As digital health technology becomes more pervasive, machine learning (ML) provides a
robust way to analyze and interpret the myriad of collected features. The purpose of this …

Type 2 diabetes (T2D) is a risk factor for cognitive decline. In neurodegenerative disease
research, remote digital cognitive assessments and unobtrusive sensors are gaining traction …

Brain aging leads to difficulties in functional independence. Mitigating these difficulties can
benefit from technology that predicts, monitors, and modifies brain aging. Translational …

Due to the growing capabilities of mobile phones and devices, mobile crowd sensing (MCS)
is rapidly gaining popularity among researchers in different fields, given its ability to collect …

This article focuses on the development of algorithms for a smart neurorehabilitation system,
whose core is made up of artificial neural networks. The authors of the article have proposed …

Background: Due to the COVID-19 pandemic, beneficial physical intervention classes for
individuals with Parkinson's disease (PD) were cancelled. Objective: To understand effects …

Mobile devices are becoming more pervasive in the monitoring of individuals' health as
device functionalities increase as does overall device prevalence in daily life. Therefore, it is …

Wearable devices are increasingly used in health monitoring due to the provision of
objective and longitudinal measures of physiological functions. The purpose of this work …

Background: Mobile devices and their capabilities (eg, device sensors and human-device
interactions) are increasingly being considered for use in clinical assessments and disease …

Digital health technology is becoming more ubiquitous in monitoring individuals' health as
both device functionality and overall prevalence increase. However, as individuals age …