Resources

Wearable technology has progressed significantly so that today's smartwatches are able to offer advanced health monitoring functions such as electrocardiogram (ECG). This study contains data that can be used to clinically assess the accuracy of four…

Parkinson’s disease (PD) is the second-most common neurodegenerative disorder, while incidence and worldwide burden are further increasing. In the era of digital health transformation, smart devices and mobile sensors, including smartphones and smar…

This dataset was collected as part of a study on investigating the sources of inaccuracy in wearable optical heart rate sensors across a variety of activities (sitting, deep breathing, walking, and typing). The original study examined covariates inc…

Sleep is an intrinsic part of human life, and recent advancements in wearable technology and machine learning have promised continuous and non-invasive methods of tracking sleep health and patterns, providing an important facet to a more holistic un…

This dataset supports the study "A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19," which developed an Intelligent Testing Allocation (ITA) method. The study demo…

Wearable cardiovascular monitoring aims to transition cardiovascular care from episodic clinical visits to continuous, free-living assessment. Many proposed systems use smartwatches and smart rings to capture varying combinations of photoplethysmogr…

Individuals who menstruate are frequently led to believe that there is a standard menstrual cycle, typically characterized as 28 days in length with predictable and uniform patterns. This framing often emphasizes cycle dates as the only relevant met…

The CogWear database consists of two datasets: pilot and survey gamification experiment. The pilot dataset includes physiological signals from 11 volunteers captured by three wearable devices (Empatica E4 watch, Samsung Galaxy Watch4 and Muse S EEG …