Learning to Predict Human Stress Level with Incomplete Sensor Data from Wearable Devices
Learning to Predict Human Stress Level with Incomplete Sensor Data from Wearable Devices
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DOI：
10.1145/3357384.3357831
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发表时间：
2019-11
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影响因子：
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通讯作者：
Jyun-Yu Jiang;Zehan Chao;A. Bertozzi;Wei Wang;S. Young;D. Needell
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作者：
Jyun-Yu Jiang;Zehan Chao;A. Bertozzi;Wei Wang;S. Young;D. Needell
压力是现代生活中常见的问题，它会带来心理和身体上的疾病。可穿戴传感器通常用于研究身体记录与精神状态之间的关系。虽然可穿戴设备产生的传感器数据为预测医学提供了识别人们压力的机会，但在实践中，这些数据通常是复杂和模糊的，而且往往是碎片化的。在本文中，我们提出了数据补全与昼夜规则（DCDR）和时间分层注意网络（THAN）来解决碎片化数据问题，并利用恢复的传感器数据预测人类的压力水平。我们将碎片化建模为稀疏性问题。首先应用基于低秩假设的核范数最小化方法推导了具有人类行为日模式的未观测传感器数据。然后，利用具有注意机制的递归神经网络对重构传感器数据中的时间结构信息进行建模，从而推断出预测的应力水平。这项研究的数据来自75名本科生（来自一项更大的研究的样本），他们提供了智能腕带的传感器数据。他们还完成了每周的压力调查，作为他们压力水平的基本事实标签。本次调查持续了12周，传感器记录也在此期间。实验结果表明，我们的方法在数据补全和应力水平预测方面都明显优于传统方法。此外，深入分析进一步表明了我们的方法的有效性和稳健性。
Stress is a common problem in modern life that can bring both psychological and physical disorder. Wearable sensors are commonly used to study the relationship between physical records and mental status. Although sensor data generated by wearable devices provides an opportunity to identify stress in people for predictive medicine, in practice, the data are typically complicated and vague and also often fragmented. In this paper, we propose DataCompletion with Diurnal Regularizers (DCDR) and TemporallyHierarchical Attention Network (THAN) to address the fragmented data issue and predict human stress level with recovered sensor data. We model fragmentation as a sparsity issue. The nuclear norm minimization method based on the low-rank assumption is first applied to derive unobserved sensor data with diurnal patterns of human behaviors. A hierarchical recurrent neural network with the attention mechanism then models temporally structural information in the reconstructed sensor data, thereby inferring the predicted stress level. Data for this study were from 75 undergraduate students (taken from a sample of a larger study) who provided sensor data from smart wristbands. They also completed weekly stress surveys as ground-truth labels about their stress levels. This survey lasted 12 weeks and the sensor records are also in this period. The experimental results demonstrate that our approach significantly outperforms conventional methods in both data completion and stress level prediction. Moreover, an in-depth analysis further shows the effectiveness and robustness of our approach.