Browsing by Subject "Feature extraction"

Now showing items 1-4 of 4

    • Comparison of CNN-learned vs. handcrafted features for detection of Parkinson's disease dysgraphia in a multilingual dataset 

      Galaz, Zoltan; Drotar, Peter; Mekyska, Jiri; Gazda, Matej; Mucha, Jan; Zvoncak, Vojtech; Smekal, Zdenek; Faundez-Zanuy, Marcos; Castrillon, Reinel; Orozco-Arroyave, Juan Rafael; Rapcsak, Steven; Kincses, Tamas; Brabenec, Lubos; Rektorova, Irena (Frontiers in Neuroinformatics. 2022;(16):877139, 2022)
      Parkinson’s disease dysgraphia (PDYS), one of the earliest signs of Parkinson’s disease (PD), has been researched as a promising biomarker of PD and as the target of a noninvasive and inexpensive ...

    • Emotional state recognition performance improvement on a handwriting and drawing task 

      Nolazco Flores, Juan Arturo; Faundez-Zanuy, Marcos; Velázquez-Flores, O. A.; Cordasco, Gennaro; Esposito, Anna (IEEE Access. 2021 Feb 23;(9):28496-28504, 2021-02-21)
      In this work we combine time, spectral and cepstral features of the signal captured in a tablet to characterize depression, anxiety, and stress emotional state recognition on the EMOTHAW database. EMOTHAW ...

    • Exploiting spectral and cepstral handwriting features on diagnosing Parkinson’s disease 

      Nolazco Flores, Juan Arturo; Faundez-Zanuy, Marcos; de la Cueva, Victor; Mekyska, Jiri (IEEE Access. 2021 Oct 22;(9):141599-141610, 2021-10-22)
      Parkinson’s disease (PD) is the second most frequent neurodegenerative disease associated with several motor symptoms, including alterations in handwriting, also known as PD dysgraphia. Several computerized ...

    • Mood state detection in handwritten tasks using PCA–mFCBF and automated machine learning 

      Nolazco Flores, Juan Arturo; Faundez-Zanuy, Marcos; Velázquez-Flores, O. A.; Del-Valle-Soto, Carolina; Cordasco, Gennaro; Esposito, Anna (Sensors. 2022 Feb 21;22(4):1686, 2022 Feb 2)
      In this research, we analyse data obtained from sensors when a user handwrites or draws on a tablet to detect whether the user is in a specific mood state. First, we calculated the features based on the ...