Multimodal Smartphone-Based System for Long-Term Monitoring of Patients with Parkinson's Disease

The paper presents a smartphone-based system for long-term self-monitoring patients with Parkinson's disease. Particularly promising multimodal functionality includes collecting data from different internal measurement units in two modes, evaluating the performance of specific tasks and special non-obtrusive passive sensing features based on day-basis activities. Information about physical activities and symptoms is processed and displayed in the form of a diary. The general system architecture and functional architecture are introduced. We outlined the main design principles and provisions on data completeness and engagement of participants during the first two study months. Some aspects of data analysis are also discussed. The classification accuracy with one data processing method for tremor data is 89,7%. Insofar as MeCo system uses different tremor components, the enhancement of the classification accuracy may be achieved by combination of criteria from different techniques. The combination of criteria provides up to 10% more accurate results in comparison with a single analysis.