Monitoring of Mobility in People with Parkinson’s Disease through Multimodal Analysis (MoMoPa – AM)

The MoMoPa–AM project aims to establish and develop the scientific and technical foundations of a novel wearable sensor capable of diagnosing the onset of motor symptoms in Parkinson’s disease (PD) without relying exclusively on inertial sensors.

The project addresses one of the key challenges in Parkinson’s care, the accurate detection of motor symptoms during periods of low or no mobility. To achieve this, MoMoPa–AM proposes a multimodal approach, combining surface electromyography (sEMG), photoplethysmography (PPG), and electrodermal activity (EDA) techniques to obtain a more complete and robust understanding of patients’ motor states.

The proposed methodology involves the creation of a clinical database of 100 Parkinson’s patients, who will be monitored using non-inertial wearable sensors for a minimum of eight hours. The study will follow a clinical protocol approved by the corresponding Ethical Committees. Based on this dataset, an algorithmic block will be developed using supervised learning and Artificial Intelligence methods to detect and classify symptoms from labelled sensor signals.

This algorithmic core will become the foundation for a future wearable device capable of identifying motor symptoms in Parkinson’s patients even during phases of minimal movement. The innovation lies in establishing the scientific and technical groundwork for a new product with the first multimodal system designed specifically to detect and analyse PD symptoms in conditions of low activity.

Through this initiative, the project aims to develop the most comprehensive ambulatory monitoring solution for the motor symptoms of Parkinson’s disease currently available for therapeutic purposes. The expected outcome is a next-generation product that enables continuous and detailed monitoring, covering a market segment not addressed by current solutions, particularly patients in advanced disease stages who require precise treatment adjustments to maintain their quality of life.

Funding and Acknowledgement

This project is supported by the Centro para el Desarrollo Tecnológico y la Innovación (CDTI) through the Plan de Recuperación, Transformación y Resiliencia (PRTR) and co-funded by the European Union – NextGenerationEU.