In the face of the large number of people with motor function disabilities,rehabilitation robots have attracted more and more attention.In order to promote the active participation of the user's motion intention i...In the face of the large number of people with motor function disabilities,rehabilitation robots have attracted more and more attention.In order to promote the active participation of the user's motion intention in the assisted rehabilitation process of the robots,it is crucial to establish the human motion prediction model.In this paper,a hybrid prediction model built on long short-term memory(LSTM)neural network using surface electromyography(sEMG)is applied to predict the elbow motion of the users in advance.This model includes two sub-models:a back-propagation neural network and an LSTM network.The former extracts a preliminary prediction of the elbow motion,and the latter corrects this prediction to increase accuracy.The proposed model takes time series data as input,which includes the sEMG signals measured by electrodes and the continuous angles from inertial measurement units.The offline and online tests were carried out to verify the established hybrid model.Finally,average root mean square errors of 3.52°and 4.18°were reached respectively for offline and online tests,and the correlation coefficients for both were above 0.98.展开更多
Lower limb motion recognition techniques commonly employ Surface Electromyographic Signal(sEMG)as input and apply a machine learning classifier or Back Propagation Neural Network(BPNN)for classification.However,this a...Lower limb motion recognition techniques commonly employ Surface Electromyographic Signal(sEMG)as input and apply a machine learning classifier or Back Propagation Neural Network(BPNN)for classification.However,this artificial feature engineering technique is not generalizable to similar tasks and is heavily reliant on the researcher’s subject expertise.In contrast,neural networks such as Convolutional Neural Network(CNN)and Long Short-term Memory Neural Network(LSTM)can automatically extract features,providing a more generalized and adaptable approach to lower limb motion recognition.Although this approach overcomes the limitations of human feature engineering,it may ignore the potential correlation among the sEMG channels.This paper proposes a spatial–temporal graph neural network model,STGNN-LMR,designed to address the problem of recognizing lower limb motion from multi-channel sEMG.STGNN-LMR transforms multi-channel sEMG into a graph structure and uses graph learning to model spatial–temporal features.An 8-channel sEMG dataset is constructed for the experimental stage,and the results show that the STGNN-LMR model achieves a recognition accuracy of 99.71%.Moreover,this paper simulates two unexpected scenarios,including sEMG sensors affected by sweat noise and sudden failure,and evaluates the testing results using hypothesis testing.According to the experimental results,the STGNN-LMR model exhibits a significant advantage over the control models in noise scenarios and failure scenarios.These experimental results confirm the effectiveness of the STGNN-LMR model for addressing the challenges associated with sEMG-based lower limb motion recognition in practical scenarios.展开更多
High-precision fiber Bragg grating se nsor demodulation instrument with wide-range dynamic scanning can effectivel y improve the measuring range of the optical fiber grating sensor.Ethernet com munication module is an...High-precision fiber Bragg grating se nsor demodulation instrument with wide-range dynamic scanning can effectivel y improve the measuring range of the optical fiber grating sensor.Ethernet com munication module is an extremely important part of the high-precision grating sensor demodulation device.Network interface based on Ethernet control chip D M9000A is used to send and receive the Bragg grating sensing pulse.The network transformer YL18-2050S is used to convert and filter the pulse from network.The transmitting and receiving program of grating demodulation,hardware circuit of Ethernet transmission interface are designed.The experimental results show that the network interface can achieve accurate and real-time transmissi on of the grating sensing information at high speed.展开更多
The sEMG signals are collected from the vastus lateralis,vastus medialis,biceps femoris,and semitendinosus of lower extremity during level walking among control subjects and knee osteoarthritis (OA) patients,the latte...The sEMG signals are collected from the vastus lateralis,vastus medialis,biceps femoris,and semitendinosus of lower extremity during level walking among control subjects and knee osteoarthritis (OA) patients,the latter including mild,moderate and severe degree.The 5-fold cross-validation is used to measure the accuracy of the proposed analysis algorithm on collected sEMG recordings.For comparison,the more classical feature vectors of form factor,degree of skewness,kurtosis,and wavelet entropy are also tested.In experiment,the normalized energy ratio and marginal spectrum ratio achieve larger accuracy than the other features for all the four muscular groups.Moreover the accuracy of vastus medialis and biceps femoris are larger than that of vastus lateralis and semitendinosus.These results suggest that the normalized energy ratio and marginal spectrum ratio via the analysis of knee sEMG signals by HHT can server as characteristic parameters to easily classify osteoarthritis with noninvasive method.The more important muscular groups for maintaining the knee joint function are medialis and biceps femoris;as a result of that they should be exercise especially for rehabilitation.展开更多
People's working capability is badly affected when they sufer an amputated arm.Artifcial replacements with prosthetic devices to get a satisfactory level of performance for essential functions with the currently a...People's working capability is badly affected when they sufer an amputated arm.Artifcial replacements with prosthetic devices to get a satisfactory level of performance for essential functions with the currently available prosthetic technology are very dificult.Myoelectric arm prostheses are becoming popular because they are operated by a natural contraction of intact muscles.Hence,SEMG based artifdal arm was fabricated.The system cousists of diferent electronic and mechanical assemblies for operation of hand utilizing microcontroller in order to have minimum signal loss during its processing.With the hep of relay switching connected to low power DC motor,system is capable of opening and closing of grip according to individual wish.展开更多
The surface electromyography(sEMG)is one of the basic processing techniques to the gesture recognition because of its inherent advantages of easy collection and non-invasion.However,limited by feature extraction and c...The surface electromyography(sEMG)is one of the basic processing techniques to the gesture recognition because of its inherent advantages of easy collection and non-invasion.However,limited by feature extraction and classifier selection,the adaptability and accuracy of the conventional machine learning still need to promote with the increase of the input dimension and the number of output classifications.Moreover,due to the different characteristics of sEMG data and image data,the conventional convolutional neural network(CNN)have yet to fit sEMG signals.In this paper,a novel hybrid model combining CNN with the graph convolutional network(GCN)was constructed to improve the performance of the gesture recognition.Based on the characteristics of sEMG signal,GCN was introduced into the model through a joint voting network to extract the muscle synergy feature of the sEMG signal.Such strategy optimizes the structure and convolution kernel parameters of the residual network(ResNet)with the classification accuracy on the NinaPro DBl up to 90.07%.The experimental results and comparisons confirm the superiority of the proposed hybrid model for gesture recognition from the sEMG signals.展开更多
Natural rubber(NR)latex is a renewable colloidal dispersion used in medical gloves,coatings,and flexible products.It is known for its excellent elasticity and film-forming ability but is limited by insufficient mechan...Natural rubber(NR)latex is a renewable colloidal dispersion used in medical gloves,coatings,and flexible products.It is known for its excellent elasticity and film-forming ability but is limited by insufficient mechanical robustness and chemical resistance.Incorporating nanofillers,such as graphene oxide(GO),is an effective approach to enhance its performance;however,achieving strong interfacial compatibility between hydrophilic GO and the nonpolar rubber matrix remains challenging.To overcome this issue,a multifunctional interfacial design inspired by mussel adhesion chemistry was developed to construct a hierarchical and cohesive GO network within the NR latex matrix.A tannic acid-based modifier(TM)bearing catechol and thiol groups was synthesized and anchored onto latex particles via hydrogen bonding with surface proteins and phospholipids,enabling subsequentπ-πinteractions and hydrogen bonding with GO nanosheets.This guided the selective self-assembly of GO into a continuous segregated network along the latex particle boundaries.Hierarchical interface reinforcement was achieved through Eu^(3+)ligand coordination.The incorporation of GO and enhancement of interfacial interactions promoted strain-induced crystallization,resulting in increased crystallinity and improved load transfer.The resulting composite film containing 0.5 part per hundred rubber GO and the bioinspired interface exhibited a tensile strength that was 107.8%higher than that of the pure NR latex film,while maintaining an elongation at break of 915%.Tear strength increased by 118.5%,toughness reached 61.7 MJ/m~3,nitrogen permeability decreased by 20.1%,and antibacterial efficiency against both Escherichia coli and Staphylococcus aureus reached 99.9%.The films also exhibited enhanced resistance to organic solvents,acids,and alkalis.This study provides a green and scalable strategy for fabricating high-performance NR latex-based products suitable for medical,protective,and engineering applications.展开更多
基金the National Key Research and Development Program of China(No.2020YFC2007500)the Science and Technology Commission of Shanghai Municipality(No.20DZ2220400)。
文摘In the face of the large number of people with motor function disabilities,rehabilitation robots have attracted more and more attention.In order to promote the active participation of the user's motion intention in the assisted rehabilitation process of the robots,it is crucial to establish the human motion prediction model.In this paper,a hybrid prediction model built on long short-term memory(LSTM)neural network using surface electromyography(sEMG)is applied to predict the elbow motion of the users in advance.This model includes two sub-models:a back-propagation neural network and an LSTM network.The former extracts a preliminary prediction of the elbow motion,and the latter corrects this prediction to increase accuracy.The proposed model takes time series data as input,which includes the sEMG signals measured by electrodes and the continuous angles from inertial measurement units.The offline and online tests were carried out to verify the established hybrid model.Finally,average root mean square errors of 3.52°and 4.18°were reached respectively for offline and online tests,and the correlation coefficients for both were above 0.98.
文摘Lower limb motion recognition techniques commonly employ Surface Electromyographic Signal(sEMG)as input and apply a machine learning classifier or Back Propagation Neural Network(BPNN)for classification.However,this artificial feature engineering technique is not generalizable to similar tasks and is heavily reliant on the researcher’s subject expertise.In contrast,neural networks such as Convolutional Neural Network(CNN)and Long Short-term Memory Neural Network(LSTM)can automatically extract features,providing a more generalized and adaptable approach to lower limb motion recognition.Although this approach overcomes the limitations of human feature engineering,it may ignore the potential correlation among the sEMG channels.This paper proposes a spatial–temporal graph neural network model,STGNN-LMR,designed to address the problem of recognizing lower limb motion from multi-channel sEMG.STGNN-LMR transforms multi-channel sEMG into a graph structure and uses graph learning to model spatial–temporal features.An 8-channel sEMG dataset is constructed for the experimental stage,and the results show that the STGNN-LMR model achieves a recognition accuracy of 99.71%.Moreover,this paper simulates two unexpected scenarios,including sEMG sensors affected by sweat noise and sudden failure,and evaluates the testing results using hypothesis testing.According to the experimental results,the STGNN-LMR model exhibits a significant advantage over the control models in noise scenarios and failure scenarios.These experimental results confirm the effectiveness of the STGNN-LMR model for addressing the challenges associated with sEMG-based lower limb motion recognition in practical scenarios.
文摘High-precision fiber Bragg grating se nsor demodulation instrument with wide-range dynamic scanning can effectivel y improve the measuring range of the optical fiber grating sensor.Ethernet com munication module is an extremely important part of the high-precision grating sensor demodulation device.Network interface based on Ethernet control chip D M9000A is used to send and receive the Bragg grating sensing pulse.The network transformer YL18-2050S is used to convert and filter the pulse from network.The transmitting and receiving program of grating demodulation,hardware circuit of Ethernet transmission interface are designed.The experimental results show that the network interface can achieve accurate and real-time transmissi on of the grating sensing information at high speed.
基金Sponsored by the International Science and Technology Cooperation Project of China(Grant No.2009DFA32050)
文摘The sEMG signals are collected from the vastus lateralis,vastus medialis,biceps femoris,and semitendinosus of lower extremity during level walking among control subjects and knee osteoarthritis (OA) patients,the latter including mild,moderate and severe degree.The 5-fold cross-validation is used to measure the accuracy of the proposed analysis algorithm on collected sEMG recordings.For comparison,the more classical feature vectors of form factor,degree of skewness,kurtosis,and wavelet entropy are also tested.In experiment,the normalized energy ratio and marginal spectrum ratio achieve larger accuracy than the other features for all the four muscular groups.Moreover the accuracy of vastus medialis and biceps femoris are larger than that of vastus lateralis and semitendinosus.These results suggest that the normalized energy ratio and marginal spectrum ratio via the analysis of knee sEMG signals by HHT can server as characteristic parameters to easily classify osteoarthritis with noninvasive method.The more important muscular groups for maintaining the knee joint function are medialis and biceps femoris;as a result of that they should be exercise especially for rehabilitation.
文摘People's working capability is badly affected when they sufer an amputated arm.Artifcial replacements with prosthetic devices to get a satisfactory level of performance for essential functions with the currently available prosthetic technology are very dificult.Myoelectric arm prostheses are becoming popular because they are operated by a natural contraction of intact muscles.Hence,SEMG based artifdal arm was fabricated.The system cousists of diferent electronic and mechanical assemblies for operation of hand utilizing microcontroller in order to have minimum signal loss during its processing.With the hep of relay switching connected to low power DC motor,system is capable of opening and closing of grip according to individual wish.
基金supported by the Development of Sleep Disordered Breathing Detection and Auxiliary Regulation System Project(No.2019I1009)。
文摘The surface electromyography(sEMG)is one of the basic processing techniques to the gesture recognition because of its inherent advantages of easy collection and non-invasion.However,limited by feature extraction and classifier selection,the adaptability and accuracy of the conventional machine learning still need to promote with the increase of the input dimension and the number of output classifications.Moreover,due to the different characteristics of sEMG data and image data,the conventional convolutional neural network(CNN)have yet to fit sEMG signals.In this paper,a novel hybrid model combining CNN with the graph convolutional network(GCN)was constructed to improve the performance of the gesture recognition.Based on the characteristics of sEMG signal,GCN was introduced into the model through a joint voting network to extract the muscle synergy feature of the sEMG signal.Such strategy optimizes the structure and convolution kernel parameters of the residual network(ResNet)with the classification accuracy on the NinaPro DBl up to 90.07%.The experimental results and comparisons confirm the superiority of the proposed hybrid model for gesture recognition from the sEMG signals.
基金supported by the National Natural Science Foundation of China(No.52303063)。
文摘Natural rubber(NR)latex is a renewable colloidal dispersion used in medical gloves,coatings,and flexible products.It is known for its excellent elasticity and film-forming ability but is limited by insufficient mechanical robustness and chemical resistance.Incorporating nanofillers,such as graphene oxide(GO),is an effective approach to enhance its performance;however,achieving strong interfacial compatibility between hydrophilic GO and the nonpolar rubber matrix remains challenging.To overcome this issue,a multifunctional interfacial design inspired by mussel adhesion chemistry was developed to construct a hierarchical and cohesive GO network within the NR latex matrix.A tannic acid-based modifier(TM)bearing catechol and thiol groups was synthesized and anchored onto latex particles via hydrogen bonding with surface proteins and phospholipids,enabling subsequentπ-πinteractions and hydrogen bonding with GO nanosheets.This guided the selective self-assembly of GO into a continuous segregated network along the latex particle boundaries.Hierarchical interface reinforcement was achieved through Eu^(3+)ligand coordination.The incorporation of GO and enhancement of interfacial interactions promoted strain-induced crystallization,resulting in increased crystallinity and improved load transfer.The resulting composite film containing 0.5 part per hundred rubber GO and the bioinspired interface exhibited a tensile strength that was 107.8%higher than that of the pure NR latex film,while maintaining an elongation at break of 915%.Tear strength increased by 118.5%,toughness reached 61.7 MJ/m~3,nitrogen permeability decreased by 20.1%,and antibacterial efficiency against both Escherichia coli and Staphylococcus aureus reached 99.9%.The films also exhibited enhanced resistance to organic solvents,acids,and alkalis.This study provides a green and scalable strategy for fabricating high-performance NR latex-based products suitable for medical,protective,and engineering applications.
