手动手势的机械图形数据集采集使用加速度计和陀螺仪
Khalid A Abbas1,2, Mofeed Turky Rashid1, Luigi Fortuna3
1Electrical Engineering Department, University of Basrah, Basrah, Iraq.
Data in brief
|June 19, 2025
概括
这项研究引入了用于人与计算机交互研究的新机械图形 (MMG) 数据集,其中包括由可穿戴传感器捕获的11种手势. 该数据集有助于开发用于手势识别和基于MMG的控制系统的AI模型.
科学领域:
- 生物医学工程 生物医学工程
- 人与计算机的交互
- 信号处理 信号处理
背景情况:
- 机械肌谱 (MMG) 提供非侵入性肌肉活动测量,与其他技术相比具有优势.
- 现有的MMG数据集可能缺乏先进的HCI应用的多样性或特定特性.
- 可穿戴式传感器技术为捕获生理信号提供了新的可能性.
研究的目的:
- 为人机交互 (HCI) 研究引入一个新的,全面的机械图 (MMG) 信号数据集.
- 为开发和验证用于手势识别和控制的AI模型提供丰富的资源.
- 促进基于MMG的系统的参与者独立评估.
主要方法:
- 收集了43名参与者 (18-69岁) 的6轴加速度计和陀螺仪数据.
- 利用一种新的可穿戴系统,记录手腕曲肌和伸展肌的MMG信号.
- 每个参与者记录了50次11个不同的手势的重复,每人共计550个样本.
主要成果:
- 成功创建了一个包含11个手势 (如拍手,拳头,手腕运动) 的数据集.
- 该数据集包括适合机器学习的人口信息和详细的手势数据.
- 这些数据有助于开发基于MMG的控制器和用于手势识别的AI模型.
结论:
- 拟议的MMG数据集是HCI研究,AI模型开发和系统验证的宝贵资源.
- 这一数据集可以在人机交互,游戏,辅助技术和医疗保健领域推进应用.
- 这些数据支持使用参与者独立方法评估现有数据集和人工智能模型.
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