一个精神物理数据集,用于振动感应增强感知
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
概括
这项研究引入了用于触觉感知建模的新型数据集,解决了对各种人类数据的需求. 该数据集捕捉了个人和会话之间的振动感知,有助于未来对触觉反系统的研究.
科学领域:
- 触觉和人机交互的人机交互
- 感官感知与神经科学 感官感知与神经科学
- 数据科学和机器学习
背景情况:
- 触觉感知建模至关重要,但由于个人的认知差异,具有挑战性.
- 现有的研究缺乏丰富的数据集,具有足够的学科间,学期间和学期内可变性.
- 需要一个全面的数据集来推动准确的触觉感知模型的开发.
研究的目的:
- 为人类对振动模式的触觉感知提供一个新的,多样化的数据集.
- 促进先进的触觉感知模型的开发.
- 为了研究触觉感知中的个体差异,并为触觉感知反系统的设计提供信息.
主要方法:
- 在两个会议中收集了40名参与者的数据 (20名女性,20名男性).
- 记录了51个振动模式的感知强度和位置,以及选择时间和置信级别.
- 收集人体测量,人口统计数据和生物电阻分析 (BIA) 对每个参与者的结果.
主要成果:
- 现在可以获得一个独特的数据集,其中包含51种振动模式的详细触觉感知数据.
- 该数据集包括人类触觉感知中的跨主体,跨会话和内会话变化.
- 参与者数据包括感知强度,位置,选择时间,信心,问卷和身体组成指标.
结论:
- 这一数据集是推进触觉感知建模的宝贵资源.
- 它支持对感官感知中的个人差异的研究.
- 这些数据可以指导触感反技术的设计和改进.
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