Sugimoto, Maki

写真a

Affiliation

Faculty of Science and Technology, Department of Information and Computer Science （Yagami）

Position

Professor

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Academic Degrees 【 Display / hide 】

  • 博士(工学), The University of Electro-Communications

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Research Areas 【 Display / hide 】

  • Informatics / Human interface and interaction

  • Informatics / Entertainment and game informatics

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Books 【 Display / hide 】

  • コンピュータグラフィックス［改訂新版］

    金井 崇, 杉本 麻樹, CGーARTS協会, 2015.03

    Scope: 8章「CGシステム」

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Papers 【 Display / hide 】

  • Effects of the Number of Bodies on Ownership of Multiple Bodies

    Kondo R., Sugimoto M.

    ACM International Conference Proceeding Series （ACM International Conference Proceeding Series)     314 - 316 2023.03

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    Virtually reality can induce body ownership (Sense of being one's own body) of a not innate body or multiple bodies. However, the relationship between the number of bodies and body ownership has not been clarified. In a study that investigated body ownership for one, two, and four virtual bodies, the greater the number of bodies, the closer the body ownership tended to approach the strength of ownership for a single body. Therefore, in the present study, we investigated whether increasing the number of bodies strengthens body ownership for multiple bodies. In the experiment, multiple virtual bodies that moved in synchronization with the participant's movement were lined up in a single file line, and the participant observed the multiple virtual bodies through a head-mounted display from the position of the hindmost virtual body. We measured body ownership and drifts in self-location. Our results showed that contrary to expectations, the greater the number of bodies, the weaker body ownership.

  • Investigating Effects of Facial Self-Similarity Levels on the Impression of Virtual Agents in Serious/Non-Serious Contexts

    Niwa M., Masai K., Yoshida S., Sugimoto M.

    ACM International Conference Proceeding Series （ACM International Conference Proceeding Series)     221 - 230 2023.03

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    Recent technological advances have enabled the use of AI agents to assist with human tasks and augment human cognitive abilities in a variety of contexts, including decision making. It is critical that users trust these AI agents in order to use them effectively. Given that people tend to trust other people who are similar to themselves, incorporating features of one's own face into the AI agent's face may improve one's trust in the AI agent. However, it is still unclear how impressions differ when comparing agents with the same appearance as one's own and some similarities under the same conditions. Recognizing the appropriate level of similarity when using a self-similar agent is important for establishing a trustworthy agent relationship between people and the AI agent. Therefore, we investigated the effect of the degree of self-similarity of the face of the AI agent on the user's trust in the agent. We examined users' impressions of four AI agents with different degrees of face self-similarity in different scenarios. The results showed that the AI agent, whose similarity to the user's facial feature was slightly recognizable but not obvious, received higher ratings on the feeling of closeness, attractiveness, and facial preferences. These self-similar AI agents were also more trustworthy in everyday non-serious decisions and were more likely to improve people's trustworthiness in such situations. Finally, we discuss the potential applications of our findings to design real-world AI agents.

  • Exploring the Effect of Transfer Learning on Facial Expression Recognition using Photo-Reflective Sensors embedded into a Head-Mounted Display

    Nakamura F., Sugimoto M.

    ACM International Conference Proceeding Series （ACM International Conference Proceeding Series)     317 - 319 2023.03

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    As one of the techniques to recognize head-mounted display (HMD) user's facial expressions, the photo-reflective sensor (PRS) has been employed. Since the classification performance of PRS-based method is affected by rewearing an HMD and difference in facial geometry for each user, the user have to perform dataset collection for each wearing of an HMD to build a facial expression classifier. To tackle this issue, we investigate how transfer learning improve within-user and cross-user accuracy and reduce training data in the PRS-based facial expression recognition. We collected a dataset of five facial expressions (Neutral, Smile, Angry, Surprised, Sad) when participants wore the PRS-embedded HMD five times. Using the dataset, we evaluated facial expression classification accuracy using a neural network with/without fine tuning. Our result showed fine tuning improved the within-user and cross-user facial expression classification accuracy compared with non-fine-tuned classifier. Also, applying fine tuning to the classifier trained with the other participant dataset achieved higher classification accuracy than the non-fine-tuned classifier.

  • Sensory Attenuation with a Virtual Robotic Arm Controlled Using Facial Movements

    Fukuoka M., Nakamura F., Verhulst A., Inami M., Kitazaki M., Sugimoto M.

    IEEE Transactions on Visualization and Computer Graphics （IEEE Transactions on Visualization and Computer Graphics)   2023

    ISSN  10772626

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    When humans generate stimuli voluntarily, they perceive the stimuli more weakly than those produced by others, which is called sensory attenuation (SA). SA has been investigated in various body parts, but it is unclear whether an extended body induces SA. This study investigated the SA of audio stimuli generated by an extended body. SA was assessed using a sound comparison task in a virtual environment. We prepared robotic arms as extended bodies, and the robotic arms were controlled by facial movements. To evaluate the SA of robotic arms, we conducted two experiments. Experiment 1 investigated the SA of the robotic arms under four conditions. The results showed that robotic arms manipulated by voluntary actions attenuated audio stimuli. Experiment 2 investigated the SA of the robotic arm and innate body under five conditions. The results indicated that the innate body and robotic arm induced SA, while there were differences in the sense of agency between the innate body and robotic arm. Analysis of the results indicated three findings regarding the SA of the extended body. First, controlling the robotic arm with voluntary actions in a virtual environment attenuates the audio stimuli. Second, there were differences in the sense of agency related to SA between extended and innate bodies. Third, the SA of the robotic arm was correlated with the sense of body ownership.

  • Investigating the Minimal Condition of the Dynamic Invisible Body Illusion

    Kondo R., Sugimoto M.

    Proceedings - 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2023 （Proceedings - 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2023)     601 - 602 2023

    　View Summary

    Visual-tactile synchronization or visual-motor synchronization makes a non-innate body feel like one's own body (illusory body ownership). A recent study has shown that body ownership is induced to an empty space between the hands and feet from the motion of only hands and feet (dynamic invisible body illusion). However, it is unclear whether both hands and feet are necessary to induce the illusion. In this study, we investigated the minimal condition for the dynamic invisible body illusion by manipulating the presentation of hands and feet. Our results suggest that both hands and feet are necessary for the illusion.

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Papers, etc., Registered in KOARA 【 Display / hide 】

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Reviews, Commentaries, etc. 【 Display / hide 】

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Research Projects of Competitive Funds, etc. 【 Display / hide 】

  • 没入型バーチャルリアリティ環境における表情認識技術の構築

    2016.04
    -
    2020.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Young Scientists (A), Principal investigator

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Courses Taught 【 Display / hide 】

  • RECITATION IN INFORMATION AND COMPUTER SCIENCE

    2024

  • MIXED REALITY

    2024

  • LABORATORIES IN INFORMATION AND COMPUTER SCIENCE 2B

    2024

  • LABORATORIES IN INFORMATION AND COMPUTER SCIENCE 2A

    2024

  • INTRODUCTION TO INFORMATION AND COMPUTER SCIENCE

    2024

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Memberships in Academic Societies 【 Display / hide 】

  • 日本バーチャルリアリティ学会

    　

  • 情報処理学会

    　

  • 電子情報通信学会

    　

  • 計測自動制御学会

    　
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