背景:个人感应,利用从生态环境中患者的可穿戴设备被动和近乎连续地收集的数据,是监测情绪障碍(MD)的有希望的范例,全球疾病负担的主要决定因素。然而,收集和注释可穿戴数据是资源密集型的。因此,这种研究通常只能招募几十名患者。这构成了将现代监督机器学习技术应用于MD检测的主要障碍之一。
目的:在本文中,我们克服了这一数据瓶颈,并在自监督学习(SSL)的最新进展的基础上,从可穿戴设备数据中推进了对急性MD发作的检测.这种方法利用未标记的数据在预训练期间学习表示,随后被用于监督任务。
方法:我们收集了使用EmpaticaE4腕带记录的开放访问数据集,与MD监测无关,个人感知任务——从超级马里奥玩家的情感识别到本科生的压力检测——并设计了一个预处理管道,执行体内/体外检测,睡眠/唤醒检测,分割,和(可选地)特征提取。161例E4记录的受试者,我们引入了E4SelfLearning,迄今为止最大的开放访问集合,和它的预处理管道。我们开发了一种新颖的E4定制变压器(E4mer)架构,作为SSL和完全监督学习的蓝图;我们评估了自我监督预培训是否以及在何种条件下导致了对完全监督基线的改进(即,完全监督的E4mer和预深度学习算法)从64个记录片段中检测急性MD发作(n=32,50%,急性,n=32,50%,稳定)患者。
结果:使用我们的新型E4mer或极端梯度增强(XGBoost),SSL的性能明显优于完全监督的管道:n=3353(81.23%)对n=3110(75.35%;E4mer)和n=2973(72.02%;XGBoost)从总共4128个片段中正确分类了记录片段。SSL性能与用于预训练的特定代理任务密切相关,以及无标签的数据可用性。
结论:我们发现SSL,一种范式,其中模型在未标记的数据上进行预训练,在部署到感兴趣的有监督目标任务之前不需要人工注释,有助于克服注释瓶颈;预训练代理任务的选择和预训练的未标记数据的大小是SSL成功的关键决定因素。我们介绍了E4mer,可以用于SSL,并分享了E4SelfLearning系列,连同它的预处理管道,这可以促进和加快未来对个人感知SSL的研究。
BACKGROUND: Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders (MDs), a major determinant of the worldwide disease burden. However, collecting and annotating wearable data is resource intensive. Studies of this kind can thus typically afford to recruit only a few dozen patients. This constitutes one of the major obstacles to applying modern supervised machine learning techniques to MD detection.
OBJECTIVE: In this paper, we overcame this data bottleneck and advanced the detection of acute MD episodes from wearables\' data on the back of recent advances in self-supervised learning (SSL). This approach leverages unlabeled data to learn representations during pretraining, subsequently exploited for a supervised task.
METHODS: We collected open access data sets recording with the Empatica E4 wristband spanning different, unrelated to MD monitoring, personal sensing tasks-from emotion recognition in Super Mario players to stress detection in undergraduates-and devised a preprocessing pipeline performing on-/off-body detection, sleep/wake detection, segmentation, and (optionally) feature extraction. With 161 E4-recorded subjects, we introduced E4SelfLearning, the largest-to-date open access collection, and its preprocessing pipeline. We developed a novel E4-tailored transformer (E4mer) architecture, serving as the blueprint for both SSL and fully supervised learning; we assessed whether and under which conditions self-supervised pretraining led to an improvement over fully supervised baselines (ie, the fully supervised E4mer and pre-deep learning algorithms) in detecting acute MD episodes from recording segments taken in 64 (n=32, 50%, acute, n=32, 50%, stable) patients.
RESULTS: SSL significantly outperformed fully supervised pipelines using either our novel E4mer or extreme gradient boosting (XGBoost): n=3353 (81.23%) against n=3110 (75.35%; E4mer) and n=2973 (72.02%; XGBoost) correctly classified recording segments from a total of 4128 segments. SSL performance was strongly associated with the specific surrogate task used for pretraining, as well as with unlabeled data availability.
CONCLUSIONS: We showed that SSL, a paradigm where a model is pretrained on unlabeled data with no need for human annotations before deployment on the supervised target task of interest, helps overcome the annotation bottleneck; the choice of the pretraining surrogate task and the size of unlabeled data for pretraining are key determinants of SSL success. We introduced E4mer, which can be used for SSL, and shared the E4SelfLearning collection, along with its preprocessing pipeline, which can foster and expedite future research into SSL for personal sensing.