Motion intention recognition is considered the key technology for enhancing the training effectiveness of upper limb rehabilitation robots for stroke patients,but traditional recognition systems are difficult to simul...Motion intention recognition is considered the key technology for enhancing the training effectiveness of upper limb rehabilitation robots for stroke patients,but traditional recognition systems are difficult to simultaneously balance real-time performance and reliability.To achieve real-time and accurate upper limb motion intention recognition,a multi-modal fusion method based on surface electromyography(sEMG)signals and arrayed flexible thin-film pressure(AFTFP)sensors was proposed.Through experimental tests on 10 healthy subjects(5 males and 5 females,age 23±2 years),sEMG signals and human-machine interaction force(HMIF)signals were collected during elbow flexion,extension,and shoulder internal and external rotation.The AFTFP signals based on dynamic calibration compensation and the sEMG signals were processed for feature extraction and fusion,and the recognition performance of single signals and fused signals was compared using a support vector machine(SVM).The experimental results showed that the sEMG signals consistently appeared 175±25 ms earlier than the HMIF signals(p<0.01,paired t-test).In offline conditions,the recognition accuracy of the fused signals exceeded 99.77%across different time windows.Under a 0.1 s time window,the real-time recognition accuracy of the fused signals was 14.1%higher than that of the single sEMG signal,and the system’s end-to-end delay was reduced to less than 100 ms.The AFTFP sensor is applied to motion intention recognition for the first time.And its low-cost,high-density array design provided an innovative solution for rehabilitation robots.The findings demonstrate that the AFTFP sensor adopted in this study effectively enhances intention recognition performance.The fusion of its output HMIF signals with sEMG signals combines the advantages of both modalities,enabling real-time and accurate motion intention recognition.This provides efficient command output for human-machine interaction in scenarios such as stroke rehabilitation.展开更多
For intelligent transportation systems(ITS),understanding pedestrian motion intention is crucial for enhancing traffic safety,enabling human-centered mobility services,and facilitating adaptive vehicle-pedestrian inte...For intelligent transportation systems(ITS),understanding pedestrian motion intention is crucial for enhancing traffic safety,enabling human-centered mobility services,and facilitating adaptive vehicle-pedestrian interactions.This paper proposes a pedestrian gait recognition method based on a modified particle swarm optimization-support vector machine(MPSO-SVM),utilizing fused surface electromyography(sEMG)signals and ankle joint angles.Seven lower-limb gait features were extracted from these signals to characterize walking patterns.The MPSO algorithm optimizes the support vector machine(SVM)parameters to improve classification performance.Experimental results based on data collected from healthy subjects demonstrate a recognition accuracy exceeding 92.5%across four gait phases.The proposed method offers significantly enhanced accuracy and robustness compared to traditional classifiers.These results suggest that the method is suitable for deployment in intelligent traffic control systems,autonomous vehicle navigation,and urban pedestrian behavior prediction.展开更多
The electromyography(EMG)signal is the biocurrent associated with muscle contraction and can be used as the input signal to a myoelectric intelligent bionic hand to control different gestures of the hand.Increasing th...The electromyography(EMG)signal is the biocurrent associated with muscle contraction and can be used as the input signal to a myoelectric intelligent bionic hand to control different gestures of the hand.Increasing the number of myoelectric-signal channels can yield richer information of motion intention and improve the accuracy of gesture recognition.However,as the number of acquisition channels increases,its effect on the improvement of the accuracy of gesture recognition gradually diminishes,resulting in the improvement of the control effect reaching a plateau.To address these problems,this paper presents a proposed method to improve gesture recognition accuracy by virtually increasing the number of EMG signal channels.This method is able to improve the recognition accuracy of various gestures by virtually increasing the number of EMG signal channels and enriching the motion intention information extracted from data collected from a certain number of physical channels,ultimately providing a solution to the issue of the recognition accuracy plateau caused by saturation of information from physical recordings.Meanwhile,based on the idea of the filtered feature selection method,a quantitative measure of sample sets(separability of feature vectors[SFV])derived from the divergence and correlation of the extracted features is introduced.The SFV value can predict the classification effect before performing the classification,and the effectiveness of the virtual-dimension increase strategy is verified from the perspective of feature set differentiability change.Compared to the statistical motion intention recognition success rate,SFVis a more representative and faster measure of classification effectiveness and is also suitable for small sample sets.展开更多
基金supported by Guangdong Basic and Applied Basic Research Foundation(No.2024A1515012810).
文摘Motion intention recognition is considered the key technology for enhancing the training effectiveness of upper limb rehabilitation robots for stroke patients,but traditional recognition systems are difficult to simultaneously balance real-time performance and reliability.To achieve real-time and accurate upper limb motion intention recognition,a multi-modal fusion method based on surface electromyography(sEMG)signals and arrayed flexible thin-film pressure(AFTFP)sensors was proposed.Through experimental tests on 10 healthy subjects(5 males and 5 females,age 23±2 years),sEMG signals and human-machine interaction force(HMIF)signals were collected during elbow flexion,extension,and shoulder internal and external rotation.The AFTFP signals based on dynamic calibration compensation and the sEMG signals were processed for feature extraction and fusion,and the recognition performance of single signals and fused signals was compared using a support vector machine(SVM).The experimental results showed that the sEMG signals consistently appeared 175±25 ms earlier than the HMIF signals(p<0.01,paired t-test).In offline conditions,the recognition accuracy of the fused signals exceeded 99.77%across different time windows.Under a 0.1 s time window,the real-time recognition accuracy of the fused signals was 14.1%higher than that of the single sEMG signal,and the system’s end-to-end delay was reduced to less than 100 ms.The AFTFP sensor is applied to motion intention recognition for the first time.And its low-cost,high-density array design provided an innovative solution for rehabilitation robots.The findings demonstrate that the AFTFP sensor adopted in this study effectively enhances intention recognition performance.The fusion of its output HMIF signals with sEMG signals combines the advantages of both modalities,enabling real-time and accurate motion intention recognition.This provides efficient command output for human-machine interaction in scenarios such as stroke rehabilitation.
基金partly supported by the Research Project of Higher Education in Henan Province(Grant no.23A413008)partly by the Project of the Science and Technology Department of Henan Province(Grant no.252102221011).
文摘For intelligent transportation systems(ITS),understanding pedestrian motion intention is crucial for enhancing traffic safety,enabling human-centered mobility services,and facilitating adaptive vehicle-pedestrian interactions.This paper proposes a pedestrian gait recognition method based on a modified particle swarm optimization-support vector machine(MPSO-SVM),utilizing fused surface electromyography(sEMG)signals and ankle joint angles.Seven lower-limb gait features were extracted from these signals to characterize walking patterns.The MPSO algorithm optimizes the support vector machine(SVM)parameters to improve classification performance.Experimental results based on data collected from healthy subjects demonstrate a recognition accuracy exceeding 92.5%across four gait phases.The proposed method offers significantly enhanced accuracy and robustness compared to traditional classifiers.These results suggest that the method is suitable for deployment in intelligent traffic control systems,autonomous vehicle navigation,and urban pedestrian behavior prediction.
文摘The electromyography(EMG)signal is the biocurrent associated with muscle contraction and can be used as the input signal to a myoelectric intelligent bionic hand to control different gestures of the hand.Increasing the number of myoelectric-signal channels can yield richer information of motion intention and improve the accuracy of gesture recognition.However,as the number of acquisition channels increases,its effect on the improvement of the accuracy of gesture recognition gradually diminishes,resulting in the improvement of the control effect reaching a plateau.To address these problems,this paper presents a proposed method to improve gesture recognition accuracy by virtually increasing the number of EMG signal channels.This method is able to improve the recognition accuracy of various gestures by virtually increasing the number of EMG signal channels and enriching the motion intention information extracted from data collected from a certain number of physical channels,ultimately providing a solution to the issue of the recognition accuracy plateau caused by saturation of information from physical recordings.Meanwhile,based on the idea of the filtered feature selection method,a quantitative measure of sample sets(separability of feature vectors[SFV])derived from the divergence and correlation of the extracted features is introduced.The SFV value can predict the classification effect before performing the classification,and the effectiveness of the virtual-dimension increase strategy is verified from the perspective of feature set differentiability change.Compared to the statistical motion intention recognition success rate,SFVis a more representative and faster measure of classification effectiveness and is also suitable for small sample sets.
