Machine learning applied to gait analysis data in cerebral palsy and stroke: A systematic review.

Journal: Gait & posture
PMID: 38663321

Abstract

BACKGROUND: Among neurological pathologies, cerebral palsy and stroke are the main contributors to walking disorders. Machine learning methods have been proposed in the recent literature to analyze gait data from these patients. However, machine learning methods still fail to translate effectively into clinical applications. This systematic review addressed the gaps hindering the use of machine learning data analysis in the clinical assessment of cerebral palsy and stroke patients.

Authors

  • Farshad Samadi Kohnehshahri
    Department of Electronic and Information Engineering, University of Bologna, Italy; Gait and Motion Analysis Laboratory, Sol et Salus Hospital, Torre Pedrera, Rimini, Italy. Electronic address: Farshad.samadi2@unibo.it.
  • Andrea Merlo
    Gait and Motion Analysis Laboratory OPA Sol et Salus, Torre Pedrera, Rimini, Italy.
  • Davide Mazzoli
    Gait and Motion Analysis Laboratory OPA Sol et Salus, Torre Pedrera, Rimini, Italy.
  • Maria Chiara Bò
    Gait and Motion Analysis Laboratory, Sol et Salus Hospital, Torre Pedrera, Rimini, Italy; Merlo Bioengineering, Parma, Italy. Electronic address: chiarabo88@gmail.com.
  • Rita Stagni
    Department of Electronic and Information Engineering, University of Bologna, Italy. Electronic address: rita.stagni@unibo.it.

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