Wakayama University · Faculty of Systems Engineering

Research keywordsVR学習支援・AR学習支援・動作データ機械学習・3D点群GNN・樹木枝構造再現・ドローン方向感覚支援・LiDAR点群モデリング・振動フィードバック

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• New Training Method for Non-dominant Hand Pitching Motion based on Reversal Trajectory of Dominant Hand Pitching Motion using AR and Vibration2023 · Masato SOGA, Taiki MORI (担当区分： 筆頭著者, 責任著者 )
• 人工知能学大事典，第15章 教育支援，15-19項目 「身体スキルの学習」2017 · 曽我 真人( 担当： 分担執筆)
• Investigation of the Influence of Social Rank and Spatial Arrangement Using the Psychophysical Experiments2016 · According to social rank, high rank people are located "Up" and low rank people are located "Low". "Up" and "Low" are concept of spatial arrangement. However, they are used as metaphors to represent the social rank. This study describes psychophysical experiments to confirm whether there is a link between the "Up and Low" of the spatial arrangement and "High and Low" of the social rank. In the experiments, two randomly words from three words that have social ranks were shown to subjects vertically on the display. We taught the subjects a target word at the beginning of the experiments. We asked the subjects to check whether the target word appears. If the target word appeared, subjects pressed the button as soon as possible. We obtained reaction times of the button press by psychophysical experiments. We assumed a linear model that contains three elements: "Time which differs between the target word", "Time which differs between the spatial arrangement of the words to be presented", "Time which differs between the social rank". The results show that the reaction time in case that social rank coincides with spatial arrangement is significantly shorter than the reaction time in case that social rank does not coincide with spatial arrangement.
• Development of a learning environment for novices' erhu playings2014 · In this paper, we describe the development of a learning environment for novices' erhu playings. In case of self-learning, the novices who want to learn playing bowed string instruments give up because they get into a wrong habit and don't understand how to play. Therefore, our goal is to develop a learning environment for novices. We focus on the erhu in bowed string instruments. The system we developed diagnoses finger positions and bow strokes by using magnetic position sensors and gives awareness of errors by showing finger positions and bow strokes on virtual 3D space. In case of bow strokes, the system shows how to improve bow strokes by using arrows. Moreover, the system shows how and where the errors occur on score. Accordingly, the novices can recognize and improve their errors by using this system. In future works, we will conduct evaluation experiments to disclose learning effects by using this system.

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