杉本　麻樹 （スギモト　マキ）

Sugimoto, Maki

写真a

所属（所属キャンパス）

理工学部 情報工学科 （矢上）

職名

教授

HP
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学位 【 表示 ／ 非表示 】

  • 博士(工学), 電気通信大学

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研究分野 【 表示 ／ 非表示 】

  • 情報通信 / ヒューマンインタフェース、インタラクション

  • 情報通信 / エンタテインメント、ゲーム情報学

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著書 【 表示 ／ 非表示 】

  • 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年03月

    担当範囲: 8章「CGシステム」

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論文 【 表示 ／ 非表示 】

  • 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年03月

    　概要を見る

    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年03月

    　概要を見る

    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年03月

    　概要を見る

    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.

  • Assessing Individual Decision-Making Skill by Manipulating Predictive and Unpredictive Cues in a Virtual Baseball Batting Environment

    Tani Y., Kobayashi A., Masai K., Fukuda T., Sugimoto M., Kimura T.

    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）     775 - 776 2023年

    　概要を見る

    We propose a virtual reality (VR) baseball batting system for assessing individual decision-making skill based on swing judgement of pitch types and the underlying prediction ability by manipulating combinations of pitching motion and ball trajectory cues. Our analy-sis of data from 10 elite baseball players revealed highly accurate swing motions in conditions during which the batter made precise swing decisions. Delays in swing motion were observed in conditions during which predictive cues were mismatched. Our findings indicated that decision-making based on pitch type influences the inherent stability of decision and accuracy of hitting, and that most batters made decisions based on pitching motion cues rather than on ball trajectory.

  • Exploring Enhancements towards Gaze Oriented Parallel Views in Immersive Tasks

    Teo T., Sakurada K., Sugimoto M.

    Proceedings - 2023 IEEE Conference Virtual Reality and 3D User Interfaces, VR 2023 （Proceedings - 2023 IEEE Conference Virtual Reality and 3D User Interfaces, VR 2023）     620 - 630 2023年

    　概要を見る

    Parallel view is a technique that allows a VR user to see multiple locations at a time. It enables the user to control several remote or virtual body parts while seeing parallel views to solve synchronous tasks. However, these techniques only explored the benefits and drawbacks of a user performing different tasks. In this paper, we explored enhancements on a singular or asynchronous task by utilizing information obtained in parallel views. We developed three prototypes where parallel views are fixed, moving in symmetric order, or following the user's eye gaze. We conducted a user study to compare each prototype against traditional VR (without parallel views) in three types of tasks: object search and interaction tasks in a 1) simple environment and 2) complex environment, and 3) object distances estimation task. We found parallel views improved multi-embodiment while each technique helped different tasks. No parallel view provided a clean interface, thus improving spatial presence, mental effort, and user performance. However, participants' feedback highlighted potential usefulness and a lower physical effort by using parallel views to solve complicated tasks.

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競争的研究費の研究課題 【 表示 ／ 非表示 】

  • 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年04月
    -
    2020年03月

    文部科学省・日本学術振興会, 科学研究費助成事業, 杉本　麻樹, 若手研究(A), 補助金,  研究代表者

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担当授業科目 【 表示 ／ 非表示 】

  • 情報工学輪講

    2025年度

  • 複合現実感

    2025年度

  • 情報工学実験第２Ｂ

    2025年度

  • 情報工学実験第２Ａ

    2025年度

  • 開放環境科学課題研究

    2025年度

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所属学協会 【 表示 ／ 非表示 】

  • 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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