A system for the management of the automatic assessment of Parkinson’s Disease (PD) at-home is presented. The system is based on a non-contact and natural human computer interface which is suitable for motor impaired users, as are PD patients. The interface, built around optical RGB-Depth devices, allows for both gesture-based interaction with the system and tracking of hands and body movements during the performance of standard upper and lower limb tasks, as specified by the Unified Parkinson’s Disease Rating Scale (UPDRS). The accurate tracking and characterization of the movements allows for an automatic and objective assessment of the UPDRS tasks, making feasible the monitoring of motor fluctuations at-home and on daily basis, which are important features in the management of the disease progression. The assessment of the different tasks is performed by machine learning techniques. Selected kinematic parameters characterizing the movements are input to trained classifiers to rate the motor performance. Results on monitoring experiments at-home and on the system accuracy as compared to clinical evaluations are presented and discussed.

Assessment of parkinson’s disease at-home using a natural interface based system

Ferraris C.;Azzaro C.;Priano L.;Mauro A.
2018-01-01

Abstract

A system for the management of the automatic assessment of Parkinson’s Disease (PD) at-home is presented. The system is based on a non-contact and natural human computer interface which is suitable for motor impaired users, as are PD patients. The interface, built around optical RGB-Depth devices, allows for both gesture-based interaction with the system and tracking of hands and body movements during the performance of standard upper and lower limb tasks, as specified by the Unified Parkinson’s Disease Rating Scale (UPDRS). The accurate tracking and characterization of the movements allows for an automatic and objective assessment of the UPDRS tasks, making feasible the monitoring of motor fluctuations at-home and on daily basis, which are important features in the management of the disease progression. The assessment of the different tasks is performed by machine learning techniques. Selected kinematic parameters characterizing the movements are input to trained classifiers to rate the motor performance. Results on monitoring experiments at-home and on the system accuracy as compared to clinical evaluations are presented and discussed.
2018
Ambient Assisted Living
Springer Verlag
Lecture Notes in Electrical Engineering
544
417
427
978-3-030-05920-0
978-3-030-05921-7
http://www.springer.com/series/7818
At-home monitoring; Automated assessment; Body tracking; Hand tracking; Movement disorders; Natural human computer interface; Parkinson’s disease; RGB-Depth; UPDRS
Ferraris C.; Nerino R.; Chimienti A.; Pettiti G.; Azzaro C.; Albani G.; Priano L.; Mauro A.
File in questo prodotto:
File Dimensione Formato  
2019__Assessment of Parkinson’s Disease At-Home_Copia autore_Springer_finale.pdf

Accesso riservato

Tipo di file: POSTPRINT (VERSIONE FINALE DELL’AUTORE)
Dimensione 773.96 kB
Formato Adobe PDF
773.96 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1721758
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact