Intra-subject approach for gait-event prediction by neural network interpretation of EMG signals

Biomed Eng Online. 2020 Jul 28;19(1):58. doi: 10.1186/s12938-020-00803-1.

Abstract

Background: Machine learning models were satisfactorily implemented for estimating gait events from surface electromyographic (sEMG) signals during walking. Most of them are based on inter-subject approaches for data preparation. Aim of the study is to propose an intra-subject approach for binary classifying gait phases and predicting gait events based on neural network interpretation of sEMG signals and to test the hypothesis that the intra-subject approach is able to achieve better performances compared to an inter-subject one. To this aim, sEMG signals were acquired from 10 leg muscles in about 10.000 strides from 23 healthy adults, during ground walking, and a multi-layer perceptron (MLP) architecture was implemented.

Results: Classification/prediction accuracy was tested vs. the ground truth, represented by the foot-floor-contact signal provided by three foot-switches, through samples not used during training phase. Average classification accuracy of 96.1 ± 1.9% and mean absolute value (MAE) of 14.4 ± 4.7 ms and 23.7 ± 11.3 ms in predicting heel-strike (HS) and toe-off (TO) timing were provided. Performances of the proposed approach were tested by a direct comparison with performances provided by the inter-subject approach in the same population. Comparison results showed 1.4% improvement of mean classification accuracy and a significant (p < 0.05) decrease of MAE in predicting HS and TO timing (23% and 33% reduction, respectively).

Conclusions: The study developed an accurate methodology for classification and prediction of gait events, based on neural network interpretation of intra-subject sEMG data, able to outperform more typical inter-subject approaches. The clinically useful contribution consists in predicting gait events from only EMG signals from a single subject, contributing to remove the need of further sensors for the direct measurement of temporal data.

Keywords: Gait-phase classification; Ground walking; Intra-subject data; Machine learning; Neural networks; Surface EMG.

MeSH terms

  • Adult
  • Electromyography*
  • Female
  • Gait Analysis*
  • Humans
  • Male
  • Neural Networks, Computer*
  • Signal Processing, Computer-Assisted*