Multivideo Models for Classifying Hand Impairment After Stroke Using Egocentric Video.

Journal: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Published Date:

Abstract

OBJECTIVES: After stroke, hand function assessments are used as outcome measures to evaluate new rehabilitation therapies, but do not reflect true performance in natural environments. Wearable (egocentric) cameras provide a way to capture hand function information during activities of daily living (ADLs). However, while clinical assessments involve observing multiple functional tasks, existing deep learning methods developed to analyze hands in egocentric video are only capable of considering single ADLs. This study presents a novel multi-video architecture that processes multiple task videos to make improved estimations about hand impairment.

Authors

  • Anne Mei
  • Meng-Fen Tsai
  • Jose Zariffa

Keywords

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