  In EMG based pattern recognition (EMG-PR), deep learning-based techniques
have become more prominent for their self-regulating capability to extract
discriminant features from large data-sets. Moreover, the performance of
traditional machine learning-based methods show limitation to categorize over a
certain number of classes and degrades over a period of time. In this paper, an
accurate, robust, and fast convolutional neural network-based framework for EMG
pattern identification is presented. To assess the performance of the proposed
system, five publicly available and benchmark data-sets of upper limb
activities were used. This data-set contains 49 to 52 upper limb motions
(NinaPro DB1, NinaPro DB2, and NinaPro DB3), Data with force variation, and
data with arm position variation for intact and amputated subjects. The
classification accuracies of 91.11% (53 classes), 89.45% (49 classes), 81.67%
(49 classes of amputees), 95.67% (6 classes with force variation), and 99.11%
(8 classes with arm position variation) have been observed during the testing
and validation. The performance of the proposed system is compared with the
state of art techniques in the literature. The findings demonstrate that
classification accuracy and time complexity have improved significantly. Keras,
TensorFlow's high-level API for constructing deep learning models, was used for
signal pre-processing and deep-learning-based algorithms. The suggested method
was run on an Intel 3.5GHz Core i7, 7th Gen CPU with 8GB DDR4 RAM.
