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

DOI: 10.4236/jilsa.2022.144007, PP. 89-95

Keywords: Machine Learning, Biomarker, Parkinson’s Disease

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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.

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