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Arakawa Yutaka Last modified date：2024.04.26

Professor / Advanced Information and Communication Technology
Department of Advanced Information Technology
Faculty of Information Science and Electrical Engineering


Graduate School
Department of Advanced information Technology
Graduate School of Information Science and Electrical Engineering
Undergraduate School
Department of Electrical Engineering and Computer Science
School of Engineering
Administration Post
Other
Other
Other
Other


Homepage
https://kyushu-u.elsevierpure.com/en/persons/yutaka-arakawa
　Reseacher Profiling Tool　Kyushu University Pure
https://app.ait.kyushu-u.ac.jp/
SIG - Applied Human Information Systems (Arakawa-Mine-Fukushima Laboratory): For research on human information systems, we collaborate with Associate Professor Mine (recommendation systems, data analysis on education and ITS), and Associate Professor Fukushima (various information presentation technologies such as AR/VR and tactile sensation) . .
https://arakawa-lab.com/
Academic Degree
Doctor of Engineering, Ph.D
Country of degree conferring institution (Overseas)
Yes　Bachelor　Master　Doctor
Field of Specialization
Sensors, IoT(Internet of Things), Activity recognition, Behavior Change Support System, ICT service, networking
ORCID(Open Researcher and Contributor ID)
0000-0002-7156-9160
Total Priod of education and research career in the foreign country
02years03months
Outline Activities
Main research topics are kind of cyber-physical systems (CPS: Cyber-Physical Systems) that improves human life, where real-world sensing technology, cloud data processing technology, networking technology, and a variety of information technologies are combined dynamically. We call such a system as Humanophilic systems. Humanophilic is a coined word that combines the suffix “philic”, which means human-friendly. In the laboratory, we are focusing on the research on the behavior recognition of people using sensors (IoT) and machine learning (AI), and we are conducting a wide range of applications from the development of new sensors to the implementation of applications in order to realize the humanophilic systems.

In addition to the sensing of human external states (behavior and activity), the recognization of human internal states (emotions and stress) becomes a part of topics. We always consider and discuss what kind of sensors can be used, what kind of algorithms are most suitable. In recent years, as research ahead of behavior recognition, we start to focus on the behavior change support system (BCSS) because information technology can change our behavior better.
Research
Research Interests
  • Behavior Change Support System
    keyword : Adaptive notification, Context-aware Dialog System, ICT-based gimmick
    2019.04.
  • Learning Activity Recognition and Intervention
    keyword : Eye Tracking, Web control analytics, Confidence estimation
    2019.04.
  • Internal State Sensing by sensors
    keyword : Stress Estimation, Emotion Estimation, Wearable
    2019.04.
  • Energy Harvest Place Recognition
    keyword : Energy Harvesting, Solar, Localization
    2019.04.
  • Activity Recognition by sensors
    keyword : Activity Recognition，Wearable
    2019.04.
Academic Activities
Reports
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Papers
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1. Masaki Gogami, Yuki Matsuda, Yutaka Arakawa, Keiichi Yasumoto, Detection of Careless Responses in Online Surveys Using Answering Behavior on Smartphone, IEEE Access, 10.1109/access.2021.3069049, 9, 53205-53218, 2021.05, Some respondents make careless responses due to the 'satisficing,' which is an attempt to complete a questionnaire as quickly and easily as possible. To obtain results that reflect a fact, detecting satisficing and excluding the responses with satisficing from the analysis targets are required. One of the devised methods detects satisficing by adding questions that check violations of instructions and inconsistencies. However, this approach may cause respondents to lose their motivation and prompt them to satisficing. Additionally, a deep learning model that automatically answers these questions was reported. This threatens the reliability of the conventional method. To detect careless responses without inserting such screening questions, machine learning (ML) detection using data obtained from answer results was attempted in a previous study, with a detection rate of 55.6%, which is not sufficient from the viewpoint of practicality. Therefore, we hypothesized that a supervised ML model with a higher detection rate could be constructed by using on-screen answering behavior as features. However, (1) no existing questionnaire system can record on-screen answering behavior and (2) even if the answering behavior can be recorded, it is unclear which answering behavior features are associated with satisficing. We developed an answering behavior recording plug-in for LimeSurvey, an online questionnaire system used all over the world, and collected a large amount of data (from 5,692 people) in Japan. Then, a variety of features were examined and generated from answering behavior, and we constructed ML models to detect careless responses. We call this detection method the ML-ABS (ML-based answering behavior scale). Evaluation by cross-validation demonstrated that the detection rate for careless responses was 85.9%, which is much higher than the previous ML method. Among the various features we proposed, we found that reselecting the Likert scale and scrolling particularly contributed to the detection of careless responses..
2. Zhihua Zhang, Juliana Miehle, Yuki Matsuda, Manato Fujimoto, Yutaka Arakawa, Keiichi Yasumoto, Wolfgang Minker, Exploring the Impacts of Elaborateness and Indirectness in a Behavior Change Support System, IEEE Access, 10.1109/access.2021.3079473, 9, 74778-74788, 2021.05, Numerous technologies exist for promoting a healthier lifestyle. These technologies collectively referred to as "Behavior Change Support Systems". However, the majority of existing apps use quantitative data representation. Since it is difficult to understand the meaning behind quantitative data, this approach has been suggested to lower users' motivation and fail to promote behavior change. Therefore, an interpretation of quantitative data needs to be provided as a supplement. However, different descriptions of the same data may lead to different outcomes. In this paper, we explore the impact of different communication styles for interpretations of quantitative data on behavior change by developing and evaluating Walkeeper - a web-based app that provides interpretations of the users' daily step counts using different levels of elaborateness and indirectness with the aim of promoting walking. Through the quantitative analysis and results of a user study, we contribute new knowledge on designing such interpretations for quantitative data..
3. Mohamed A. Abdelwahab, Shizuo Kaji, Maiya Hori, Shigeru Takano, Yutaka Arakawa, Rin-Ichiro Taniguchi, Measuring “Nigiwai” From Pedestrian Movement, IEEE Access, 10.1109/ACCESS.2021.3056698, 9, 24859-24871, 2021.02, The analysis of the movement of people in a shopping area with the aim of improving marketing is an important research topic. Many conventional methods are dependent on the density of people in the area, which is easily estimated by counting the people entering or exiting the area. However, a high density does not always mean an increase in activity, as certain people are simply passing the area at a given time. The primary goal of this study was to introduce a set of indicators for measuring the bustle of the area, which we call 'Nigiwai,' from pedestrian movement by using an analogy from classical kinematics. Such indicators can be used to measure the impact of promotional events and to optimize the design of the area. Our novel indicators were evaluated with simulated pedestrian scenarios and were demonstrated to distinguish shopping scenarios from those in which people move around without shopping successfully, even when the latter scenarios had much higher densities. The indicators were computed solely from the pedestrian trajectory, which can easily be obtained from ordinary sensors using deep learning-based techniques. As a demonstration with real data, we applied our method to a video of a street and provided a visualization of the indicators..
4. Hiroyuki Miyagi, Masahiro Hayashitani, Daisuke Ishii, Yutaka Arakawa and Naoaki Yamanaka,, Advanced Wavelength Reservation Method based on Deadline-Aware Scheduling for Lambda Grid Networks, Journal of Lightwave Technology, Vol. 25, No. 10, pp. 2904-2910, 2007.10.
5. Yutaka Arakawa and Naoaki Yamanaka,, QoS Differentiation Scheme with Multiple Burst Transmission and Virtual Resource Reservation for Optical Burst Switching Networks, Journal of Optical Networking, Vol. 6, Issue. 8, pp. 1003-1013, 2007.07.
Works, Software and Database
1. .
Presentations
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Membership in Academic Society
  • Association for Computing Machinery
  • The Institute of Electrical and Electronics Engineers, Inc.
  • Information Processing Society of Japan
  • The Institute of Electronics, Information and Communication Engineers
Awards
  • Honorable Mention Award
  • Excellent Paper Award
  • Awards for Science and Technology (Research Category), The Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology
  • Best Demonstration Award
  • Honorable Mention Award
  • 山下記念研究賞
  • Ando Incentive Prize for the Study of Electronics
  • Mobile App Competition 2nd place
  • 長尾真記念特別賞
  • 研究者特別賞
  • Noguchi Award
  • センサアプリケーションアイデアコンテスト テクニカル賞
  • Best Demo Award
  • IPSJ/IEEE-CS Young Computer Researcher Award
  • Best Teaching Award
  • Best Demonstration Award
Educational
Educational Activities
- Special Lecture on Designing Social Infrastructure based on ICT
- Operating System
- Distributed System


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