arakawa
Keiichi Yasumoto
Wolfgang Minker
spellingShingle Yuki Matsuda
Dmitrii Fedotov
Yuta Takahashi
Yutaka Arakawa
Keiichi Yasumoto
Wolfgang Minker
EmoTour: Estimating Emotion and Satisfaction of Users Based on Behavioral Cues and Audiovisual Data
Sensors
ubiquitous computing
emotion recognition
satisfaction estimation
wearable computing
dialogue systems
smart tourism
smart cities
author_facet Yuki Matsuda
Dmitrii Fedotov
Yuta Takahashi
Yutaka Arakawa
Keiichi Yasumoto
Wolfgang Minker
author_sort Yuki Matsuda
title EmoTour: Estimating Emotion and Satisfaction of Users Based on Behavioral Cues and Audiovisual Data
title_short EmoTour: Estimating Emotion and Satisfaction of Users Based on Behavioral Cues and Audiovisual Data
title_full EmoTour: Estimating Emotion and Satisfaction of Users Based on Behavioral Cues and Audiovisual Data
title_fullStr EmoTour: Estimating Emotion and Satisfaction of Users Based on Behavioral Cues and Audiovisual Data
title_full_unstemmed EmoTour: Estimating Emotion and Satisfaction of Users Based on Behavioral Cues and Audiovisual Data
title_sort emotour: estimating emotion and satisfaction of users based on behavioral cues and audiovisual data
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2018-11-01
description With the spread of smart devices, people may obtain a variety of information on their surrounding environment thanks to sensing technologies. To design more context-aware systems, psychological user context (e.g., emotional status) is a substantial factor for providing useful information in an appropriate timing. As a typical use case that has a high demand for context awareness but is not tackled widely yet, we focus on the tourism domain. In this study, we aim to estimate the emotional status and satisfaction level of tourists during sightseeing by using unconscious and natural tourist actions. As tourist actions, behavioral cues (eye and head/body movement) and audiovisual data (facial/vocal expressions) were collected during sightseeing using an eye-gaze tracker, physical-activity sensors, and a smartphone. Then, we derived high-level features, e.g., head tilt and footsteps, from behavioral cues. We also used existing databases of emotionally rich interactions to train emotion-recognition models and apply them in a cross-corpus fashion to generate emotional-state prediction for the audiovisual data. Finally, the features from several modalities are fused to estimate the emotion of tourists during sightseeing. To evaluate our system, we conducted experiments with 22 tourists in two different touristic areas located in Germany and Japan. As a result, we confirmed the feasibility of estimating both the emotional status and satisfaction level of tourists. In addition, we found that effective features used for emotion and satisfaction estimation are different among tourists with different cultural backgrounds.
topic ubiquitous computing
emotion recognition
satisfaction estimation
wearable computing
dialogue systems
smart tourism
smart cities
url https://www.mdpi.com/1424-8220/18/11/3978
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