Detection of cervical myelopathy with Leap Motion Sensor by random forests

Masaru Watanabe, Yuta Sugiura, Hideo Saito, Takafumi Koyama, Koji Fujita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a method for automatic detection of cervical myelopathy by using Leap Motion Sensor for sensing hand motion to apply a method called the 10-s grip and release test. Leap Motion data is analyzed for computing feature of hand motion, which is used for automatic detection of patient or healthy people by random forests algorithm for machine learning. For evaluating automatic detection performance, we collect real data of hand motion 54 of patient and healthy people, which is used for validating accuracy of detection.

Original languageEnglish
Title of host publicationLifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages214-216
Number of pages3
ISBN (Electronic)9781728170633
DOIs
Publication statusPublished - 2020 Mar
Externally publishedYes
Event2nd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2020 - Kyoto, Japan
Duration: 2020 Mar 102020 Mar 12

Publication series

NameLifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies

Conference

Conference2nd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2020
CountryJapan
CityKyoto
Period20/3/1020/3/12

Keywords

  • Detection algorithms
  • Machine learning
  • Motion Detection
  • Neurological disorders

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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