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Lopez-de-Ipina, K., Solé-Casals, J., Sánchez-Méndez, J.I., Romero-Garcia, R., Fernandez, E., Requejo, C., Poologaindran, A., Faúndez-Zanuy, M., Martí-Massó, J.F., Bergareche, A. and Suckling, J. (2021) Analysis of Fine Motor Skills in Essential Tremor: Combining Neuroimaging and Handwriting Biomarkers for Early Management. Frontiers in Human Neuroscience, 15, 235.
https://doi.org/10.3389/fnhum.2021.648573

has been cited by the following article:

• TITLE: Digital Biomarker Identification for Parkinson’s Disease Using a Game-Based Approach

  AUTHORS: Ilman Shazhaev, Dimitry Mihaylov, Abdulla Shafeeg

  KEYWORDS: Machine Learning, Biomarker, Parkinson’s Disease

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.14 No.4, November 8, 2022

  ABSTRACT: Despite the fact that their neurobiological processes and clinical criteria are well-established, early identification remains a significant hurdle to effective, disease-modifying therapy and prolonged life quality. Gaming on computers, gaming consoles, and mobile devices has become a popular pastime and provides valuable data from several sources. High-resolution data generated when users play commercial digital games includes information on play frequency as well as performance data that reflects low-level cognitive and motor processes. In this paper, we review some methods present in the literature that is used for identification of digital biomarkers for Parkinson’s disease. We also present a machine learning method for early identification of problematic digital biomarkers for Parkinson’s disease based on tapping activity from Farcana-Mini players. However, more data is required to reach a complete evaluation of this method. This data is being collected, with their consent, from players who play Farcana-Mini. Data analysis and a full assessment of this method will be presented in future work.