Abstract
Machine-learning techniques are suitably employed for gait-event prediction from only surface electromyographic (sEMG) signals in control subjects during walking. Nevertheless, a reference approach is not available in cerebral-palsy hemiplegic children, likely due to the large variability of foot-floor contacts. This study is designed to investigate a machine-learning-based approach, specifically developed to binary classify gait events and to predict heel-strike (HS) and toe-off (TO) timing from sEMG signals in hemiplegic-child walking. To this objective, sEMG signals are acquired from five hemiplegic-leg muscles in nearly 2500 strides from 20 hemiplegic children, acknowledged as Winters' group 1 and 2. sEMG signals, segmented in overlapping windows of 600 samples (pace = 5 samples), are used to train a multi-layer perceptron model. Intra-subject and inter-subject experimental settings are tested. The best-performing intra-subject approach is able to provide in the hemiplegic population a mean classification accuracy (±SD) of 0.97±0.01 and a suitable prediction of HS and TO events, in terms of average mean absolute error (MAE, 14.8±3.2 ms for HS and 17.6±4.2 ms for TO) and F1-score (0.95±0.03 for HS and 0.92±0.07 for TO). These results outperform previous sEMG-based attempts in cerebral-palsy populations and are comparable with outcomes achieved by reference approaches in control populations. In conclusion, the findings of the study prove the feasibility of neural networks in predicting the two main gait events using surface EMG signals, also in condition of high variability of the signal to predict as in hemiplegic cerebral palsy.
Highlights
C EREBRAL palsy is the most common motor disability in childhood [1]
Percentage of strides characterized by the different foot-floor-contact sequences is reported in Table I for each hemiplegic child, together with the total number of strides measured in the hemiplegic limb
Average performances in identifying HS and TO timing in testing set are expressed in Table II by mean absolute error (MAE), time delay (TD), precision, recall, and F1-score
Summary
C EREBRAL palsy is the most common motor disability in childhood [1]. Pediatric hemiplegia is a form of unilateralManuscript received November 24, 2020; revised March 11, 2021; accepted April 13, 2021. It may cause altered selective motor control, weakness, stiffness of the limbs, and consequent balance and walking difficulties [2]. Clinical gait analysis (CGA) is the main tool to supply different indexes and parameters, suitable to quantitatively characterize human locomotion and to stress possible impairments of motor function. CGA is able to provide four types of different data: spatial-temporal parameters, kinematics data, kinetics data, and electromyographic (EMG) signals. Many recent CGA studies focused on the acquisition of surface electromyographic (sEMG) signals in cerebral-palsy children [3]–[8], probably due to the increasing availability of solutions based on it. The assessment of muscular recruitment by means myoelectric-signal analysis is, strongly advised in hemiplegic cerebral palsy, due to the neuromuscular involvement of this disorder [6]
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