title: Knowledge representation of motor activity of patients with Parkinson's disease.
abstract: An approach to the knowledge representation extraction from biomedical signals analysis concerning motor activity of Parkinson disease patients is proposed in this paper. This is done utilizing accelerometers attached to their body as well as exploiting video image of their hand movements. Experiments are carried out employing artificial neural networks and support vector machine to the recognition of characteristic motor activity disorders in patients. Obtained results indicate that it is possible to interpret some selected patient's body movements with a sufficiently high effectiveness.
authors.affiliation: Audio Acoustics Lab, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland.
authors.forename: Bożena
authors.initials: B
authors.lastname: Kostek
authors.affiliation: Multimedia Systems Department, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland.
authors.forename: Adam
authors.initials: A
authors.lastname: Kupryjanow
authors.affiliation: Multimedia Systems Department, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland.
authors.forename: Andrzej
authors.initials: A
authors.lastname: Czyżewski
keywords: ANN; Biomedical signal; Granular representation; Motor activity data processing; Parkinson’s disease; SVM