  Prosthetic hands can be used to support upper-body amputees. Myoelectric
prosthesis, one of the externally-powered active prosthesis categories,
requires proper processing units in addition to recording electrodes and
instrumentation amplifiers. In this paper, the following myoelectric prosthesis
control methods were discussed in detail: On-off and finite-state,
proportional, direct, and posture, simultaneous, classification and
regression-based control, and deep learning methods. Myoelectric control
performance indices, such as completion time and rate, throughput, lag, and
path length, were reviewed. The advantages and disadvantages of the control
methods were also discussed. Some of myoelectric prosthesis control's
significant challenges are comfort, durability, cost, the application of
under-sampled signals, and electrode shift. Moreover, the proposed algorithms
must be usually tuned after each don and doff, which is not comfortable for the
users. Real-time simultaneous and proportional myoelectric control, resampling
human's arm, has brought much attention. However, increasing the degree of
freedom reduces the overall performance. Applying a 3D printed prosthesis arm
and under-sampled electromyographic signals could reduce the fabrication cost
and improve the application of such methods in practice. There are many
technological and clinical challenges in this area to reduce the prosthesis
rejection rate.
