cStress: Towards a Gold Standard for Continuous Stress Assessment in the Mobile Environment.
cStress: Towards a Gold Standard for Continuous Stress Assessment in the Mobile Environment.
复制标题
DOI：
10.1145/2750858.2807526
复制
发表时间：
2015-09
期刊：
影响因子：
--
通讯作者：
Kumar S
中科院分区：
文献类型：
--
作者：
Hovsepian K;al'Absi M;Ertin E;Kamarck T;Nakajima M;Kumar S
移动的健康方面的最新进展已经产生了几种用于从可穿戴传感器推断压力的新模型。但是，缺乏黄金标准是临床使用可穿戴传感器获得的连续应力测量的主要障碍。在本文中，我们提出了一个压力模型（称为cStress），该模型经过精心开发，关注计算建模的每一步，包括数据收集，筛选，清洗，过滤，特征计算，归一化和模型训练。更重要的是，cStress是使用从21名参与者的严格实验室研究中收集的数据进行训练的，并在两个独立收集的数据集上进行验证-在26名参与者的实验室研究和20名参与者的为期一周的实地研究中。在测试中，该模型获得了89%的召回率和5%的假阳性率。在现场数据上，该模型能够以72%的准确率预测每个瞬时自我报告。
Recent advances in mobile health have produced several new models for inferring stress from wearable sensors. But, the lack of a gold standard is a major hurdle in making clinical use of continuous stress measurements derived from wearable sensors. In this paper, we present a stress model (called cStress) that has been carefully developed with attention to every step of computational modeling including data collection, screening, cleaning, filtering, feature computation, normalization, and model training. More importantly, cStress was trained using data collected from a rigorous lab study with 21 participants and validated on two independently collected data sets — in a lab study on 26 participants and in a week-long field study with 20 participants. In testing, the model obtains a recall of 89% and a false positive rate of 5% on lab data. On field data, the model is able to predict each instantaneous self-report with an accuracy of 72%.