TY  - CONF
VL  - 42
PB  - Elsevier B.V.
SP  - 175
AV  - none
ID  - scholars4586
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925609274&doi=10.1016%2fj.procs.2014.11.049&partnerID=40&md5=44e15092b2d1933f2c14eb6f985d6b2a
A1  - Nurhanim, K.
A1  - Elamvazuthi, I.
A1  - Vasant, P.
A1  - Ganesan, T.
A1  - Parasuraman, S.
A1  - Ahamed Khan, M.K.A.
N1  - cited By 13; Conference of International Symposium on Medical and Rehabilitation Robotics and Instrumentation, MRRI 2013 ; Conference Date: 2 December 2013 Through 4 December 2013; Conference Code:115828
EP  - 182
SN  - 18770509
N2  - The conventional robotic assistive device was based on pre-programmed functions by the robot expert. This makes it difficult for stroke patients use it effectively due to difficulty of torque setting that is suitable for the user movement. Electromyography (EMG) signal measures the electrical signal of muscle contraction. The EMG-based robotics assistive technology would enable the stroke patients to control the robot movement according to the user's own strength of natural movement. This paper discusses the mapping of surface electromyography signals (sEMG) to torque for robotic rehabilitation. Particle swarm optimization (PSO) has been applied as a control algorithm for a number of selected mathematical models. sEMG signals were determined as input data to the mathematical model where parameters of the mathematical model were optimized using PSO. Hence, the good correlated estimated torque as output was obtained. © 2014 The Authors.
TI  - Joint torque estimation model of surface electromyography(sEMG) based on swarm intelligence algorithm for robotic assistive device
Y1  - 2014///
ER  -