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Digital Devices for Assessing Motor Functions in Mobility-Impaired and Healthy Populations: Systematic Literature Review.

Christine Cong GuoPatrizia Andrea ChiesaCarl de MoorMir Sohail FazeliThomas SchofieldKimberly HoferShibeshih BelachewAlf Scotland
Published in: Journal of medical Internet research (2022)
Sensor-derived motion data can be leveraged to classify and quantify disease status for a variety of neurological conditions. However, most of the recent research on digital clinical measures is derived from proof-of-concept studies with considerable variation in methodological approaches, and much of the reviewed literature has focused on clinical validation, with less than one-quarter of the studies performing analytical validation. Overall, future research is crucially needed to further consolidate that sensor-derived motion data may lead to the development of robust and transformative digital measurements intended to predict, diagnose, and quantify neurological disease state and its longitudinal change.
Keyphrases
  • electronic health record
  • systematic review
  • big data
  • case control
  • high speed
  • case report
  • liquid chromatography
  • deep learning
  • brain injury
  • subarachnoid hemorrhage
  • blood brain barrier
  • genetic diversity