Prediction of stride duration by neural-network interpretation of surface EMG signals

Measuring stride duration as a marker of regular walking is a relevant issue, also in the modern gait analysis. The present project was designed to test the hypothesis that an artificial-neural-network approach is able to provide a reliable prediction of stride, stance, and swing duration, based on the analysis of only EMG signals acquired during able-bodied walking. To this objective, surface EMG signals from ten leg muscles of 23 adult subjects are used to train a multi-layer perceptron model. Performance of classifiers is tested vs. gold standard, represented by foot-floor-contact signals measured by means of three footswitches positioned under each foot. Outcomes indicate an accurate prediction of stride duration (mean absolute value, MAE ± SD = 18.1 ± 6.2 ms), stance duration (MAE ± SD = 29.2 ± 10.3 ms), and swing duration (MAE ± SD = 28.8 ± 9.6 ms), at least comparable to those reported in IMU-based studies. A significant contribution of this approach is that only sEMG signals (and no further data) during patient walking are needed to get the gait durations, after training the neural network. This contributes to reduce the costs of the test, the clinical time-wasting, and the invasiveness of instrumentation worn by the patient, making this approach very suitable especially for the clinical analysis of neuromuscular disorders where the evaluation of muscular recruitment is recommended.