Estimation of Torque Based on EMG using ANFIS

There are wide verities of possible human movements that involve a range from the gait for the lifting of a load by a factory worker to the performance of a superior athlete. Output of the movement can be described by a large number of kinematic variables like knee joint angle, torque. This paper proposes a system that contains a non-parametric model with EMG signal of two muscles is used as input to estimate torque. The mapping of EMG to any joint dynamics is very subject dependent. It also depends on walking, running, jumping or climbing. Each type of posture consists of combination of isometric, eccentric and concentric type of muscle contraction with different intensity level depending on velocity, angle and lifted weight (muscle activation level). To capture the EMG signal pattern which is complex and so dynamic in time and space, an adaptive feature in computational intelligence is desired which will not only learn but also make decision based on EMG channel signal pattern to estimate torque. The EMG signal has been collected from volunteer who has completed the knee joint extension with maximum voluntary contraction (MVC) at different degree/sec ranging from 5deg/Sec to 360deg/Sec. The volunteer was also asked to perform extension with moderate and low effort against different impedance like 5deg/Sec, 20deg/Sec, and 45deg/Sec. RMS feature along with 2nd order digital filter has been used to smooth the raw EMG signal. The proposed study is intended to explore an ANFIS like Neuro-Fuzzy type knowledge based adaptive network with embedded RBF kernel neuron to estimate torque.