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

Morbidoni, Christian; Cucchiarelli, Alessandro; Fioretti, Sandro; Nardo, Francesco Di

doi:10.3390/electronics8080894

Cited by 70 publications

(110 citation statements)

References 34 publications

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“…The sEMG signals were focused on the time domain because of the time-sequence. The amplitude extracted from the raw sEMG signals was used for the training of the RFPCA, similar to the work done by the authors of [16], who directly used the envelopes of the EMG signal to train the network. This feature was carried out from digital filtering and using simple math.…”

Section: Signal Processingmentioning

confidence: 99%

“…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. Nazmi et al [17] proposed a classification system for both stance and swing phases, by extracting the patterns of electromyography signals from time domain features and feeding them into an artificial neural network (ANN) classifier.…”

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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Section: Signal Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Guan

Zou

et al. 2019

Electronics

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“…Recent availability of technological advancements is allowing to limit the experimental complexity of gait-analysis set-up, providing a less expensive, less intrusive, and more comfortable estimation of gait data. Robust artificial intelligence techniques for managing a lot of biological data and signals coming from smart sensors such as inertial measurements units (IMU) are undoubtedly among the most used approaches to this aim [6][7][8][9][10][11][12][13][14]. Specifically, the problem of estimating temporal parameters of gait could take great advantage by the development of these new approaches.…”

Section: Introductionmentioning

confidence: 99%

“…Frequently, the use of IMUs appears to be suitable for a smart assessment of walking parameters, such as gait-phase duration and timing of heel strike (time when the foot touches the ground) and toe off (time when the foot-toes clear the ground) [11]. Attempts based on artificial intelligence were also applied in a satisfactory way for the assessment of gait parameters during walking [6,7,9,10,[12][13][14][15].…”

Section: Introductionmentioning

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%

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

et al. 2020

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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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Recent Developments and Future Directions of Wearable Skin Biosignal Sensors

Kim,

Min,

2023

Advanced Sensor Research

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This review article explores the transformative advancements in wearable biosignal sensors powered by machine learning, focusing on four notable biosignals: electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), and photoplethysmogram (PPG). The integration of machine learning with these biosignals has led to remarkable breakthroughs in various medical monitoring and human–machine interface applications. For ECG, machine learning enables automated heartbeat classification and accurate disease detection, improving cardiac healthcare with early diagnosis and personalized interventions. EMG technology, combined with machine learning, facilitates real‐time prediction and classification of human motions, revolutionizing applications in sports medicine, rehabilitation, prosthetics, and virtual reality interfaces. EEG analysis powered by machine learning goes beyond traditional clinical applications, enabling brain activity understanding in psychology, neurology, and human–computer interaction, and holds promise in brain–computer interfaces. PPG, augmented with machine learning, has shown exceptional progress in diagnosing and monitoring cardiovascular and respiratory disorders, offering non‐invasive and accurate healthcare solutions. These integrated technologies, powered by machine learning, open new avenues for medical monitoring and human–machine interaction, shaping the future of healthcare.

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

Cited by 70 publications

References 34 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

Recent Developments and Future Directions of Wearable Skin Biosignal Sensors

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