Islam Tanvir, Washington Peter
Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI 96822, USA.
Appl Sci (Basel). 2023 Nov;13(21). doi: 10.3390/app132112035. Epub 2023 Nov 4.
Stress is widely recognized as a major contributor to a variety of health issues. Stress prediction using biosignal data recorded by wearables is a key area of study in mobile sensing research because real-time stress prediction can enable digital interventions to immediately react at the onset of stress, helping to avoid many psychological and physiological symptoms such as heart rhythm irregularities. Electrodermal activity (EDA) is often used to measure stress. However, major challenges with the prediction of stress using machine learning include the subjectivity and sparseness of the labels, a large feature space, relatively few labels, and a complex nonlinear and subjective relationship between the features and outcomes. To tackle these issues, we examined the use of model personalization: training a separate stress prediction model for each user. To allow the neural network to learn the temporal dynamics of each individual's baseline biosignal patterns, thus enabling personalization with very few labels, we pre-trained a one-dimensional convolutional neural network (1D CNN) using self-supervised learning (SSL). We evaluated our method using the Wearable Stress and Affect Detection(WESAD) dataset. We fine-tuned the pre-trained networks to the stress-prediction task and compared against equivalent models without any self-supervised pre-training. We discovered that embeddings learned using our pre-training method outperformed the supervised baselines with significantly fewer labeled data points: the models trained with SSL required less than 30% of the labels to reach equivalent performance without personalized SSL. This personalized learning method can enable precision health systems that are tailored to each subject and require few annotations by the end user, thus allowing for the mobile sensing of increasingly complex, heterogeneous, and subjective outcomes such as stress.
压力被广泛认为是导致各种健康问题的主要因素。利用可穿戴设备记录的生物信号数据进行压力预测是移动传感研究中的一个关键研究领域，因为实时压力预测可以使数字干预措施在压力开始时立即做出反应，有助于避免许多心理和生理症状，如心律不齐。皮肤电活动（EDA）常被用于测量压力。然而，使用机器学习进行压力预测面临的主要挑战包括标签的主观性和稀疏性、特征空间大、标签相对较少，以及特征与结果之间复杂的非线性和主观关系。为了解决这些问题，我们研究了模型个性化的应用：为每个用户训练一个单独的压力预测模型。为了让神经网络学习每个个体基线生物信号模式的时间动态，从而能够用极少的标签实现个性化，我们使用自监督学习（SSL）对一维卷积神经网络（1D CNN）进行了预训练。我们使用可穿戴压力与情感检测（WESAD）数据集对我们的方法进行了评估。我们将预训练网络微调至压力预测任务，并与没有任何自监督预训练的等效模型进行了比较。我们发现，使用我们的预训练方法学习到的嵌入优于有监督的基线模型，所需的标记数据点显著更少：使用SSL训练的模型在达到同等性能时所需的标签不到30%，而无需个性化的SSL。这种个性化学习方法可以实现针对每个个体量身定制的精准健康系统，并且最终用户只需很少的注释，从而能够对压力等日益复杂、多样和主观的结果进行移动传感。
