Gait Phase Classification from Surface EMG Signals Using Neural Networks

Morbidoni, Christian; Principi, Lorenzo; Mascia, Guido; Strazza, Annachiara; Verdini, Federica; Cucchiarelli, Alessandro; Nardo, Francesco Di

doi:10.1007/978-3-030-31635-8_9

Cited by 10 publications

(16 citation statements)

References 13 publications

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“…Since the aim of this work is using ML to build an estimation model which can be adjusted to suit the subject himself, the training and testing data are from the same subject for each validation, and a similar method can be found in [30]. The results of the subjects in this work using RFPCA was acceptable.…”

Section: Discussionmentioning

confidence: 98%

Estimation of Knee Movement from Surface EMG Using Random Forest with Principal Component Analysis

Guan

Zou

et al. 2019

Electronics

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To study the relationship between surface electromyography (sEMG) and joint movement, and to provide reliable reference information for the exoskeleton control, the sEMG and the corresponding movement of the knee during the normal walking of adults have been measured. After processing the experimental data, the estimation model for knee movement from sEMG was established using the novel method of random forest with principal component analysis (RFPCA). The influence of the sample size and the previous sEMG data on the prediction efficiency was analyzed. The estimation model was not sensitive to the sample size when samples increased to a certain value, and the results of different previous sEMG showed that the prediction accuracy of the estimation models did not always improve with the increasing features of input. By comparing the estimation model of back propagation neural network with principal component analysis (BPPCA), it was found that RFPCA was suitable for all participants in the experiment with less execution time, and the root mean square error was around 5 • which was lower than BPPCA with errors varying from 7 • to 25 • . Therefore, it was concluded that the RFPCA method for the estimation of knee movement from sEMG is feasible and could be used for motion analysis and the control of exoskeleton.

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Section: Discussionmentioning

confidence: 98%

Estimation of Knee Movement from Surface EMG Using Random Forest with Principal Component Analysis

Guan

Zou

et al. 2019

Electronics

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“…Not so many efforts are available in literature, providing classification of gait phases from only sEMG signals [13,14,21,[23][24][25]. Most of these studies aim only at classifying gait phases, not providing estimation of gait events (HS and TO).…”

Section: Related Workmentioning

confidence: 99%

“…The best-case accuracy was 91.1%. The present group of researchers was able to achieve a mean binary-classification accuracy of 95.2%, adopting a multi-layer perceptron (MLP) classifier to interpret EMG data [25]. To this aim, an intra-subject approach was used on twelve healthy volunteers.…”

Section: Related Workmentioning

confidence: 99%

“…From this point of view, an interesting attempt has been performed by Liu et al [10], who proposed a technique for the recognition of gait phases using only joint angular sensors of the exoskeleton robot, containing the position, velocity, acceleration, and further motion data; very promising results were achieved. In the same way, different studies attempted to provide a reliable classification of gait phases and an accurate prediction of heel strike (HS) and toe off (TO) from only surface electromyographic (sEMG) sensors [13,14,21,[23][24][25]. Details about methodology and outcomes of these studies are reported in Section 2 (related works).…”

Section: Introductionmentioning

confidence: 99%

“…Following the line taken by the above-mentioned studies, the present work was designed to propose a novel approach for the binary classification of gait phases and the prediction of gait events, based only on deep-learning analysis of sagittal knee-joint angle measured by a single electrogoniometer. Although promising classification performances were achieved by sEMG-based methods [13,14,21,[23][24][25], gold-standard approaches are not available in literature. Thus, in order to evaluate the robustness of the proposed approach, a direct comparison in the same population was also performed with the sEMG-based experiment [14], which achieves the best performance in HS and TO prediction among the approaches reported in literature (see Section 2).…”

Section: Introductionmentioning

confidence: 99%

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Recognition of Gait Phases with a Single Knee Electrogoniometer: A Deep Learning Approach

et al. 2020

Self Cite

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Artificial neural networks were satisfactorily implemented for assessing gait events from different walking data. This study aims to propose a novel approach for recognizing gait phases and events, based on deep-learning analysis of only sagittal knee-joint angle measured by a single electrogoniometer per leg. Promising classification/prediction performances have been previously achieved by surface-EMG studies; thus, a further aim is to test if adding electrogoniometer data could improve classification performances of state-of-the-art methods. Gait data are measured in about 10,000 strides from 23 healthy adults, during ground walking. A multi-layer perceptron model is implemented, composed of three hidden layers and a one-dimensional output. Classification/prediction accuracy is tested vs. ground truth represented by foot–floor-contact signals, through samples acquired from subjects not seen during training phase. Average classification-accuracy of 90.6 ± 2.9% and mean absolute value (MAE) of 29.4 ± 13.7 and 99.5 ± 28.9 ms in assessing heel-strike and toe-off timing are achieved in unseen subjects. Improvement of classification-accuracy (four points) and reduction of MAE (at least 35%) are achieved when knee-angle data are used to enhance sEMG-data prediction. Comparison of the two approaches shows as the reduction of set-up complexity implies a worsening of mainly toe-off prediction. Thus, the present electrogoniometer approach is particularly suitable for the classification tasks where only heel-strike event is involved, such as stride recognition, stride-time computation, and identification of toe walking.

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Lower Limb Motion Intent Recognition Based on Sensor Fusion and Fuzzy Multitask Learning

Wang,

Chen,

et al. 2024

IEEE Trans. Fuzzy Syst.

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Gait Phase Classification from Surface EMG Signals Using Neural Networks

Cited by 10 publications

References 13 publications

Estimation of Knee Movement from Surface EMG Using Random Forest with Principal Component Analysis

Estimation of Knee Movement from Surface EMG Using Random Forest with Principal Component Analysis

Recognition of Gait Phases with a Single Knee Electrogoniometer: A Deep Learning Approach

Lower Limb Motion Intent Recognition Based on Sensor Fusion and Fuzzy Multitask Learning

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