Myoelectric Control Systems for Hand Rehabilitation Device: A Review

Abstract

One of the challenges of the hand rehabilitation device is to create a smooth interaction between the device and user. The smooth interaction can be achieved by considering myoelectric signal generated by human's muscle. Therefore, the so-called myoelectric control system (MCS) has been developed since the 1940s. Various MCS's has been proposed, developed, tested, and implemented in various hand rehabilitation devices for different purposes. This article presents a review of MCS in the existing hand rehabilitation devices. The MCS can be grouped into main groups, the non-pattern recognition and pattern recognition ones. In term of implementation, it can be classified as MCS for prosthetic and exoskeleton hand. Main challenges for MCS today is the robustness issue that hampers the implementation of MCS on the clinical application.

A review on the application of intelligent control strategies for post-stroke hand rehabilitation machines

“…When the EMG signal amplitude is greater than the set threshold value is on, and conversely is off, this control method is the most stable and simple, mostly used in commercial prosthetic devices. 75 Proportional control is a proportional mapping of the amplitude of the EMG signal to the controlled object, where the controlled object can be the position or speed of the motor rotation, etc. This control method is more in line with human habits than the switch control, and the user will feel more natural in the process of use, however, proportional control is suitable for controlling single-degree-of-freedom exoskeletons due to its mapping relationship, and it is difficult to cope with the complex situation of multi-degree-of-freedom.…”

A Fast EMG-Based Algorithm for Upper-Limb Motion Intention Detection by Using Levant’s Differentiators

“…In detail, a myoelectric control system estimates the user's movement intention from EMG signals and uses this information to control the activation of the device [6], [7]. Fig.…”

“…The time-frequency domain characteristics provide an accurate description of the physical phenomenon; however, these features are obtained through local stationarity, i.e., signals are assumed to be stationary within a time window [8], which causes a delay in the movement detection depending on the length of the window. Feature extraction in the time domain is the preferred class of methods used for processing EMG signals [4], [6], [7], [14]. The most frequently used feature extraction methods in this domain are: mean absolute value (MAV) [10], [15]- [19], root mean square (RMS) [4], [6], [11], [20], [21], zero-crossing (ZC) [4], [10], [19], [22], variance (VAR) [10], [22], mean absolute difference (MAD) [23], and slope sign change (SSC) [19].…”

Design, Construction and Tests of a Low-Cost Myoelectric Thumb

“…Besides these and other low-cost prostheses, there are also attempts to train people with neurological injuries or to extend the physical properties of non-handicapped people by exoskeletons [23][24][25]. Regarding hands or fingers, there are a few studies and design approaches investigating the effect of a third thumb, e.g., controlled by the feet [26], allowing to give people extended motor skills and at the same time enabling neuro-scientific research on neuroplasticity [27,28].…”