Machine Learning for Healthcare Wearable Devices: The Big Picture

DOI PDF XML 被引用文献1件
  • Farida Sabry
    Computer Science and Engineering Department, Faculty of Engineering, Qatar University, Doha, Qatar
  • Tamer Eltaras
    Computer Science and Engineering Department, Faculty of Engineering, Qatar University, Doha, Qatar
  • Wadha Labda
    Computer Science and Engineering Department, Faculty of Engineering, Qatar University, Doha, Qatar
  • Khawla Alzoubi
    Engineering Technology Department, Community College of Qatar, Doha, Qatar
  • Qutaibah Malluhi
    Computer Science and Engineering Department, Faculty of Engineering, Qatar University, Doha, Qatar
  • Yuxiang Wu
    editor

抄録

<jats:p>Using artificial intelligence and machine learning techniques in healthcare applications has been actively researched over the last few years. It holds promising opportunities as it is used to track human activities and vital signs using wearable devices and assist in diseases’ diagnosis, and it can play a great role in elderly care and patient’s health monitoring and diagnostics. With the great technological advances in medical sensors and miniaturization of electronic chips in the recent five years, more applications are being researched and developed for wearable devices. Despite the remarkable growth of using smart watches and other wearable devices, a few of these massive research efforts for machine learning applications have found their way to market. In this study, a review of the different areas of the recent machine learning research for healthcare wearable devices is presented. Different challenges facing machine learning applications on wearable devices are discussed. Potential solutions from the literature are presented, and areas open for improvement and further research are highlighted.</jats:p>

収録刊行物

被引用文献 (1)*注記

もっと見る

詳細情報 詳細情報について

問題の指摘

ページトップへ