  Electromyography (EMG) signal analysis is a popular method for controlling
prosthetic and gesture control equipment. For portable systems, such as
prosthetic limbs, real-time low-power operation on embedded processors is
critical, but to date, there has been no record of how existing EMG analysis
approaches support such deployments. This paper presents a novel approach to
time-domain classification of multi-channel EMG signals harnessed from
randomly-placed sensors according to the wrist-hand movements which caused
their occurrence. It shows how, by employing a very small set of time-domain
features, Kernel Fisher discriminant feature projection and Radial Bias
Function neural network classifiers, nine wrist-hand movements can be detected
with accuracy exceeding 99% - surpassing the state-of-the-art on record. It
also shows how, when deployed on ARM Cortex-A53, the processing time is not
only sufficient to enable real-time processing but is also a factor 50 shorter
than the leading time-frequency techniques on record.
