Embedded system for upper-limb exoskeleton based on electromyography control

Unfortunately, the implementation of the joint prediction on an embedded system for the exoskeleton based on the EMG control with non-pattern recognition-based is very rare. [...]this study presents an implementation of elbow-joint angle prediction on an embedded system to control an upper limb exoskeleton based on the EMG signal. [...]it is necessary to implement the joint angle prediction algorithm in the embedded system to reduce the size of the system, to minimize the power consumption and to achieve a real-time system. [...]the performance of the prediction was evaluated using root mean square error (RMSE) and Pearson's correlation coefficient (r). [...]in the sagittal plane motion, Lenzi proved that the EMG signal generated from triceps showed insignificant activities [8]. [...]in this work, the movement of the exoskeleton was only controlled by biceps. 2.3.