BUSCADOR RECOLECTA

Archivo Digital UPM

oai:oa.upm.es:80571

In the development of deep learning systems aimed at detecting Parkinson's Disease (PD) using inertial sensors, some aspects could be essential to refine tremor detection methodologies in realistic scenarios. This work analyses the effect of the subjects’ posture during tremor recordings and the required amount of data to assess a proper PD detection in a Leave-One-Subject-Out Cross-Validation (LOSO CV) scenario. We propose a deep learning architecture that learns a PD biomarker from accelerometer signals to classify subjects between healthy and PD patients. This study uses the PD-BioStampRC21 dataset, containing accelerometer recordings from healthy and PD participants equipped with five inertial sensors. An increment of performance was obtained when using sitting windows compared to using lying windows for Fast Fourier Transform (FFT) input signal domain. Moreover, using 5 minutes per subject could be sufficient to properly evaluate the PD status of a patient without losing performance, reaching a windowlevel accuracy of 77.71 ± 1.07 % and a user-level accuracy of 87.10 ± 11.80 %. Furthermore, a knowledge transfer could be performed when training the system with sitting instances and testing with lying examples, indicating that the sitting activity contains valuable information that allows an effective generalization to lying instances.