Myoelectric movements in various degrees but during

Myoelectric upper limbs prosthesis has been
developed 1940s, despite advanced in computing power and the algorithms these
prosthesis are based on the earliest control strategies.

 The aim
from this review is to detail the development, advantages and disadvantages of
these prosthesis control system.

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Introduction:

Myoelectric control of prosthetic upper limbs
is an established technology.

Whereas battery life time, weight saving,
cosmetic features and its component have been developed and improved over time
but the control strategies didn’t changed and still based on the earliest
strategies.

   Amputee’s
still controlling prosthesis by using one muscle group to open a hand and the
other muscle to close the hand and there are some advanced systems which allow
movement in wrist. Anyway this is far from the ability of the neutral human
hand and this may frustrate the prosthetic users.

Since the mid-1970s there is pattern
recognition to deciphering the useful information present in the
electromyographic (EMG) signal to allow more movements in various degrees but
during this days this capability not yet been applied due to high processing
power.

Moreover in daily living the arm affected by
several changes of variation such as the arm position, re-positioning of the
electrode and fatigue and this is challenge in accuracy of pattern
recognition of these daily variation.

The engineer took the advantages of varying
intensity of muscular contractions to design a state of the prosthesis which
known as on/off control or crisp control.

Activation threshold allowed the actions of the
prosthesis, when the muscle slight contraction the hand closed, when the muscle
strong contraction the hand open, and when no contraction the device will be
slight, also the same application is applied when there are two muscle group
controlling the state of the hand and two electrodes is used to determine
opposite action.

   

(Early controlling in
prosthesis hand)

A pattern recognition algorithm provided by
multifunctional control which classify a muscle activation was introduce to the
prosthetic research community in 1970 in tested research conditions.

These algorithms was include a sequence of
event that contain EMG data which was first viewed before using it in
controlling, however these data was useless 
and didn’t show interest until 1992 when Hudgins et al put a basis for
rising wave from these algorithms.

The development of these algorithms increased a
number of input channels to increase the accuracy and the multifunctional
control to include wrist flexion, extension, forearm pronation and supination,?rearm pronation and
supination,hims  humeral rotation, ulnar and radial deviation
at the wrist, and the current trend is to identify muscle pattern to control
individual fingers

This advance was in laboratory and there are
some factors that prevent to transition from laboratory to clinical conditions.
Most laboratory test was applied on either healthy volunteers or amputees in
static conditions. This is notable that the testing database was collected in
these conditions, when the muscle contraction it would be in the ideal case and
it’s different from the real situation, moreover in real situation the
muscle contraction pattern change in stressful situations or reacts to wrong
movement also during active movement, displacement of electrodes which can
reduced  the reception of electric
field  and reduce the maximum
polarizations at each compartment due to the displacement of electrodes from
the target area. In addition sweating, changes of the muscle size and shape
post-surgery all these factors can influence the EMG signal characteristics.
The effect of some these factors can be relieved such as the increasing
distance between electrodes and alignment with respect to muscle fibers to
improve signal acquisition.