Hallux valgus orthoses are available in a wide range of designs and materials, but the effects of their design on functional performance have not been fully investigated.

The treatment effect of orthoses for hallux valgus (HV) is unclear with little interventional studies, the design involves multiple complex factors, and therefore a systematic analysis with meta-analysis is necessary. The objective of this systematic review and meta-analysis is to determine whether current foot orthoses are effective in treating HV.

In this article, the impact of postural variations on hand anthropometry and distribution of skin strain ratios has been investigated. The literature suggests the glove fit directly affects hand functions.

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.