Research
Machine learning for handwriting and speech analysis
Functional handwriting involves complex interactions among physical, cognitive and sensory systems. Impairments in many aspects of these systems are associated with several diseases and disorders such as neurodegenerative diseases (Parkinson disease, Alzheimer disease) and pervasive developmental disorders. Our aim is to develop framework for decision support systems, that will allow for objective and more effective diagnosis and treatment.
Related publications:
Gazda, M., Hireš, M., and Drotár, P. (2021) . Multiple-Fine-Tuned Convolutional Neural Networks for Parkinson's Disease Diagnosis From Offline Handwriting," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, doi: 10.1109/TSMC.2020.3048892.
Drotár, P., Dobeš, M. (2020). Dysgraphia detection through machine learning. Scientific Reports, 10, 21541. doi:10.1038/s41598-020-78611-9 (OPEN ACCESS)
Dankovičová, Z., Sovák, D., Drotár, P., Vokorokos. L. (2018). Machine Learning Approach to Dysphonia Detection. Applied Sciences. 8, 1927, https://doi.org/10.3390/app8101927.
Drotár, P., Mekyska, J., Rektorová, I., Masarová, L., Smékal, Z., & Faundez-Zanuy, M. (2016). Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease. Artificial intelligence in medicine, 67, 39-46.
Drotár, P., Mekyska, J., Rektorová, I., Masarová, L., Smékal, Z., & Faundez-Zanuy, M. (2015). Decision support framework for parkinson’s disease based on novel handwriting markers. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(3), 508-516.
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