TY - GEN
T1 - Motion recognition of the bilateral upper-limb rehabilitation using sEMG based on ensemble EMD
AU - Song, Xuan
AU - Guo, Shuxiang
AU - Gao, Baofeng
AU - Wang, Zhenyu
PY - 2014
Y1 - 2014
N2 - Surface electromyography signal (sEMG) is deeply related with the activation of motor muscle and motion of human body, which can be used to estimate the intention of the human movement. So it is advantaged in the application of bilateral rehabilitation, where hemiplegic patients can perform rehabilitation training to their impaired limbs following the motion of intact limbs by using a certain training tool. Therefore, a novel framework based primarily on empirical mode decomposition (EMD) was developed to reduce all the three noise contaminations from surface EMG. In addition to regular EMD, the ensemble EMD (EEMD) was also examined for surface EMG de-noising. The advantages of the EMD based methods were demonstrated by comparing them with the traditional digital filters, using signals derived from our routine electrode array surface EMG recordings. The experiments showed good performance of motion recognition with EEMD compared to the angel record derived from an inertia sensor.
AB - Surface electromyography signal (sEMG) is deeply related with the activation of motor muscle and motion of human body, which can be used to estimate the intention of the human movement. So it is advantaged in the application of bilateral rehabilitation, where hemiplegic patients can perform rehabilitation training to their impaired limbs following the motion of intact limbs by using a certain training tool. Therefore, a novel framework based primarily on empirical mode decomposition (EMD) was developed to reduce all the three noise contaminations from surface EMG. In addition to regular EMD, the ensemble EMD (EEMD) was also examined for surface EMG de-noising. The advantages of the EMD based methods were demonstrated by comparing them with the traditional digital filters, using signals derived from our routine electrode array surface EMG recordings. The experiments showed good performance of motion recognition with EEMD compared to the angel record derived from an inertia sensor.
KW - Ensemble empirical mode decomposition
KW - Recognition for motion
KW - Surface electromyography
UR - https://www.scopus.com/pages/publications/84906991118
U2 - 10.1109/ICMA.2014.6885945
DO - 10.1109/ICMA.2014.6885945
M3 - Conference contribution
AN - SCOPUS:84906991118
SN - 9781479939787
T3 - 2014 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2014
SP - 1637
EP - 1642
BT - 2014 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2014
PB - IEEE Computer Society
T2 - 11th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2014
Y2 - 3 August 2014 through 6 August 2014
ER -