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
examine 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-train a 1-dimensional convolutional neural network
(CNN) using self-supervised learning (SSL). We evaluate our method using the
Wearable Stress and Affect prediction (WESAD) dataset. We fine-tune the
pre-trained networks to the stress prediction task and compare against
equivalent models without any self-supervised pre-training. We discover that
embeddings learned using our pre-training method outperform supervised
baselines with significantly fewer labeled data points: the models trained with
SSL require less than 30% of the labels to reach equivalent performance without
personalized SSL. This personalized learning method can enable precision health
systems which 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.

使用穿戴设备收集的生物信号数据进行压力预测，通过个性化的自监督学习方法，基于神经网络模型实现了较少标注数据即可达到等效结果，从而辅助移动感知技术在诸如压力等复杂、异质和主观主题上精准度健康评估系统。

压力移动传感的个性化使用自我监督学习

Personalization of Stress Mobile Sensing using Self-Supervised Learning

Transfer learning and meta-learning offer some of the most promising avenues
to unlock the scalability of healthcare and consumer technologies driven by
biosignal data. This is because current methods cannot generalise well across
human subjects' data and handle learning from different heterogeneously
collected data sets, thus limiting the scale of training data. On the other
side, developments in transfer learning would benefit significantly from a
real-world benchmark with immediate practical application. Therefore, we pick
electroencephalography (EEG) as an exemplar for what makes biosignal machine
learning hard. We design two transfer learning challenges around diagnostics
and Brain-Computer-Interfacing (BCI), that have to be solved in the face of low
signal-to-noise ratios, major variability among subjects, differences in the
data recording sessions and techniques, and even between the specific BCI tasks
recorded in the dataset. Task 1 is centred on the field of medical diagnostics,
addressing automatic sleep stage annotation across subjects. Task 2 is centred
on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across
both subjects and data sets. The BEETL competition with its over 30 competing
teams and its 3 winning entries brought attention to the potential of deep
transfer learning and combinations of set theory and conventional machine
learning techniques to overcome the challenges. The results set a new
state-of-the-art for the real-world BEETL benchmark.

该研究介绍了如何使用转移学习和元学习技术，来处理来自不同来源的生物信号数据，以解决医疗诊断和脑 - 机器接口任务的自动化问题。在这个研究里，以脑电图 (EEG) 为例，设计两项转移学习挑战任务，并通过竞赛验证了这些方法的效果。