An oscillator-based smooth real-time estimate of gait phase for wearable robotics

Tingfang Yan, Andrea Parri, Virginia Ruiz Garate, Marco Cempini, Renaud Ronsse, Nicola Vitiello

Research output: Contribution to journalArticlepeer-review


This paper presents a novel methodology for estimating the gait phase of human walking through a simple sensory apparatus. Three subsystems are combined: a primary phase estimator based on adaptive oscillators, a desired gait event detector and a phase error compensator. The estimated gait phase is expected to linearly increase from 0 to 2(Formula presented.) rad in one stride and remain continuous also when transiting to the next stride. We designed two experimental scenarios to validate this gait phase estimator, namely treadmill walking at different speeds and free walking. In the case of treadmill walking, the maximum phase error at the desired gait events was found to be 0.155 rad, and the maximum phase difference between the end of the previous stride and beginning of the current stride was 0.020 rad. In the free walking trials, phase error at the desired gait event was never larger than 0.278 rad. Our algorithm outperformed against two other benchmarked methods. The good performance of our gait phase estimator could provide consistent and finely tuned assistance for an exoskeleton designed to augment the mobility of patients.

Original languageEnglish
Pages (from-to)1-16
Number of pages16
JournalAutonomous Robots
Publication statusE-pub ahead of print - May 4 2016


  • Adaptive oscillators
  • Phase error learning
  • Real-time gait phase estimate
  • Wearable robotics

ASJC Scopus subject areas

  • Artificial Intelligence


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