The neural population spiking activity recorded by intracortical brain-computer interfaces
(iBCIs) contain rich structure. Current models of such spiking activity are largely prepared for …

Denoising diffusion probabilistic models (DDPMs) have recently been shown to accurately
generate complicated data such as images, audio, or time series. Experimental and clinical …

Wearable and Internet of Things devices (IoT) are becoming ubiquitous in health
applications, such as movement disorders, rehabilitation, and activity monitoring. Wearable …

Technological advancements have substantially increased computational power and data
availability, enabling the application of powerful machine-learning (ML) techniques across …

The field of general time series analysis has recently begun to explore unified modeling,
where a common architectural backbone can be retrained on a specific task for a specific …

Wearable devices that have low-power sensors, processors, and communication
capabilities are gaining wide adoption in several health applications. The machine learning …

Human activity recognition (HAR) is an important component in a number of health
applications, including rehabilitation, Parkinson's disease, daily activity monitoring, and …

Wearable and Internet of Things (IoT) devices are becoming popular in several applications,
such as health monitoring, wide area sensing, and digital agriculture. These devices are …

Publicly available data sets of structural MRIs might not contain specific measurements of
brain Regions of Interests (ROIs) that are important for training machine learning models …

Wearable devices are being increasingly used in high-impact health applications including
vital sign monitoring, rehabilitation, and movement disorders. Wearable health monitoring …