Measurement of Stride Time by Machine Learning: Sensitivity Analysis for the Simplification of the Experimental Protocol

Limited stride-time variability is considered a marker of safe walking. Thus, the measurement of stride time is a meaningful information for gait analysis. The use of machine-learning (ML) techniques has been proven to be useful to this aim, even if the amount of data provided as input influences the computation process. This study is aiming to analyze the sensitivity of the experimental protocol (number of sensors and signals) on the performance of a stride-time measurement system based on ML interpretation of surface EMG signals (sEMG). To this purpose, sEMG signals from ten leg muscles of 30 volunteers are used to train a single-layer neural network. Five experimental protocols (from five to one sEMG sensors per leg) are comparatively tested. Results show that reducing the sEMG-protocol complexity (less sensors utilized) is decreasing the prediction performances. Based on the test results, this study proposes an experimental protocol composed of two sEMG sensors per leg [over gastrocnemius lateralis (GL) and tibialis anterior (TA)], as the best compromise between the need of a simplified experimental set-up and the necessity of high performances (F1-score ± SD = 99.0 ± 1.2%; mean absolute value, MAE ± SD = 17.9 ± 4.3 ms). The use of only two sEMG probes is going to have a great impact on gait analysis, improving patient comfort and reducing clinical costs and time consumption. A possible, further reduction of experimental protocol to a single muscle GL is feasible accepting a less efficient prediction of the stride-time.