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Computerized and mobile cognitive tasks have been used to assess psychomotor performance (e.g., slowed response time), but have the disadvantage of high participant burden. In contrast, typing (keystroke dynamics) in daily life provides a low-burden method to infer psychomotor functioning (e.g., fine motor coordination, alertness) that can support remote health monitoring. This project uses the innovative method of keystroke dynamics (e.g., typing speed) to continuously and remotely monitor, with low burden, psychomotor functioning as an indicator of patient health (e.g., cognitive health, medication on-off effects). Specifically, keystroke data can enable remote monitoring of patient health and be used to trigger just-in-time notifications to support pro-active care and intervention using a tool (i.e., computer keyboard) that individuals naturally use in daily life. Only a few companies (e.g. Neurametrix, Neurokeys) offer applications for keystroke data collection. In this project, we will develop artificial intelligence (AI) and machine learning (ML) based system to support identification and investigation of novel digital biomarkers based on keystroke dynamics to remotely monitor changes in psychomotor performance associated with health outcomes (e.g., Parkinson's and Alzheimer's Disease).
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