バイブロタクティルの拡張知覚のための精神物理データセット
Mostafa Hamidifard1, Samar Nikfarjad1, Hosein Pirmohammadi1
1Research Institute for Robotic, Artificial Intelligence, and Information Science (RAIIS), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Scientific data
|February 18, 2026
まとめ
この研究は,触覚知覚モデリングのための新しいデータセットを導入し,多様なヒトデータへのニーズに対応しています. このデータセットは,個体とセッションの間の振動知覚を捉え,触覚フィードバックシステムに関する将来の研究を支援します.
科学分野:
- ハプティックと人間とコンピュータの相互作用
- 感覚知覚と神経科学について
- データサイエンスと機械学習
背景:
- タクティルの知覚モデリングは重要ですが,個々の認知の違いのために挑戦的です.
- 現存する研究には,対象間,セッション間,およびセッション内での十分な変動性を持つ豊富なデータセットが不足しています.
- 正確な触覚知覚モデルの開発を進めるために,包括的なデータセットが必要です.
研究 の 目的:
- 振動パターンの人間の触覚知覚のための新しい,多様なデータセットを提示する.
- 先進的な触覚知覚モデルの開発を促進する.
- タクティルの知覚における個々の違いの研究を可能にし,タクティルの感覚フィードバックシステムの設計を参考にします.
主な方法:
- 2つのセッションで40人の参加者 (20人の女性,20人の男性) からデータを収集しました.
- 51の振動パターンの知覚された強度と位置を記録し,選択時間と信頼レベルを記録しました.
- 各参加者のアンソポメトリック,人口統計データ,バイオエレクトリック・インペデンス分析 (BIA) の結果を収集した.
主要な成果:
- 51の振動パターンの詳細な触覚知覚データを含むユニークなデータセットが現在利用可能になっています.
- このデータセットには,ヒトの触覚知覚における主体間,セッション間,およびセッション内の変動性が含まれています.
- 参加者データには,知覚された強度,ポジション,選択時間,自信,アンケート,体組成指標が含まれています.
結論:
- 提示されたデータセットは,触覚知覚モデリングを進めるための貴重なリソースです.
- 感覚知覚における個々の差異の研究を支援します.
- データは,触覚感覚フィードバック技術の設計と改善を導くことができます.
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