Advances in Automation Technologies for Lower Extremity Neurorehabilitation: A Review and Future Challenges

Wenhao Deng, Ioannis Papavasileiou, Zhi Qiao, Wenlong Zhang, Kam Yiu Lam, Song Han

Research output: Contribution to journalReview articlepeer-review

28 Scopus citations

Abstract

The world is experiencing an unprecedented, enduring, and pervasive aging process. With more people who need walking assistance, the demand for lower extremity gait rehabilitation has increased rapidly over the years. The current clinical gait rehabilitative training requires heavy involvement of both medical doctors and physical therapists, and thus, are labor intensive, subjective, and expensive. To address these problems, advanced automation techniques, especially along with the proliferation of smart sensing and actuation devices and big data analytics platforms, have been introduced into this field to make the gait rehabilitation convenient, efficient, and personalized. This survey paper provides a comprehensive review on recent technological advances in wearable sensors, biofeedback devices, and assistive robots. Empowered by the emerging networking and computing technologies in the big data era, these devices are being interconnected into smart and connected rehabilitation systems to provide nonintrusive and continuous monitoring of physical and neurological conditions of the patients, perform complex gait analysis and diagnosis, and allow real-time decision making, biofeedback, and control of assistive robots. For each technology category, a detailed comparison among the existing solutions is provided. A thorough discussion is also presented on remaining open problems and future directions to further improve the safety, efficiency, and usability of the technologies.

Original languageEnglish (US)
Article number8354783
Pages (from-to)289-305
Number of pages17
JournalIEEE Reviews in Biomedical Engineering
Volume11
DOIs
StatePublished - May 3 2018

Keywords

  • Assistive robots
  • big data analytics platforms
  • biofeedback
  • disease diagnosis and analysis
  • gait quantification
  • lower extremity neurorehabilitation
  • wearable sensors

ASJC Scopus subject areas

  • Biomedical Engineering

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