Contact person:
Dario Salvi
  • Mats Paulsson Stiftelserna
Responsible at MaU:
Dario Salvi
Project members:
Collaborators and other project members:
  • University of Edinburgh
  • Karolinska Institutet
Time frame:
01 January 2021 - 30 June 2022
Research environment :
Research subject:

About the project

Parkinson's disease (PD) is a chronic neurodegenerative disease characterised by motor symptoms such as slowness of movement and non-motor symptoms like depression and cognitive alterations. PD is a common neurodegenerative disease and its worldwide prevalence is estimated to be 9 million cases by 2030. The diagnosis and assessment of PD are mainly based on clinical criteria such as the Unified Parkinson Disease Rating Scale (UPDRS), but these are subjective, infrequent and not always reliable.

Detect symptoms with smartphones

The use of smartphones is being experimented by researchers as a way to complement clinical assessment with low-cost technologies and at patients homes. This is very relevant in times of pandemics when healthcare systems are under stress and vulnerable patients are discouraged from attending clinics. The sensors embedded in smartphones offer opportunities to detect PD symptoms objectively and reliably. While most existing research has focussed on a single measurement modality, like assessing movement through accelerometry or voice changes with microphones, little research has been done to integrate these different modalities.

We aim to develop a digital health tool for continuous multi-dimensional remote assessment of PD patients. We will use conventional smartphones and smartwatches to gather data from patients and we will develop algorithms to translate that data into clinically useful indicators, which will be made available to clinicians through a web site. The research will be led by the University of Malmö and will involve the University of Edinburgh with their expertise in developing algorithms for PD, and the Karolinska Institute, which will provide clinical expertise and will recruit patients. 

The project will be split into three parts:

  • the first will focus on developing algorithms and testing them with available datasets,
  • in the second part, we will further develop the algorithms using data gathered from a small cohort of patients
  • and in the third part, we will involve 30 patients in a clinical evaluation of the system.

Further studies

We expect our technology to be usable and highly accepted by both patients and clinicians. Further studies will be needed after this, to improve the technology and make it more robust for scaling it up to larger cohorts. This project will allow future research collaborations among the three partners and may lead to a system for PD home monitoring used in the clinical practice in Sweden.