Sarker Hillol, Tyburski Matthew, Rahman Md Mahbubur, Hovsepian Karen, Sharmin Moushumi, Epstein David H, Preston Kenzie L, Furr-Holden C Debra, Milam Adam, Nahum-Shani Inbal, al'Absi Mustafa, Kumar Santosh
University of Memphis.
NIDA Intramural Research Program.
Proc SIGCHI Conf Hum Factor Comput Syst. 2016 May;2016:4489-4501. doi: 10.1145/2858036.2858218.
Management of daily stress can be greatly improved by delivering sensor-triggered just-in-time interventions (JITIs) on mobile devices. The success of such JITIs critically depends on being able to mine the time series of noisy sensor data to find the most opportune moments. In this paper, we propose a time series pattern mining method to detect significant stress episodes in a time series of discontinuous and rapidly varying stress data. We apply our model to 4 weeks of physiological, GPS, and activity data collected from 38 users in their natural environment to discover patterns of stress in real-life. We find that the duration of a prior stress episode predicts the duration of the next stress episode and stress in mornings and evenings is lower than during the day. We then analyze the relationship between stress and objectively rated disorder in the surrounding neighborhood and develop a model to predict stressful episodes.
通过在移动设备上提供传感器触发的即时干预（JITIs），可以大大改善日常压力管理。此类即时干预的成功关键取决于能否挖掘有噪声的传感器数据的时间序列，以找到最恰当的时机。在本文中，我们提出了一种时间序列模式挖掘方法，用于在不连续且快速变化的压力数据时间序列中检测显著的压力事件。我们将模型应用于从38名用户在自然环境中收集的4周生理、GPS和活动数据，以发现现实生活中的压力模式。我们发现，先前压力事件的持续时间可预测下一个压力事件的持续时间，且早晚的压力低于白天。然后，我们分析了压力与周边社区客观评定的无序状态之间的关系，并开发了一个预测压力事件的模型。
