In this paper a L-dopa-Induced Dyskinesia Detection System based on Machine Learning Algorithms (MLAs), smartwatch data and a smartphone is presented. The development of this system was performed in three steps. In the first step each patient wears the smartwatch and fulfills some tasks while the smartphone Application captures data. The second phase is the features extraction from acceleration and angular velocity signals and the application of a Z-score normalization. In the last step two MLAs, trained with these features as input, are implemented in order to detect dyskinesias. © 2018 IEEE.
Upper and Lower Limbs Dyskinesia Detection for Patients with Parkinson's Disease / Belgiovine, G.; Capecci, M.; Ciabattoni, L.; Fiorentino, MARIA CHIARA; Foresi, G.; Monteriu, A.; Pepa, L.. - ELETTRONICO. - (2018), pp. 206-209. (Intervento presentato al convegno 7th IEEE Global Conference on Consumer Electronics, GCCE 2018 tenutosi a Nara Royal Hotel, jpn nel 2018) [10.1109/GCCE.2018.8574646].
Upper and Lower Limbs Dyskinesia Detection for Patients with Parkinson's Disease
Capecci, M.;Ciabattoni, L.;FIORENTINO, MARIA CHIARA;Foresi, G.;Monteriu, A.;Pepa, L.
2018-01-01
Abstract
In this paper a L-dopa-Induced Dyskinesia Detection System based on Machine Learning Algorithms (MLAs), smartwatch data and a smartphone is presented. The development of this system was performed in three steps. In the first step each patient wears the smartwatch and fulfills some tasks while the smartphone Application captures data. The second phase is the features extraction from acceleration and angular velocity signals and the application of a Z-score normalization. In the last step two MLAs, trained with these features as input, are implemented in order to detect dyskinesias. © 2018 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
