Our previous study suggested that the subcutaneous muscle displacement caused by joint movements might alter muscle activation patterns and thus affect the classification performance.To further analyze the effect of j...Our previous study suggested that the subcutaneous muscle displacement caused by joint movements might alter muscle activation patterns and thus affect the classification performance.To further analyze the effect of joint movements on the online performance of Electromyography(EMG)Pattern Recognition(PR),this study assessed online classification performance with and without joint movements.EMG signals were recorded from the dominant forearm of 10 able-bodied subjects under two motion scenarios:Hand and Wrist Joints Unconstrained(HAWJU)and Constrained(HAWJC).Sixth-order autoregressive coefficients and four time-domain features were extracted from EMG signals.Linear Discriminant Analysis(LDA)models were trained to perform an online performance evaluation of the limb motions.The experimental results showed that the four online performance metrics:Motion Selection Time(MST),Motion Completion Time(MCT),Motion Completion Rate(MCR),and Online Classification Accuracy(ONCA)were 0.35 s,1.44 s,97.40%,and 82.61%for HAWJU and 0.37 s,1.47 s,89.70%,and 73.57%for HAWJC,respectively.The outcomes of this study indicated that subcutaneous muscle displacement due to joint movements has a positive effect on online classification performance.The absence of joint movements may be a physiological factor contributing to the poor online performance of the EMG-PR of transradial amputees.This study can provide a new perspective for improving the online performance of EMG-PR for transradial amputees.展开更多
Unsupervised and supervised pattern recognition( PR)techniques are used to classify the acoustic emission( AE) data originating from the quasi-isotropic self-reinforced polyethylene composites,in order to identify the...Unsupervised and supervised pattern recognition( PR)techniques are used to classify the acoustic emission( AE) data originating from the quasi-isotropic self-reinforced polyethylene composites,in order to identify the various mechanisms in the multiangle-ply thermoplastic composites. Ultra-high molecular weight polyethylene / low density polyethylene( UHMWPE / LDPE)composites were made and tested under quasi-static tensile load. The failure process was monitored by the AE technique. The collected AE signals were classified by unsupervised and supervised PR techniques, respectively. AE signals were clustered with unsupervised PR scheme automatically and mathematically. While in the supervised PR scheme,the labeled AE data from simple lay-up UHMWPE / LDPE laminates were utilized as the reference data.Comparison was drawn according to the analytical results. Fracture surfaces of the UHMWPE / LDPE specimens were observed by a scanning electron microscope( SEM) for some physical support. By combining both classification results with the observation results,correlations were established between the AE signal classes and their originating damage modes. The comparison between the two classifying schemes showed a good agreement in the main damage modes and their failure process. It indicates both PR techniques are powerful for the complicated thermoplastic composites. Supervised PR scheme can lead to a more precise classification in that a suitable reference data set is input.展开更多
基金The authors thank all volunteers who participated in the study.This work was supported by National Natural Science Foundation of China(Grant No.52005364,52122501)This work was also supported by the Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education(Tianjin University).
文摘Our previous study suggested that the subcutaneous muscle displacement caused by joint movements might alter muscle activation patterns and thus affect the classification performance.To further analyze the effect of joint movements on the online performance of Electromyography(EMG)Pattern Recognition(PR),this study assessed online classification performance with and without joint movements.EMG signals were recorded from the dominant forearm of 10 able-bodied subjects under two motion scenarios:Hand and Wrist Joints Unconstrained(HAWJU)and Constrained(HAWJC).Sixth-order autoregressive coefficients and four time-domain features were extracted from EMG signals.Linear Discriminant Analysis(LDA)models were trained to perform an online performance evaluation of the limb motions.The experimental results showed that the four online performance metrics:Motion Selection Time(MST),Motion Completion Time(MCT),Motion Completion Rate(MCR),and Online Classification Accuracy(ONCA)were 0.35 s,1.44 s,97.40%,and 82.61%for HAWJU and 0.37 s,1.47 s,89.70%,and 73.57%for HAWJC,respectively.The outcomes of this study indicated that subcutaneous muscle displacement due to joint movements has a positive effect on online classification performance.The absence of joint movements may be a physiological factor contributing to the poor online performance of the EMG-PR of transradial amputees.This study can provide a new perspective for improving the online performance of EMG-PR for transradial amputees.
基金Scientific Research Foundation of Guangdong Polytechnic,China(No.K2010201)
文摘Unsupervised and supervised pattern recognition( PR)techniques are used to classify the acoustic emission( AE) data originating from the quasi-isotropic self-reinforced polyethylene composites,in order to identify the various mechanisms in the multiangle-ply thermoplastic composites. Ultra-high molecular weight polyethylene / low density polyethylene( UHMWPE / LDPE)composites were made and tested under quasi-static tensile load. The failure process was monitored by the AE technique. The collected AE signals were classified by unsupervised and supervised PR techniques, respectively. AE signals were clustered with unsupervised PR scheme automatically and mathematically. While in the supervised PR scheme,the labeled AE data from simple lay-up UHMWPE / LDPE laminates were utilized as the reference data.Comparison was drawn according to the analytical results. Fracture surfaces of the UHMWPE / LDPE specimens were observed by a scanning electron microscope( SEM) for some physical support. By combining both classification results with the observation results,correlations were established between the AE signal classes and their originating damage modes. The comparison between the two classifying schemes showed a good agreement in the main damage modes and their failure process. It indicates both PR techniques are powerful for the complicated thermoplastic composites. Supervised PR scheme can lead to a more precise classification in that a suitable reference data set is input.
