A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons

Wei, Baichun; Ding, Zhen; Yi, Chunzhi; Guo, Hao; Wang, Zhipeng; Zhu, Jianfei; Jiang, Feng

doi:10.3389/fnbot.2021.704226

Cited by 10 publications

(4 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In another study, Wei et al [27] utilized sEMG signals to recognize gait phases and predict lower limb kinematics. The research employed LDA, SVM, and LSTM as predictive models, collecting sEMG data from nine leg muscles to estimate four gait parameters: initial contact, foot flat, heel-off, and toe-off.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Gait Parameters of Leg Movement with sEMG and Accelerometer Using CatBoost Machine Learning

Sharma,

Liu,

Zhu

et al. 2024

Electronics

View full text Add to dashboard Cite

This study aims to evaluate leg movement by integrating gait analysis with surface electromyography (sEMG) and accelerometer (ACC) data from the lower limbs. We employed a wireless, self-made, and multi-channel measurement system in combination with commercial GaitUp Physilog® 5 shoe-worn inertial sensors to record the walking patterns and muscle activations of 17 participants. This approach generated a comprehensive dataset comprising 1452 samples. To accurately predict gait parameters, a machine learning model was developed using features extracted from the sEMG signals of thigh and calf muscles, and ACCs from both legs. The study utilized evaluation metrics including accuracy (R2), Pearson correlation coefficient (PCC), root mean squared error (RMSE), mean absolute percentage error (MAPE), mean squared error (MSE), and mean absolute error (MAE) to evaluate the performance of the proposed model. The results highlighted the superiority of the CatBoost model over alternatives like XGBoost and Decision Trees. The CatBoost’s average PCCs for 17 temporospatial gait parameters of the left and right legs are 0.878 ± 0.169 and 0.921 ± 0.047, respectively, with MSE of 7.65, RMSE of 1.48, MAE of 1.00, MAPE of 0.03, and Accuracy (R2-Score) of 0.91. This research marks a significant advancement by providing a more comprehensive method for detecting and analyzing gait statuses.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Armand et al [25] used fuzzy decision trees to link clinical data with kinematic gait patterns of toe-walking. In the existing literature, only Liu et al [26] have predicted 11 temporospatial gait parameters, whereas other studies [22,27] have typically predicted 2-6 gait parameters.…”

Section: Introductionmentioning

confidence: 99%

Predicting Gait Parameters of Leg Movement with sEMG and Accelerometer Using CatBoost Machine Learning

Sharma,

Liu,

Zhu

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…When the number of muscles selected reaches five, skeletal muscle can be used for feature classification to reach a stable value; when the number reaches nine, the accuracy of feature classification begins to decline [20,21]. In order to reduce the error caused by muscle duplication, after studying the muscle activities related to the selected movements, the device was used to collect data on six selected muscles, namely, the biceps femoris, lateral femoris, medial femoris, tibialis anterior, gastrocnemius, and semitendinosus muscles of the human lower limbs, as the source of surface EMG [22,23]. These muscles are used to fix and stretch the bones in order to realize the action of the lower limbs, mainly involving the ankle, knee, and hip joints.…”

Section: Test and Inspectionmentioning

confidence: 99%

An Embedded Electromyogram Signal Acquisition Device

Lu,

Xu,

Liu

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

In this study, we design an embedded surface EMG acquisition device to conveniently collect human surface EMG signals, pursue more intelligent human–computer interactions in exoskeleton robots, and enable exoskeleton robots to synchronize with or even respond to user actions in advance. The device has the characteristics of low cost, miniaturization, and strong compatibility, and it can acquire eight-channel surface EMG signals in real time while retaining the possibility of expanding the channel. This paper introduces the design and function of the embedded EMG acquisition device in detail, which includes the use of wired transmission to adapt to complex electromagnetic environments, light signals to indicate signal strength, and an embedded processing chip to reduce signal noise and perform filtering. The test results show that the device can effectively collect the original EMG signal, which provides a scheme for improving the level of human–computer interactions and enhancing the robustness and intelligence of exoskeleton equipment. The development of this device provides a new possibility for the intellectualization of exoskeleton systems and reductions in their cost.

show abstract

“…Next, LSTM, bidirectional LSTM [30], and fully connected (FC) layers are concatenated to predict the gait phase timing. Wei et al [31] used nine-channel EMG signals to predict gait phases by an LSTMbased network. These EMG signals and gait phases are leveraged to predict the corresponding ankle angles by another LSTM-based network.…”

Section: Deep Learningmentioning

confidence: 99%

Transferable multi-modal fusion in knee angles and gait phases for their continuous prediction

Guo

Zheng

et al. 2023

J. Neural Eng.

View full text Add to dashboard Cite

Objective.The gait phase and joint angle are two essential and complementary components of kinematics during normal walking, whose accurate prediction is critical for lower-limb rehabilitation, such as controlling the exoskeleton robots. Multi-modal signals have been used to promote the prediction performance of the gait phase or joint angle separately, but it is still few reports to examine how these signals can be used to predict both simultaneously. Approach.To address this problem, we propose a new method named transferable multi-modal fusion (TMMF) to perform a continuous prediction of knee angles and corresponding gait phases by fusing multi-modal signals. Specifically, TMMF consists of a multi-modal signal fusion block, a time series feature extractor, a regressor, and a classifier. The multi-modal signal fusion block leverages the Maximum Mean Discrepancy to reduce the distribution discrepancy across different modals in the latent space, achieving the goal of transferable multi-modal fusion. Subsequently, by using the long short-term memory-based network, we obtain the feature representation from time series data to predict the knee angles and gait phases simultaneously. To validate our proposal, we design an experimental paradigm with random walking and resting to collect data containing multi-modal biomedical signals from electromyography, gyroscopes, and virtual reality.Main results.Comprehensive experiments on our constructed dataset demonstrate the effectiveness of the proposed method. TMMF achieves a root mean square error of 0.090±0.022 s in knee angle prediction and a precision of 83.7±7.7\% in gait phase prediction.Significance.We demonstrate the feasibility and validity of using TMMF to predict lower-limb kinematics continuously from multi-modal biomedical signals. This proposed method represents application potential in predicting the motor intent of patients with different pathologies.

show abstract

A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons

Cited by 10 publications

References 46 publications

Predicting Gait Parameters of Leg Movement with sEMG and Accelerometer Using CatBoost Machine Learning

Predicting Gait Parameters of Leg Movement with sEMG and Accelerometer Using CatBoost Machine Learning

An Embedded Electromyogram Signal Acquisition Device

Transferable multi-modal fusion in knee angles and gait phases for their continuous prediction

Contact Info

Product

Resources

About