在GiroE-2024电动自行车多阶段比赛期间,来自非竞争运动员的智能手表数据
Paolo Bellagente1, Dennis Brandāo1, Salvatore Dello Iacono1
1University of Brescia - Information Engineering Department Via Branze 38 25123 Brescia (BS) Italy.
Data in brief
|January 26, 2026
概括
这项研究收集了智能手表在GiroE电动自行车活动期间的原始传感器数据. 该数据集支持对电动自行车应用程序的骑自行车者行为,道路状况和心率建模的研究.
科学领域:
- 运动科学 运动科学 运动科学
- 可穿戴技术可穿戴技术
- 数据科学数据科学数据科学
背景情况:
- 吉罗E活动提供了一个独特的环境,用于收集真实世界的电动自行车骑行数据.
- 配备多个传感器的智能手表可以捕获全面的生理和环境信息.
研究的目的:
- 呈现一个新的数据集的原始传感器读数从智能手表在电动自行车活动期间.
- 支持对骑自行车者的行为,道路状况分析和在现实世界骑自行车场景中的心率建模的研究.
主要方法:
- 使用了9个Garmin Fēnix 7智能手表,带有自定义数据日志应用程序.
- 从GNSS,心率 (PPG),气压高度计,指南针,加速度计和温度计传感器收集的最大采样频率的原始数据.
- 在与意大利锦标赛 (Giro d'Italia) 同步的GiroE-2024电动自行车赛期间收集的数据.
主要成果:
- 成功获得了来自电动自行车骑行者的多传感器原始数据的全面数据集.
- 数据集包括精确的位置,心率,高度,方向,运动和温度读数.
- 数据捕捉了现实世界骑自行车条件和生理反应的复杂性.
结论:
- 这一数据集为智能手表数据集成研究提供了有价值的基础知识.
- 它可以用于开发和验证骑自行车者的行为模型和生理反应.
- 这些数据对于临床试验和了解骑自行车时的正常生理条件至关重要.
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