Hidden Markov Model based stride segmentation on unsupervised free-living gait data in Parkinson’s disease patients

Nils Roth*, Arne Küderle, Martin Ullrich, Till Gladow, Franz Marxreiter, Jochen Klucken, Bjoern M. Eskofier, Felix Kluge

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

15 Citations (Scopus)

Abstract

Background: To objectively assess a patient’s gait, a robust identification of stride borders is one of the first steps in inertial sensor-based mobile gait analysis pipelines. While many different methods for stride segmentation have been presented in the literature, an out-of-lab evaluation of respective algorithms on free-living gait is still missing. Method: To address this issue, we present a comprehensive free-living evaluation dataset, including 146.574 semi-automatic labeled strides of 28 Parkinson’s Disease patients. This dataset was used to evaluate the segmentation performance of a new Hidden Markov Model (HMM) based stride segmentation approach compared to an available dynamic time warping (DTW) based method. Results: The proposed HMM achieved a mean F1-score of 92.1% and outperformed the DTW approach significantly. Further analysis revealed a dependency of segmentation performance to the number of strides within respective walking bouts. Shorter bouts (< 30 strides) resulted in worse performance, which could be related to more heterogeneous gait and an increased diversity of different stride types in short free-living walking bouts. In contrast, the HMM reached F1-scores of more than 96.2% for longer bouts (> 50 strides). Furthermore, we showed that an HMM, which was trained on at-lab data only, could be transferred to a free-living context with a negligible decrease in performance. Conclusion: The generalizability of the proposed HMM is a promising feature, as fully labeled free-living training data might not be available for many applications. To the best of our knowledge, this is the first evaluation of stride segmentation performance on a large scale free-living dataset. Our proposed HMM-based approach was able to address the increased complexity of free-living gait data, and thus will help to enable a robust assessment of stride parameters in future free-living gait analysis applications.

Original languageEnglish
Article number93
Pages (from-to)93
JournalJournal of NeuroEngineering and Rehabilitation
Volume18
Issue number1
DOIs
Publication statusPublished - 3 Jun 2021
Externally publishedYes

Keywords

  • HMM
  • IMU
  • Machine learning
  • Mobile gait analysis
  • Stride borders
  • Wearable sensors

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