This paper is in the following e-collection/theme issue:

Research Instruments, Questionnaires, and Tools (591) Artificial Intelligence (1430) Telehealth and Telemonitoring (1717) Machine Learning (1505) Wearable Devices and Sensors (905) Stroke (166)

Published on in Vol 22, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20641, first published .
Automatic Grading of Stroke Symptoms for Rapid Assessment Using Optimized Machine Learning and 4-Limb Kinematics: Clinical Validation Study

Automatic Grading of Stroke Symptoms for Rapid Assessment Using Optimized Machine Learning and 4-Limb Kinematics: Clinical Validation Study

Automatic Grading of Stroke Symptoms for Rapid Assessment Using Optimized Machine Learning and 4-Limb Kinematics: Clinical Validation Study

Authors of this article:

Eunjeong Park1 Author Orcid Image ;   Kijeong Lee2 Author Orcid Image ;   Taehwa Han3 Author Orcid Image ;   Hyo Suk Nam4 Author Orcid Image
Article Authors Cited by (27) Tweetations (4) Metrics

Eunjeong Park   1 , PhD ;   Kijeong Lee   2 , MD ;   Taehwa Han   3 , PhD ;   Hyo Suk Nam   4 , MD, PhD

1 Cerebro-Cardiovascular Disease Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea

2 Department of Radiology, Yonsei University College of Medicine, Seoul, Republic of Korea

3 Health-IT Center, Yonsei University College of Medicine, Seoul, Republic of Korea

4 Department of Neurology, Yonsei University College of Medicine, Seoul, Republic of Korea

Corresponding Author:

  • Hyo Suk Nam, MD, PhD
  • Department of Neurology
  • Yonsei University College of Medicine
  • 50 Yonsei-ro, Seodaemoon-gu
  • Seoul, 03722
  • Republic of Korea
  • Phone: 82 222280245
  • Email: hsnam@yuhs.ac