This observational and experimental study seeks to establish a Smart Device System (SDS) to monitor high-resolution handtremor-based data using Smartphones, SmartWatches and Tablets. By doing this, movement data will be analyzed in depth with advanced statistical and Deep-Learning algorithms to identify new clinical phenotypical characteristics Parkinson's Disease and Essential Tremor.

Current smart devices as smartphones and smartwatches have reached a level of technical sophistication that enables high-resolution monitoring of movements not only for everyday sports activities but also for movement disorders. Tremor-related diseases as Parkinson's Disease (PD) and Essential Tremor (ET) are two of the most common movement disorders. Disease classification is primarily based on clinical criteria and remains challenging. The primary goal of this study is to identify new phenotypical characteristics based on the captured movement data by the tremor-capturing smartwatches and tablets and smartphone-based questionnaires.

The system will be applied and analyzed within an experimental and observational setting and only captures from patients, which have received informed consent. Within the study period, the SDS is not intended as clinical diagnostic support for physicians and will be not be used as medical device.

Inclusion Criteria:

• Diagnosed with Parkinson's Disease (ICD-10-GM G20.-) or Essential Tremor (G25.0)
• Comparison group: Other movement disorders including atypical Parkinsonian disorders and healthy participants

Exclusion Criteria:

• Unable to obtain informed consent
• Skin-related conditions at one of the wrists or any other medical conditions that could harm the participant's health by wearing the smartwatch at both wrists.

Participants who attend the ambulatory clinicic for movement disorders at the University Hospital Münster will be asked for inclusion.