@inproceedings{scholars8848,
             doi = {10.1109/ROMA.2016.7847833},
            year = {2017},
            note = {cited By 4; Conference of 2nd IEEE International Symposium on Robotics and Manufacturing Automation, ROMA 2016 ; Conference Date: 25 September 2016 Through 27 September 2016; Conference Code:126431},
           title = {Development of a model for sEMG based joint-torque estimation using Swarm techniques},
         journal = {2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation, ROMA 2016},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
          author = {Nurhanim, K. and Elamvazuthi, I. and Izhar, L. I. and Ganesan, T. and Su, S. W.},
            isbn = {9781509009282},
        keywords = {Electromyography; Estimation; Joints (anatomy); Manufacture; Mathematical transformations; Robotics; Torque; Traffic signals, Coefficient of determination; Joint torques; Knee extension; Rehabilitation robot; Research focus; Sum squared error; Surface electromyography; Swarm techniques, Particle swarm optimization (PSO)},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015922961&doi=10.1109\%2fROMA.2016.7847833&partnerID=40&md5=0e20efc222787d3f690f600731ce8840},
        abstract = {Over the years, numerous researchers have explored the relationship between surface electromyography (sEMG) signal with joint torque that would be useful to develop a suitable controller for rehabilitation robot. This research focuses on the transformation of sEMG signal by adopting a mathematical model to find the estimated joint torque of knee extension. Swarm techniques such as Particle Swarm Optimization (PSO) and Improved Particle Swarm Optimization (IPSO) were adapted to optimize the mathematical model for estimated joint torque. The correlation between the estimated joint torque and actual joint torque were determined by Coefficient of Determination (R2) and fitness value of Sum Squared Error (SSE). The outcome of the research shows that both the PSO and IPSO have yielded promising results. {\^A}{\copyright} 2016 IEEE.}
}