使用可穿戴传感器 (PAAWS) 的身体活动评估数据集:标记的实验室和自由生活加速仪数据
Veronika Potter1, Hoan Tran1, Daniel Mobley2
1Northeastern University, USA.
本研究介绍了PAAWS R1数据集,该数据集包含多模式传感器数据,用于在现实环境中准确识别身体活动和睡眠模式. 本资源旨在改进人类活动识别算法,以改善健康研究和移动健康干预.
科学领域:
- 人类活动识别 人类活动识别
- 可穿戴式传感器技术
- 数字健康数字健康
背景情况:
- 睡眠不足和久坐不动行为与慢性疾病和降低生活质量有关.
- 可穿戴式传感器可以监测身体活动,久坐行为和自由生活条件下的睡眠.
- 由于基于实验室的培训,当前的人类活动识别算法与现实数据作斗争.
研究的目的:
- 引入PAAWS R1数据集,这是一个用于识别人类活动的多式联运,多传感器资源.
- 提供数据集,使得可以直接比较不同收集协议和自由生活条件的算法.
- 促进健康研究和移动计算干预 robust 算法的开发.
主要方法:
- 从252个人那里收集了4个小时的半自然主义活动.
- 从20名成年人那里收集了7天的24小时自由生活活动.
- 标注的清醒活动与秒速视频地面真相标签和睡眠阶段从PSG数据.
主要成果:
- PAAWS R1数据集包括多样化的现实活动和睡眠数据.
- 高分辨率的注释可以捕捉到现实的活动过渡和睡眠阶段.
- 该数据集支持在各种收集设置中进行直接算法比较.
结论:
- PAAWS数据集是促进人类活动识别的宝贵资源.
- 它可以为自由生活条件开发更强大的算法.
- 这项工作支持改善健康研究和创建新型移动健康应用程序.
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