Machine-learning approaches are satisfactorily implemented for classifying and assessing gait events from only surface electromyographic (sEMG) signals during walking. However, it is acknowledged that the choice of sEMG-processing type may affect the reliability of methodologies based on it. Analogously, the number of sEMG signals involved in machine-learning procedure could influence the classification process. Aim of this study is to quantify the impact of different EMGsignal- processing specifications and/or different complexity of the experimental sEMG-protocol (different number of sEMG-sensors) on the performance of a neural-network-based approach for binary classifying gait phases and predicting gait-event timing. To this purpose, sEMG signals are collected from eight leg-muscles in about 10.000 strides from 23 healthy adults during walking and then fed to a multi-layer perceptron model. Four different signal-processing approaches are tested and five experimental set-ups (from four to one sEMG sensors per leg) are compared. Results indicate that both the choice of sEMG processing and the reduction of sEMG-protocol complexity actually affect classification/prediction performances. Moreover, the study succeeds in the double goal of identifying the linear envelope as the sEMG-processing type which reaches the best neural-network performance (classification accuracy of 93.4 ± 2.3 %; mean absolute error 21.6 ± 7.0 and 38.1 ± 15.2 ms for heel-strike/toe-off prediction, respectively) and providing a quantification of the progressive deterioration of classification/prediction performances with the reduction of the number of sensors used (from 93.4 ± 2.3%–79.9 ± 6.1 % for classification accuracy). These findings could be very useful for clinics to the aim of choosing the most suitable approach balancing technical performances, patient comfort, and clinical needs.

Influence of EMG-signal processing and experimental set-up on prediction of gait events by neural network / Di Nardo, F.; Morbidoni, C.; Cucchiarelli, A.; Fioretti, S.. - In: BIOMEDICAL SIGNAL PROCESSING AND CONTROL. - ISSN 1746-8094. - 63:(2021), p. 102232. [10.1016/j.bspc.2020.102232]

Influence of EMG-signal processing and experimental set-up on prediction of gait events by neural network

Di Nardo F.;Morbidoni C.;Cucchiarelli A.;Fioretti S.
2021-01-01

Abstract

Machine-learning approaches are satisfactorily implemented for classifying and assessing gait events from only surface electromyographic (sEMG) signals during walking. However, it is acknowledged that the choice of sEMG-processing type may affect the reliability of methodologies based on it. Analogously, the number of sEMG signals involved in machine-learning procedure could influence the classification process. Aim of this study is to quantify the impact of different EMGsignal- processing specifications and/or different complexity of the experimental sEMG-protocol (different number of sEMG-sensors) on the performance of a neural-network-based approach for binary classifying gait phases and predicting gait-event timing. To this purpose, sEMG signals are collected from eight leg-muscles in about 10.000 strides from 23 healthy adults during walking and then fed to a multi-layer perceptron model. Four different signal-processing approaches are tested and five experimental set-ups (from four to one sEMG sensors per leg) are compared. Results indicate that both the choice of sEMG processing and the reduction of sEMG-protocol complexity actually affect classification/prediction performances. Moreover, the study succeeds in the double goal of identifying the linear envelope as the sEMG-processing type which reaches the best neural-network performance (classification accuracy of 93.4 ± 2.3 %; mean absolute error 21.6 ± 7.0 and 38.1 ± 15.2 ms for heel-strike/toe-off prediction, respectively) and providing a quantification of the progressive deterioration of classification/prediction performances with the reduction of the number of sensors used (from 93.4 ± 2.3%–79.9 ± 6.1 % for classification accuracy). These findings could be very useful for clinics to the aim of choosing the most suitable approach balancing technical performances, patient comfort, and clinical needs.
2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/291070
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