Kang, S.J., Choi, J.H., Kim, Y.J., Ma, H.I. and Lee, U. (2015) Development of an Acquisition and Visualization of Forearm Tremors and Pronation/Supination Motor Activities in a Smartphone-Based Environment for an Early Diagnosis of Parkinson’s Disease. Advanced Science and Technology Letters, 116, 209-212.
https://doi.org/10.14257/astl.2015.116.42

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.