Faculty Profiles - YOSHIDA Kaori

Papers - YOSHIDA Kaori

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Fuzzy Rule-Based Interpretation of Hand Gesture Intentions Reviewed International journal

Silpani D.C., Rukka F.M., Yoshida K.

Mathematics 13 ( 19 ) 2025.10

Enhancing Decision-Making System with Implicit Attitude and Preferences: A Comprehensive Review in Computational Social Science Reviewed International journal

SILPANI Dian Christy, YOSHIDA Kaori

International Journal of Affective Engineering ( Japan Society of Kansei Engineering ) advpub ( 0 ) 2024.01

Authorship：Last author Language：English Publishing type：Research paper (scientific journal)

Implicit attitudes and preferences can be dynamic and context-dependent, evolving over time and in different situations. In this study, we point out 4 main topics: Implicit attitudes, preferences, decision-making system, and computational social science, to introduce a new approach to developing a decision-making system. As a comprehensive review, we set the 3 research questions as the main considerations for this study. We collected 903,451 articles using 3 major database sources (ScienceDirect, ACM Digital Library, and IEEE Xplore), screened 5 times with different viewpoints, and surveyed 14 articles to answer the research questions. As a result of the comprehensive review, we conclude that it shows the possibility of enhancing the decision-making system with implicit attitudes and preferences in a computational social science manner.

DOI： 10.5057/ijae.ijae-d-24-00011

CiNii Research

The Impact of Large Sample Datasets on Hand Gesture Recognition by Hand Landmark Classification Reviewed International journal

AHMAD Khawaritzmi Abdallah, SILPANI Dian Christy, YOSHIDA Kaori

International Journal of Affective Engineering ( Japan Society of Kansei Engineering ) 22 ( 3 ) 253 - 259 2023.07

Authorship：Last author Language：English Publishing type：Research paper (scientific journal)

Today, almost all aspects of life are influenced by technology to simplify these aspects of life. Researching how humans and computers interact is very important to build a good model for better human-computer interaction in the future. Using technology such as computer vision, we can now collect the information we are looking for directly from a human. Humans can use many kinds of modalities to interact with computers. Hands are perhaps the largest source of body language information after the face. To understand the gesture’s meaning, we can use MediaPipe Hands, developed at Google LLC, as a method to track and recognize human hands. However, if we want to understand some kinds of hand gestures using MediaPipe Hands, we need to create a condition using if-else manners. This research tried to collect the wide variety of each hand gesture using the 21 key points in x, y, and z coordinates as a feature. We chose Support Vector Machine (SVM) and Artificial Neural Network (ANN) as classifiers to validate the impact of large sample datasets. This research found that ANN is the best among all the methods we used as a classifier method for the 3D value of 21 key points from the hand skeleton. The accuracy and F1-score from ANN were 98.4% accuracy and 98.2% F1-Score, respectively, representing the best performance for each class of all the methods we used in this research.

DOI： 10.5057/ijae.ijae-d-22-00022

CiNii Research

Other Link： https://www.jstage.jst.go.jp/article/ijae/22/3/22_IJAE-D-22-00022/_pdf

特集「地域産業を支える知の拠点」に寄せて

吉田香

感性工学 ( 日本感性工学会 ) 23 ( 3 ) 93 - 93 2025.09

Knowledge-Infused Retrieval Boosts Few-Shot Hand Gesture Recognition on HaGRID with Vision-Language Model Reviewed International journal

Indasyah E., Yoshida K.

Frontiers in Artificial Intelligence and Applications 411 396 - 413 2025.09

特集「AIとファジィの接点」にあたって

吉田香

人工知能 ( 一般社団法人人工知能学会 ) 40 ( 3 ) 266 - 268 2025.05

矢鳴虎夫先生（BMFSA 第6 代会長（1999-2001）・名誉会員）を偲ぶ

和辻直, 白濵成希, 宮本和典, 吉田香, 有田清三郎, 山川烈, 内野英治, 渡邉志

バイオメディカル・ファジィ・システム学会誌 ( バイオメディカル・ファジィ・システム学会 ) 26 ( 2 ) 59 - 66 2025.02

BLE Signal Reception and Localization Performance with Varying Receiver and Beacon Setups Reviewed International journal

Benaissa B., Hendrichovsky F., As M., Yoshida K.

Future Internet 17 ( 2 ) 2025.02

私のブックマーク：感性情報処理

吉田香

人工知能 ( 一般社団法人人工知能学会 ) 40 ( 1 ) 1080 - 1087 2025.01

Dynamic Hand Gesture Recognition by Hand Landmark Classification Using Long Short-Term Memory Reviewed International journal

Ahmad K.A., Higashi T., Yoshida K.

Pertanika Journal of Science and Technology 33 ( S2 ) 73 - 84 2025.01

Fuzzy Sentiment Analysis for Hand Gesture Interpretation in Words Expression Reviewed International journal

Rukka Faizah Mappanyompa, Silpani Dian Chisty, Yoshida Kaori

Proceedings of Computer Information Systems, Biometrics and Kansei Engineering ( International Conference on Biometrics and Kansei Engineering ) 2025 ( 0 ) 106 - 119 2025.01

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (international conference proceedings)

This paper will discuss the interpretation of hand gestures using words with the fuzzy
sentiment analysis approach in the tea-tasting experiment. In the experiment, participants were
asked to drink tea, give a hand gesture, and choose three words that could interpret the hand
gesture. The results show that hand gestures cannot be categorized as fixed sentiment labels, considering
the meaning can differ depending on each preference. Generally, traditional sentiment
categorizes labels as positive, negative, or neutral, which fails to capture human preferences.
Therefore, we propose that the fuzzy sentiment model be used to solve the traditional problem,
which provides a more flexible approach. Our findings show that interpreting hand gestures such
as positive is not always positive but can be neutral-positive and vice versa. The results show that
hand gestures alone are insufficient to determine sentiment; word expression is required to capture
sentiment's different and complex aspects.

DOI： 10.69333/icbake.2025.0_106

Kyutacar

CiNii Research

Static Hand Gesture Recognition Based on Multi-Feature Extraction with Context for User Intension Reviewed

Ahmad Khawaritzmi Abdallah, Silpani Dian Christy, Yoshida Kaori

ICIC Express Letters, Part B: Applications ( ICIC International ) 15 ( 8 ) 779 - 786 2024.08

An Investigation of the Effects of Presentation Methods for Visual Elements and Thinking Styles on Text Comprehension Reviewed International journal

Kouki Ueno, Kaori Yoshida and Naruki Shirahama

ICIC Express Letters, Part B: Applications 15 ( 6 ) 561 - 573 2024.06

Implicit Attitude of Human Behavior: A Review Reviewed

Dian Christy Silpani and Kaori Yoshida

Proceedings of Computer Information Systems, Biometrics and Kansei Engineering 2023 102 - 114 2023.09

An Investigation of the Effects of Presentation Methods for Visual Elements and Thinking Styles on Text Comprehension Reviewed International journal

Kouki Ueno, Kaori Yoshida and Naruki Shirahama

Proc. of 17th International Conference on Innovative Computing, Information and Control (ICICIC2023) ( 125 ) 2023.08

Static Hand Gesture Recognition Based on Multi-Feature Extraction with Context for User Intension Reviewed International journal

Khawaritzmi Abdallah Ahmad, Dian Christy Silpani and Kaori Yoshida

Proc. of 17th International Conference on Innovative Computing, Information and Control (ICICIC2023) ( 142 ) 2023.08

A Feasibility Study on Hand Gesture Intention Interpretation Based on Gesture Detection and Speech Recognition Reviewed International journal

Silpani Dian Christy, Suematsu Keishi, Yoshida Kaori

Journal of Advanced Computational Intelligence and Intelligent Informatics ( Fuji Technology Press Ltd. ) 26 ( 3 ) 375 - 381 2022.05

Authorship：Last author Language：English Publishing type：Research paper (scientific journal)

In this paper, considering the characteristics of hand gestures that occur during a natural, unscripted human conversation, we report the results of experiments involving the data analysis of the video recordings of experiments. The recorded conversations between two subjects and the assistant in the experiments (the first author) are analyzed for findings that might be useful to future human-robot interaction (HRI), findings regarding the recognition of hand gestures in natural conversations. In the experiment, hand gestures that appeared naturally during the conversations were manually selected, and their elapsed times and number of appearances were analyzed. In addition, these unscripted conversations were analyzed with a voice recognition system, and the meanings of the hand gestures that appear in the recorded video were interpreted. It was observed that some gestures were made consciously and others unconsciously. Some gestures appeared multiple times with similar meanings and the same intention, especially gestures involving pointing at something to emphasize a sentence or word. Other observations included that there were times when (i) each subject made different hand gestures to convey the same meaning, (ii) both subjects made the same hand gesture but intended different meanings, and (iii) a specific gesture was made by the same subject but with different intended meanings. After considering the results of these experiments, we conclude that it is better to combine gestures with the utterances at the time in order to interpret their intended meanings correctly.

DOI： 10.20965/jaciii.2022.p0375

Scopus

CiNii Research

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85132786254&origin=inward

Calibration Cost Reduction of Indoor Localization Using Bluetooth Low Energy Beacon Reviewed International journal

As Mansur, Shimizu Hiroshi, Benaissa Brahim, Yoshida Kaori, Köppen Mario

Journal of Advanced Computational Intelligence and Intelligent Informatics ( Fuji Technology Press Ltd. ) 26 ( 1 ) 97 - 106 2022.01

Language：English Publishing type：Research paper (scientific journal)

Indoor localization based on Bluetooth low energy (BLE) beacons has been rapidly developed, and many approaches have been developed to achieve higher estimation accuracy. In these methods, the received signal strength (RSS) is the input. However, the measurement of indoor environments is affected easily; the signal may be reflected and attenuated by obstacles such as the human body, walls, and furniture, which creates a challenge for methods based on signal mapping. In this study, BLE signal characteristics are investigated in an indoor localization setting. An experiment is performed using one BLE beacon and multiple receivers installed at different wall and ceiling positions. The raw RSS is observed, and the relationship between the BLE beacon signal strength characteristics against the human body effect as well as the receiver’s placement in the observation area are discussed. Signal mapping is performed, where the signal strength is measured from all receivers simultaneously. The position estimation accuracy is examined based on different data scenarios. The results show that the estimation position estimated by the BLE beacon based on extensive BLE beacon data does not affect the estimation accuracy.

DOI： 10.20965/jaciii.2022.p0097

Scopus

CiNii Article

CiNii Research

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85124460770&origin=inward

Hand Gesture Recognition by Hand Landmark Classification Reviewed International journal

AHMAD Khawaritzmi Abdallah, SILPANI Dian Christy, YOSHIDA Kaori

International Symposium on Affective Science and Engineering ( Japan Society of Kansei Engineering ) ISASE2022 ( 0 ) 1 - 4 2022.01

Authorship：Last author Language：English Publishing type：Research paper (international conference proceedings)

These years, computer vision technology has been rapidly advanced. On the other hand, customer satisfaction is essential for the industry to improve its service but use traditional methods ineffectively. Using technology such as computer vision, we can now collect the information we are looking for directly from a human. Humans can use many kinds of modalities to interact with computers. Hands are perhaps the largest source of body language information after the face. To understand the gesture's meaning, we can use MediaPipe Hands developed at Google LLC as a method to track and recognize human hands. However, if we want to understand some kinds of hand gestures using MediaPipe Hands, we need to create a condition ourselves by using if-else conditions. This research tried to collect the many varieties of each hand gesture using the 21 key points in x, y, and z coordinates as a feature. As classifiers, we chose the support vector machine (SVM) and the artificial neural network (ANN). This research found that SVM using a polynomial kernel is the best among all of the methods we used as a classifier method for the 3D value of 21 key points from the hand skeleton. The accuracy and F1-score from SVM using a polynomial kernel were 86.26% accuracy and 82% F1-score, respectively, representing the best performance for each class of all the methods we used in this research.

DOI： 10.5057/isase.2022-c000026

CiNii Research

Other Link： https://www.jstage.jst.go.jp/article/isase/ISASE2022/0/ISASE2022_1_34/_pdf

特集：「地域における AI 社会実装」特集「地域における AI 社会実装」にあたって

吉田香, 諏訪博彦, 清田陽司

人工知能 ( 一般社団法人人工知能学会 ) 37 ( 2 ) 170 - 171 2022.01

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Papers - YOSHIDA Kaori