Real-time data-driven gait phase detection using ground contact force measurements: Algorithms, platform design and performance

Ioannis Papavasileiou, Wenlong Zhang, Song Han

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

The world is experiencing an unprecedented, enduring, and pervasive aging process. With more people who need walking assistance, the demand for gait rehabilitation has increased rapidly over the years. Effective gait rehabilitation requires a comprehensive gait analysis, in which gait phase detection plays an important role. Although many specialized sensing systems have been developed for gait monitoring, most existing gait phase detection algorithms rely on significant input from medical professionals, which are subjective, manual and inaccurate. To address these problems, this paper presents a data-driven approach for real-time gait phase detection. The approach combines an infinite Gaussian mixture model (IGMM) to classify different gait phases based on the ground contact force (GCF) measurement, and a parallel particle filter to estimate and update the model parameters. Effective particle sharing mechanisms are further designed to distribute particles among different working nodes judiciously and thus strike a good balance between computational overhead and estimation accuracy. The proposed algorithm is implemented in our gait monitoring and analysis platform developed on a VMware cluster. Its estimation accuracy and computational efficiency are examined through extensive experiments using the data traces collected from both healthy human subjects and patients with neurological disorders.

Original languageEnglish (US)
Pages (from-to)34-49
Number of pages16
JournalSmart Health
Volume1-2
DOIs
StatePublished - Jun 2017

Keywords

  • Gait analysis
  • Gait phase detection
  • Infinite Gaussian mixture model (IGMM)
  • Parallel particle filter

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Information Systems
  • Health Informatics
  • Computer Science Applications
  • Health Information Management

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