Digital health technologies that quantify mobility in unsupervised, daily-living environments are emerging as a complementary evaluation approach in neurology. Data collected in these ecologically valid, patient-relevant settings can overcome significant limitations of conventional clinical assessments. Unsupervised assessments can capture fluctuating and rare events and have the promise of supporting clinical decision-making and serving as outcomes in clinical trials. However, studies that directly compared assessments made in unsupervised and supervised (i.e. in the lab or clinical) settings point to large disparities, even in the same parameters of mobility (up to 180% difference). These differences appear to be influenced by psychological, physiological, cognitive, environmental, and technical factors and by the specific aspect of mobility and diagnosis. To facilitate the successful adaptation of the unsupervised assessment of mobility in the clinic and in clinical trials, clinicians and future work should take into account these disparities and the multiple factors that contribute to them.

Lead-based perovskite solar cells (PSCs) have gained considerable interest since 2009 owing to their excellent optical and electrical properties, achieving a certified efficiency of 25.5% over a 12-year period. However,...

Sleep stage classification is essential for sleep assessment and disease diagnosis. Although previous attempts to classify sleep stages have achieved high classification performance, several challenges remain open: 1) How to effectively utilize time-varying spatial and temporal features from multi-channel brain signals remains challenging. Prior works have not been able to fully utilize the spatial topological information among brain regions. 2) Due to the many differences found in individual biological signals, how to overcome the differences of subjects and improve the generalization of deep neural networks is important.3) Most deep learning methods ignore the interpretability of the model to the brain. To address the above challenges, we propose a multi-view spatial-temporal graph convolutional networks (MSTGCN) with domain generalization for sleep stage classification. Specifically, we construct two brain view graphs for MSTGCN based on the functional connectivity and physical distance proximity of the brain regions. The MSTGCN consists of graph convolutions for extracting spatial features and temporal convolutions for capturing the transition rules among sleep stages. In addition, attention mechanism is employed for capturing the most relevant spatial-temporal information for sleep stage classification. Finally, domain generalization and MSTGCN are integrated into a unified framework to extract subjectinvariant sleep features. Experiments on two public datasets demonstrate that the proposed model outperforms the state-ofthe-art baselines.