Single-Belt Versus Split-Belt: Intelligent Treadmill Control via Microphase Gait Capture for Poststroke Rehabilitation

Shengting Cao, Mansoo Ko, Chih Ying Li, David Brown, Xuefeng Wang, Fei Hu, Yu Gan

Research output: Contribution to journalArticlepeer-review

Abstract

Stroke is the leading long-term disability and causes a significant financial burden associated with rehabilitation. In poststroke rehabilitation, individuals with hemiparesis have a specialized demand for coordinated movement between the paretic and the nonparetic legs. The split-belt treadmill can effectively facilitate the paretic leg by slowing down the belt speed for that leg while the patient is walking on a split-belt treadmill. Although studies have found that split-belt treadmills can produce better gait recovery outcomes than traditional single-belt treadmills, the high cost of split-belt treadmills is a significant barrier to stroke rehabilitation in clinics. In this article, we design an AI-based system for the single-belt treadmill to make it act like a split-belt by adjusting the belt speed instantaneously according to the patient's microgait phases. This system only requires a low-cost RGB camera to capture human gait patterns. A novel microgait classification pipeline model is used to detect gait phases in real time. The pipeline is based on self-supervised learning that can calibrate the anchor video with the real-time video. We then use a ResNet-LSTM module to handle temporal information and increase accuracy. A real-time filtering algorithm is used to smoothen the treadmill control. We have tested the developed system with 34 healthy individuals and four stroke patients. The results show that our system is able to detect the gait microphase accurately and requires less human annotation in training, compared to the ResNet50 classifier. Our system 'Splicer' is boosted by AI modules and performs comparably as a split-belt system, in terms of timely varying left/right foot speed, creating a hemiparetic gait in healthy individuals, and promoting paretic side symmetry in force exertion for stroke patients. This innovative design can potentially provide cost-effective rehabilitation treatment for hemiparetic patients.

Original languageEnglish (US)
Pages (from-to)1006-1016
Number of pages11
JournalIEEE Transactions on Human-Machine Systems
Volume53
Issue number6
DOIs
StatePublished - Dec 1 2023

Keywords

  • Deep learning
  • inter- and intragait classification
  • rehabilitation
  • self-supervised learning
  • single-belt and split-belt treadmills

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing
  • Human Factors and Ergonomics
  • Human-Computer Interaction
  • Control and Systems Engineering
  • Computer Networks and Communications
  • Computer Science Applications

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