This paper presents a L-dopa-Induced Dyskinesia Detection and Classification System based on Machine Learning Algorithms, wearable device (smartwatch) data and a smart-phone, connected via Bluetooth. This system was developed in three steps. The first step is the data collection, where each patient wears the smartwatch and performs some tasks while the smart-phone App captures data. These performed tasks are of different nature (i.e., writing, walking, sitting and cognitive task). In the second phase, some features were extracted from acceleration and angular velocity signals and a Z-score normalization is applied. In the last step two Machine Learning Algorithms, trained with these features as input, are used in order to detect and classify dyskinesias.
Upper Limbs Dyskinesia Detection and Classification for Patients with Parkinson's Disease based on Consumer Electronics Devices
Romeo L.
2018-01-01
Abstract
This paper presents a L-dopa-Induced Dyskinesia Detection and Classification System based on Machine Learning Algorithms, wearable device (smartwatch) data and a smart-phone, connected via Bluetooth. This system was developed in three steps. The first step is the data collection, where each patient wears the smartwatch and performs some tasks while the smart-phone App captures data. These performed tasks are of different nature (i.e., writing, walking, sitting and cognitive task). In the second phase, some features were extracted from acceleration and angular velocity signals and a Z-score normalization is applied. In the last step two Machine Learning Algorithms, trained with these features as input, are used in order to detect and classify dyskinesias.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
