Online learning of gait models from older adult data

J. L.S. Waugh, E. Huang, J. E. Fraser, K. B. Beyer, A. Trinh, W. E. McIlroy, D. Kulić

Research output: Contribution to journalArticleResearchpeer-review

Abstract

This paper proposes a novel approach for online, individualized gait analysis, based on an adaptive periodic model of any gait signal. The proposed method learns a model of the gait cycle during online measurement, using a continuous representation that can adapt to inter-and intra-personal variability by creating an individualized model. Once the algorithm has converged to the input signal, key gait events can be identified based on the estimated gait phase and amplitude. The approach is implemented and tested on retirement home resident 6 min walk (6MW) data using wearable accelerometers at the ankle. The proposed approach converges within approximately four gait cycles and achieves 3% error in detecting initial swing events. 1 1 An early version of this work was presented in [1]. A more extensive description of related work and an extended method, including optimization of learning rates, were added to this paper. Further, this paper applies and evaluates the method to a new and much larger gait dataset taken from older adults who each have a variety of medical conditions. Therefore, the experimental protocol was also updated and the results are entirely novel.

Original languageEnglish
Article number8665976
Pages (from-to)733-742
Number of pages10
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume27
Issue number4
DOIs
Publication statusPublished - 1 Apr 2019
Externally publishedYes

Keywords

  • adaptive algorithms
  • adaptive estimation
  • adaptive signal processing
  • Event detection
  • modeling

Cite this

Waugh, J. L. S., Huang, E., Fraser, J. E., Beyer, K. B., Trinh, A., McIlroy, W. E., & Kulić, D. (2019). Online learning of gait models from older adult data. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(4), 733-742. [8665976]. https://doi.org/10.1109/TNSRE.2019.2904477