TY - GEN
T1 - Laugh Log
T2 - Thematic Area on Human Interface and the Management of Information, HIMI 2019, held as part of the 21st International Conference on Human-Computer Interaction, HCI International 2019
AU - Ueoka, Ryoko
N1 - Funding Information:
supported by JSPS KAKENHI Grant Number
Funding Information:
This work was supported by JSPS KAKENHI Grant Number 15H01765.
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Laughter is said to be linked to satisfactory human relationships and to have a positive impact on health, hence it is often related to perceived improvements in the quality of life. In this paper, we focused on the stomach to detect natural laughter resulting from funniness, which is positive even in laughter. Consequently, by measuring the pressure change in the abdomen using a textile sensor we can build a wearable laugh log system capable of detecting and recording laughter. In our pilot experiment, we conducted experiments that induce laughter under environmental settings and examined a deep learning method to detect laughter in a period within the measured log. Results demonstrated the possibility of detection of laughter in a controlled environment. We then simulated daily scenes that were likely to trigger laughter, and then we measured and examined the detection of laughter through deep learning.
AB - Laughter is said to be linked to satisfactory human relationships and to have a positive impact on health, hence it is often related to perceived improvements in the quality of life. In this paper, we focused on the stomach to detect natural laughter resulting from funniness, which is positive even in laughter. Consequently, by measuring the pressure change in the abdomen using a textile sensor we can build a wearable laugh log system capable of detecting and recording laughter. In our pilot experiment, we conducted experiments that induce laughter under environmental settings and examined a deep learning method to detect laughter in a period within the measured log. Results demonstrated the possibility of detection of laughter in a controlled environment. We then simulated daily scenes that were likely to trigger laughter, and then we measured and examined the detection of laughter through deep learning.
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U2 - 10.1007/978-3-030-22649-7_34
DO - 10.1007/978-3-030-22649-7_34
M3 - Conference contribution
AN - SCOPUS:85069637676
SN - 9783030226480
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 426
EP - 439
BT - Human Interface and the Management of Information. Information in Intelligent Systems - Thematic Area, HIMI 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Proceedings
A2 - Yamamoto, Sakae
A2 - Mori, Hirohiko
PB - Springer Verlag
Y2 - 26 July 2019 through 31 July 2019
ER -
