This paper provides a method to infer finger flexing motions using a 4-channel surface Electronyogram （sEMG）. Surface EMGs are hannless to the humnan body and easily done. However, they do not reflect the activity o...This paper provides a method to infer finger flexing motions using a 4-channel surface Electronyogram （sEMG）. Surface EMGs are hannless to the humnan body and easily done. However, they do not reflect the activity of specific nerves or muscles, unlike invasive EMCs. On the other hand, the non-invasive type is difficult to use for discriminating various motions while using only a small number of electrodes. Surface EMG data in this study were obtained from four electodes placed around the forearm. The motions were the flexion of each 5 single fingers （thumb, index finger, middle finger, ring finger, and little fingers）. One subject was trained with these motions and another left was untrained. The maximum likelihood estimation method was used to infer the finger motion. Experimental results have showed that this method could be useful for recognizing finger motions.The average accuracy was as high as 95%.展开更多
基金supported by the The Ministry of Knowledge Economy,Koreaunder the ITRC(Information Technology Research Center)support programsupervised by the ⅡTA(Institute for Information Technology Advancement)ⅡTA-2008-C1090-0803-0006
文摘This paper provides a method to infer finger flexing motions using a 4-channel surface Electronyogram （sEMG）. Surface EMGs are hannless to the humnan body and easily done. However, they do not reflect the activity of specific nerves or muscles, unlike invasive EMCs. On the other hand, the non-invasive type is difficult to use for discriminating various motions while using only a small number of electrodes. Surface EMG data in this study were obtained from four electodes placed around the forearm. The motions were the flexion of each 5 single fingers （thumb, index finger, middle finger, ring finger, and little fingers）. One subject was trained with these motions and another left was untrained. The maximum likelihood estimation method was used to infer the finger motion. Experimental results have showed that this method could be useful for recognizing finger motions.The average accuracy was as high as 95%.
