Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and
Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms …

There remains a significant mismatch between the complexity and variability of symptoms
and disabilities in Parkinson's disease (PD), and the capabilities of existing validated …

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 …

Parkinson's disease (PD) is associated with diverse clinical manifestations including motor
and non-motor signs and symptoms, and emerging biomarkers. We aimed to reveal the …

Background The clinical manifestations of Parkinson disease are characterized by
heterogeneity in age at onset, disease duration, rate of progression, and constellation of …

Objective: To better understand the longitudinal characteristics of Parkinson's disease (PD)
through the analysis of finger tapping and memory tests collected remotely using …

There is a critical need for new analytics to personalize behavioral data analysis across
different fields, including kinesiology, sports science, and behavioral neuroscience …

Parkinson's disease (PD) patient care is limited by inadequate, sporadic symptom
monitoring, infrequent access to care, and sparse encounters with healthcare professionals …

Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and
assessment of clinical signs, including the characterization of a variety of motor symptoms …

Recent years have witnessed a strongly increasing interest in digital technology within
medicine (sensor devices, specific smartphone apps) and specifically also neurology …