Sleep stage classification is critical for diagnosing sleep quality. While deep neural networks are becoming popular for automatic sleep stage classification with supervised learning, large-scale labeled datasets are still hard to acquire.

The purpose of this paper is to learn efficient representations from raw electroencephalogram (EEG) signals for sleep stage classification via self-supervised learning (SSL). Although supervised methods have gained favorable performance, they heavily rely on manually labeled datasets.

We address the problem of unsupervised anomaly detection for multivariate data. Traditional machine learning based anomaly detection algorithms rely on specific assumptions of normal patterns and fail to model complex feature interactions and relations. Recently, existing deep learning based methods are promising for extracting representations from complex features. These methods train an auxiliary task, e.g., reconstruction and prediction, on normal samples.

Detecting anomalies accurately in time series data has been receiving considerable attention due to its enormous potential for a wide array of applications. Numerous unsupervised anomaly detection methods for time series have been developed because of the difficulty of obtaining accurate labels.

It is important to understand how the hand and fingers elongate and contract with hand posture for optimally fitting and comfortable gloves.

Accurate evaluations of the interface pressure between the bra and skin are critically important prerequisites for the ergonomic design of bra components. Nevertheless, previous analyses on contact pressure have mainly relied on human wear trials with low data repeatability.