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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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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