The objective of the work is to investigate the classifcation of different movements based on the surface electromyogram(SEMG)pattern recognition method.The testing was conducted for four arm movements using several e...The objective of the work is to investigate the classifcation of different movements based on the surface electromyogram(SEMG)pattern recognition method.The testing was conducted for four arm movements using several experiments with artificial neural network class fication scheme.Six time domain features were extracted and consequently dlassification was implemented using back propagation neural dassifier(BPNC).Further,the realization of projected network was verified using cross validation(CV)process;hence ANOVA algorithm was carried out.Performance of the network is analyzed by considering mean square error(MSE)value.A comparison was performed between the extracted feat ures and back propagation network results reported in the literature.The concurrent result indicates the significance of proposed network with classification accuracy(CA)of 100%recorded from two channels,while analysis of variance technique helps in investigating the effectiveness of classified sigmal for recognition tasks.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
文摘The objective of the work is to investigate the classifcation of different movements based on the surface electromyogram(SEMG)pattern recognition method.The testing was conducted for four arm movements using several experiments with artificial neural network class fication scheme.Six time domain features were extracted and consequently dlassification was implemented using back propagation neural dassifier(BPNC).Further,the realization of projected network was verified using cross validation(CV)process;hence ANOVA algorithm was carried out.Performance of the network is analyzed by considering mean square error(MSE)value.A comparison was performed between the extracted feat ures and back propagation network results reported in the literature.The concurrent result indicates the significance of proposed network with classification accuracy(CA)of 100%recorded from two channels,while analysis of variance technique helps in investigating the effectiveness of classified sigmal for recognition tasks.
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.
