Mobile Stride Length Estimation with Deep Convolutional Neural Networks

Julius Hannink, Thomas Kautz, Cristian F. Pasluosta, Jens Barth, Samuel Schulein, Karl Gunter Gabmann, Jochen Klucken, Bjoern M. Eskofier

Research output: Contribution to journalArticleResearchpeer-review

91 Citations (Scopus)

Abstract

Objective: Accurate estimation of spatial gait characteristics is critical to assess motor impairments resulting from neurological or musculoskeletal disease. Currently, however, methodological constraints limit clinical applicability of state-of-the-art double integration approaches to gait patterns with a clear zero-velocity phase. Methods: We describe a novel approach to stride length estimation that uses deep convolutional neural networks to map stride-specific inertial sensor data to the resulting stride length. The model is trained on a publicly available and clinically relevant benchmark dataset consisting of 1220 strides from 101 geriatric patients. Evaluation is done in a tenfold cross validation and for three different stride definitions. Results: Even though best results are achieved with strides defined frommidstance to midstance with average accuracy and precision of 0.01 5.37 cm, performance does not strongly depend on stride definition. The achieved precision outperforms state-of-the-art methods evaluated on the same benchmark dataset by 3.0 cm (36%). Conclusion: Due to the independence of stride definition, the proposed method is not subject to the methodological constrains that limit applicability of state-of-the-art double integration methods. Furthermore, it was possible to improve precision on the benchmark dataset. Significance: With more precise mobile stride length estimation, new insights to the progression of neurological disease or early indications might be gained. Due to the independence of stride definition, previously uncharted diseases in terms of mobile gait analysis can now be investigated by retraining and applying the proposed method.

Original languageEnglish
Pages (from-to)354-362
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Volume22
Issue number2
DOIs
Publication statusPublished - Mar 2018
Externally publishedYes

Keywords

  • Deep learning
  • convolutional neural networks
  • mobile gait analysis
  • regression
  • stride length

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