Abstract
The health-care domain is directly suitable for big data since the data sources involved in the health organizations are well known for their volume, heterogeneous complexity, and high dynamism. The role of big data analytics focused on techniques, platforms, and tools that are impacting on health institutions for implementing and delivering novel use-cases for potential health-care applications shows promising research directions to build smart health-care solutions in the well-being of people. In this context, Parkinson’s disease (PD) is a progressive neurodegenerative disorder that is characterized by motor symptoms. So, there are well-recognized problems in the diagnostics and treatment of the PD. Thus this chapter presents a methodology to monitor the symptoms related to PD, through the data collection by means of a mobile device, with the purpose of evaluating information obtained from the embedded sensor, using a set of automatic learning techniques, for its interpretation by a medical specialist.
| Original language | English |
|---|---|
| Title of host publication | Artificial Intelligence and Big Data Analytics for Smart Healthcare |
| Publisher | Elsevier |
| Pages | 97-119 |
| Number of pages | 23 |
| ISBN (Electronic) | 9780128220603 |
| ISBN (Print) | 9780128220627 |
| DOIs | |
| State | Published - 1 Jan 2021 |
Keywords
- Monitoring Parkinson’s disease
- Parkinson’s disease datasets
- Parkinson’s symptoms digital test
- UPDRS scale
- learning approaches
- manual dexterity
- mobile health-care application
- spatial memory test
- walking test