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Multi-Session Surface Electromyogram Signal Database for Personal Identification
Surface electromyogram (sEMG) refers to a biosignal acquired from the skin surface during the contraction of skeletal muscles, and a different signal waveform is generated, depending on the motion performed. Therefore, in contrast to generic personal identification, which uses only a piece of registered information, the sEMG changes the registered information in a personal identification method. The sEMG database (DB) for conventional personal identification has shortcomings, such as a few subjects and the inability to verify sEMG signal variability. In order to solve the problems of DBs, this paper describes a method for constructing a multi-session sEMG DB for many subjects. Data were obtained in two channels when each of the 200 subjects performed 12 motions. There were three sessions, and each motion was repeated 10 times in time intervals of a day or longer between each session. Furthermore, to verify the effectiveness of the constructed sEMG DB, we conducted a personal identification experiment. According to the experimental results, the accuracy for five subjects was 74.19%, demonstrating the applicability of the constructed multi-session sEMG DB.
Multi-Session Surface Electromyogram Signal Database for Personal Identification
Surface electromyogram (sEMG) refers to a biosignal acquired from the skin surface during the contraction of skeletal muscles, and a different signal waveform is generated, depending on the motion performed. Therefore, in contrast to generic personal identification, which uses only a piece of registered information, the sEMG changes the registered information in a personal identification method. The sEMG database (DB) for conventional personal identification has shortcomings, such as a few subjects and the inability to verify sEMG signal variability. In order to solve the problems of DBs, this paper describes a method for constructing a multi-session sEMG DB for many subjects. Data were obtained in two channels when each of the 200 subjects performed 12 motions. There were three sessions, and each motion was repeated 10 times in time intervals of a day or longer between each session. Furthermore, to verify the effectiveness of the constructed sEMG DB, we conducted a personal identification experiment. According to the experimental results, the accuracy for five subjects was 74.19%, demonstrating the applicability of the constructed multi-session sEMG DB.
Multi-Session Surface Electromyogram Signal Database for Personal Identification
Jin-Su Kim (author) / Cheol-Ho Song (author) / EunSang Bak (author) / Sung-Bum Pan (author)
2022
Article (Journal)
Electronic Resource
Unknown
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