研究者詳細 - 吉田　香

論文 - 吉田　香

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Enhancing Decision-Making System with Implicit Attitude and Preferences: A Comprehensive Review in Computational Social Science 査読有り国際誌

SILPANI Dian Christy, YOSHIDA Kaori

International Journal of Affective Engineering ( 日本感性工学会 ) advpub ( 0 ) 2024年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

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 査読有り国際誌

AHMAD Khawaritzmi Abdallah, SILPANI Dian Christy, YOSHIDA Kaori

International Journal of Affective Engineering ( 日本感性工学会 ) 22 ( 3 ) 253 - 259 2023年07月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

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

その他リンク： https://www.jstage.jst.go.jp/article/ijae/22/3/22_IJAE-D-22-00022/_pdf

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年05月

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年02月

BLE Signal Reception and Localization Performance with Varying Receiver and Beacon Setups 査読有り国際誌

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

Future Internet 17 ( 2 ) 2025年02月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

This paper examines the performance of Bluetooth Low Energy signal reception for indoor localization by analyzing the interactions between gateways, beacons, and receiver placements. The study investigates the effect of different BLE beacon placements on signal strength and localization accuracy. It evaluates ten receiver ceiling-mounted and wall-mounted configurations, as well as five beacon body positions: shoulder, front pocket, back pocket, and wrist. A dataset comprising 2700 data points was collected and localization accuracy was assessed using a Radial Basis Function-based methodology. The results demonstrate that ceiling-mounted gateways offer more stable signal strength and enhanced localization accuracy compared to wall-mounted gateways. The findings highlight the significance of optimizing both gateway positioning and body placement to improve the performance of BLE-based indoor positioning systems.

DOI： 10.3390/fi17020054

Kyutacar

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85219182448&origin=inward

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年01月

Dynamic Hand Gesture Recognition by Hand Landmark Classification Using Long Short-Term Memory 査読有り国際誌

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

Pertanika Journal of Science and Technology 33 ( S2 ) 73 - 84 2025年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

Hand gestures are a valuable modality for human-computer interaction, conveying information that can be used as input. Dynamic hand gestures, prevalent in real-world scenarios, necessitate considering temporal factors such as gesture initiation, termination, and frame sequence. A Long Short-Term Memory (LSTM) based recognition model was proposed to address this challenge. Data availability for dynamic hand gesture research is a significant hurdle. The dataset introduced by Fronteddu et al. provides 27 classes of dynamic hand gestures, serving as a suitable training resource. MediaPipe Hands, a computer vision framework, was leveraged to extract keypoints from each frame, capturing spatial features fed into the LSTM model. Experiments were conducted to determine the optimal dropout rate for the LSTM model. Results indicated that a dropout rate of 70% yielded the highest accuracy, achieving up to 98.53% validation accuracy and 99.71% test accuracy. These findings demonstrate the effectiveness of the proposed LSTM-based recognition model for dynamic hand gestures. Future research could explore integrating other deep learning techniques, such as attention mechanisms, to enhance the accuracy and robustness of dynamic hand gesture recognition systems. Additionally, investigating the application of the proposed model in real-world scenarios, such as virtual and augmented reality, would be valuable in assessing its practical utility.

DOI： 10.47836/pjst.33.S2.05

Kyutacar

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85219544263&origin=inward

STATIC HAND GESTURE RECOGNITION BASED ON MULTI-FEATURE EXTRACTION WITH CONTEXT FOR USER INTENSION 査読有り

Ahmad K.A., Silpani D.C., Yoshida K.

ICIC Express Letters, Part B: Applications ( ICIC International 学会 ) 15 ( 8 ) 779 - 786 2024年08月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

Hand gesture is a source of information that contains much information, which has the advantage that this modality does not contain private information as much as information obtained from facial modalities. However, obtaining information from hand gestures still encounters several challenges, one of which is the diversity of gestures within one target class which is influenced by many factors such as the culture and personality of the individual. In this study, we propose a method of combining hand gesture features in the form of keypoints features, convolutional neural network features, and manually generating captions as contexts to improve model performance so that the model can classify hand gestures based on the intent of the hand gesture with context support. As a result, the CNN model, which initially experienced over-fitting when trying to classify hand gestures based on the intent of the hand gesture, can be overcome by this method. Running results show a significant increase in accuracy over training models that just use uni-features, with a model having a training accuracy of 99.95% and a validation accuracy of 99.74%. The final result of the model obtained the ability to understand 6 classes of hand gestures made in 3 classes based on the intent of the hand gestures exhibited when participants try the taste of tea, namely Like, Dislike, and Neutral (So-so).

DOI： 10.24507/icicelb.15.08.779

Kyutacar

Scopus

CiNii Research

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85202546702&origin=inward

Static Hand Gesture Recognition based on Multi-Feature extraction with Context for User Intension 査読有り国際誌

(1) Khawaritzmi Abdallah Ahmad, Dian Christy Silpani and Kaori Yoshida

ICIC Express Letters, Part B: Applications 15 ( 8 ) 2024年08月

An Investigation of the Effects of Presentation Methods for Visual Elements and Thinking Styles on Text Comprehension 査読有り国際誌

Kouki Ueno, Kaori Yoshida and Naruki Shirahama

ICIC Express Letters, Part B: Applications 15 ( 6 ) 561 - 573 2024年06月

Implicit Attitude of Human Behavior: A Review 査読有り

Dian Christy Silpani and Kaori Yoshida

Proceedings of Computer Information Systems, Biometrics and Kansei Engineering 2023 102 - 114 2023年09月

Static Hand Gesture Recognition Based on Multi-Feature Extraction with Context for User Intension 査読有り国際誌

Khawaritzmi Abdallah Ahmad, Dian Christy Silpani and Kaori Yoshida

Proc. of 17th International Conference on Innovative Computing, Information and Control (ICICIC2023) ( 142 ) 2023年08月

An Investigation of the Effects of Presentation Methods for Visual Elements and Thinking Styles on Text Comprehension 査読有り国際誌

Kouki Ueno, Kaori Yoshida and Naruki Shirahama

Proc. of 17th International Conference on Innovative Computing, Information and Control (ICICIC2023) ( 125 ) 2023年08月

A Feasibility Study on Hand Gesture Intention Interpretation Based on Gesture Detection and Speech Recognition 査読有り国際誌

Silpani Dian Christy, Suematsu Keishi, Yoshida Kaori

Journal of Advanced Computational Intelligence and Intelligent Informatics ( 富士技術出版株式会社 ) 26 ( 3 ) 375 - 381 2022年05月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

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

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85132786254&origin=inward

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年01月

Calibration Cost Reduction of Indoor Localization Using Bluetooth Low Energy Beacon 査読有り国際誌

As M., Shimizu H., Benaissa B., Yoshida K., Koppen M.

Journal of Advanced Computational Intelligence and Intelligent Informatics ( 富士技術出版株式会社 ) 26 ( 1 ) 97 - 106 2022年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

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

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85124460770&origin=inward

Hand Gesture Recognition by Hand Landmark Classification 査読有り国際誌

AHMAD Khawaritzmi Abdallah, SILPANI Dian Christy, YOSHIDA Kaori

International Symposium on Affective Science and Engineering ( 日本感性工学会 ) ISASE2022 ( 0 ) 1 - 4 2022年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

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

その他リンク： https://www.jstage.jst.go.jp/article/isase/ISASE2022/0/ISASE2022_1_34/_pdf

A First Prototype of an Emotional Smart Speaker 査読有り国際誌

Enrique A. de la Cal, Alberto Gallucci, José Ramón Villar, Kaori Yoshida, Mario Koeppen

Advances in Intelligent Systems and Computing 1401 304 - 313 2021年09月

A Feasibility Study on Hand Gesture Recognition in Natural Conversation 査読有り

Silpani D.C., Suematsu K., Yoshida K.

2021 5th IEEE International Conference on Cybernetics, CYBCONF 2021 85 - 90 2021年06月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

This paper proposes to approach natural hand gestures that occur in natural conversations. Hand gesture recognition in Human-Robot Interaction is considered a preliminary study for the next step, based on the vision system of a humanoid robot. We experimented as a feasibility study on hand gestures recognition in natural conversation. The conversations between an experimental assistant (the first author) and two different subjects (both female) were recorded in the experiment. Each conversation includes two people, one of whom is the subject of the hand gesture detection that appears during the conversation. We manually captured the hand gestures that appear naturally during the session and analyzed their duration and frequency of occurrence from each video. As a result of conversations with two subjects, it was clear that hand gestures significantly impact people's conversations. We observed that hand gestures with the same word or similar meaning appeared several times with the same intention, such as pointing at something and emphasizing a sentence or word. The topic of natural hand-gesture recognition by a robot will be a challenge. Cultural diversity makes the scope of natural hand-gesture broad, but this can be considered a topic in developing the knowledge of human-robot interaction in the future.

DOI： 10.1109/CYBCONF51991.2021.9464141

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85113788192&origin=inward

より良い未来のための社会実装

吉田香

人工知能 ( 一般社団法人人工知能学会 ) 36 ( 3 ) 335 - 335 2021年01月

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