A Study of Movement Classification of the Lower Limb Based on up to 4-EMG Channels

Toledo-Pérez, Diana C.; Martínez-Prado, Miguel-Angel; Gómez‐Loenzo, Roberto A.; Paredes-García, Wilfrido J.; Rodríguez‐Reséndiz, Juvenal

doi:10.3390/electronics8030259

Cited by 53 publications

(31 citation statements)

References 34 publications

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“…Although the method of applying noise generated by sensors was sufficiently useful, there was a limitation in that the disturbance or deformation generated while walking could not be applied. In future studies, it is necessary to investigate various methods for improving classification accuracy in the real-world through sensor fusion of EMG [ 35 ], IMU, etc. as well as the data augmentation.…”

Section: Resultsmentioning

confidence: 99%

Fast Wearable Sensor–Based Foot–Ground Contact Phase Classification Using a Convolutional Neural Network with Sliding-Window Label Overlapping

Jeon

Kim

et al. 2020

Sensors

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Classification of foot–ground contact phases, as well as the swing phase is essential in biomechanics domains where lower-limb motion analysis is required; this analysis is used for lower-limb rehabilitation, walking gait analysis and improvement, and exoskeleton motion capture. In this study, sliding-window label overlapping of time-series wearable motion data in training dataset acquisition is proposed to accurately detect foot–ground contact phases, which are composed of 3 sub-phases as well as the swing phase, at a frequency of 100 Hz with a convolutional neural network (CNN) architecture. We not only succeeded in developing a real-time CNN model for learning and obtaining a test accuracy of 99.8% or higher, but also confirmed that its validation accuracy was close to 85%.

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Section: Resultsmentioning

confidence: 99%

Fast Wearable Sensor–Based Foot–Ground Contact Phase Classification Using a Convolutional Neural Network with Sliding-Window Label Overlapping

Jeon

Kim

et al. 2020

Sensors

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“…Classification of EMG signals from lower-limb muscles is usually based on time/frequency domain features extraction [34]. In order to provide a comparison with a feature-based method, we implemented a classifier following the approach described in [13].…”

Section: Comparison With Feature-based Approachmentioning

confidence: 99%

A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking

et al. 2019

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Correctly identifying gait phases is a prerequisite to achieve a spatial/temporal characterization of muscular recruitment during walking. Recent approaches have addressed this issue by applying machine learning techniques to treadmill-walking data. We propose a deep learning approach for surface electromyographic (sEMG)-based classification of stance/swing phases and prediction of the foot–floor-contact signal in more natural walking conditions (similar to everyday walking ones), overcoming constraints of a controlled environment, such as treadmill walking. To this aim, sEMG signals were acquired from eight lower-limb muscles in about 10.000 strides from 23 healthy adults during level ground walking, following an eight-shaped path including natural deceleration, reversing, curve, and acceleration. By means of an extensive evaluation, we show that using a multi layer perceptron to learn hidden features provides state of the art performances while avoiding features engineering. Results, indeed, showed an average classification accuracy of 94.9 for learned subjects and 93.4 for unlearned ones, while mean absolute difference ( ± S D ) between phase transitions timing predictions and footswitch data was 21.6 ms and 38.1 ms for heel-strike and toe off, respectively. The suitable performance achieved by the proposed method suggests that it could be successfully used to automatically classify gait phases and predict foot–floor-contact signal from sEMG signals during level ground walking.

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“…Principal component analysis (PCA) is a statistical technique that performs a linear transformation from an original set of values into a smaller one of uncorrelated variables [15]. The idea was conceived of by K. Pearson [27] and later developed by Hotelling [28].…”

Section: Principal Component Analysismentioning

confidence: 99%

“…By calculating the eigenvalue eigenvector of the covariance matrix of the data, and selecting the matrix composed of eigenvectors corresponding to k features with the largest eigenvalue (i.e., the largest variance), the data matrix can be converted into a new space to achieve dimensional reduction of data features. However, in our study, PCA is used to improve the estimation performance, rather than dimension reduction, which is the same PCA application described in [15].…”

Section: Principal Component Analysismentioning

confidence: 99%

“…These predictions can be broadly categorized as classification and regression models [14]. For the classification, Toledo-Pérez et al [15] used a support vector machine (SVM) based on sEMG to classify the intention of right lower limb movement. Morbidoni et al [16] proposed a deep learning (DL) approach for sEMG-based classification of stance/swing phases and the prediction of the foot-floor-contact signal in more natural walking conditions.…”

Section: Introductionmentioning

confidence: 99%

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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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A Study of Movement Classification of the Lower Limb Based on up to 4-EMG Channels

Cited by 53 publications

References 34 publications

Fast Wearable Sensor–Based Foot–Ground Contact Phase Classification Using a Convolutional Neural Network with Sliding-Window Label Overlapping

Fast Wearable Sensor–Based Foot–Ground Contact Phase Classification Using a Convolutional Neural Network with Sliding-Window Label Overlapping

A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking

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

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