用智能手表的旋转矢量数据，更准估算办公室久坐时间。
Smartwatch-Based Sitting Time Estimation in Real-World Office Settings
- 用欧拉角推导的旋转矢量表示运动动态
- 34小时数据实验显示性能提升
- 适合健康监测与久坐干预研究者
久坐行为是重大公共卫生风险，与肥胖、心血管疾病等慢性病密切相关。准确估算久坐时间对个体健康管理至关重要。本研究针对真实办公场景，采集智能手表惯性测量单元（IMU）在日常工作中获取的信号。提出一种新方法，通过引入由欧拉角导出的旋转矢量序列作为运动动态的新表征，从IMU信号中估计久坐时间。在34小时的数据集上实验表明，利用旋转矢量序列可显著提升算法性能，凸显其在自然环境中的鲁棒性潜力。
原文摘要 · Abstract (English)
Sedentary behavior poses a major public health risk, being strongly linked to obesity, cardiovascular disease, and other chronic conditions. Accurately estimating sitting time is therefore critical for monitoring and improving individual health. This work addresses the problem in real-world office settings, where signals from the inertial measurement units (IMU) on a smartwatch were collected from office workers during their daily routines. We propose a method that estimates sitting time from the IMU signals by introducing the use of rotation vector sequences, derived from Euler angles, as a novel representation of movement dynamics. Experiments on a 34-hour dataset demonstrate that exploiting rotation vector sequences improves algorithm performance, highlighting their potential for robust sitting time estimation in natural environments.
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