# A Review on Upper Limb Rehabilitation Robots
## EMG-Driven Exoskeleton Robots  
An electromyograph (EMG) signal should be efficiently extracted in order to use it in rehabilitation robots [32]. There are two ways to extract the EMG signal: invasive, which results in an intramuscular electromyograph (iEMG), and noninvasive, which results in a surface electromyograph (sEMG) [18]. The invasive technique involves placing the electrodes on the motor units of the muscle, while the electrodes of the noninvasive technique are placed on the belly area of the muscle that providesmaximum contraction [54]. Despite the fact that iEMGs provide accurate EMG signals, sEMGs are commonly used due to their acceptability by the patient, and they do not require any invasive procedure [55]. Using the EMG signal as a control input for robots goes through several steps, starting by choosing the right muscle to obtain the EMG, then amplifying the extracted EMG to be, finally, processed and classified by a PC, as shown in Figure 6[54; 56].  
The EMG signal can be either processed in a time domain or frequency domain. Most of the current research focused on time domain processing, as it does not require further transformation and, eventually, provides low complex computations [57]. The root mean square (RMS), mean absolute value (MAV), summation of the absolute value (IAV), zero-crossing (ZC), slope sign changes (SSC) and waveform length are all features that can be extracted from the EMG in the time domain. Feature extraction in the time domain are evaluated based on its amplitude versus time and, therefore, its preferable in pattern recognition [57].  
Processing the EMG in the frequency domain, however, requires more computing time when compared to time domain processing. Consequently, processing the EMG in the frequency domain is usually used to assess muscle fatigue over time. The mean frequency (MNF), median frequency (MDF) and mean power frequency are features that can be extracted from the EMG in the frequency domain [58].  
The raw EMG data should be amplified, filtered and segmented before extracting its features. In addition, the length of the segment was proven to have an effect on the accuracy of the EMG classification. It was shown that increasing the segment length from 125 ms to 500 ms provided better accuracy. The response time for a prosthetic limb should not be more than 300 ms in order to be considered a real-time response [55; 57].  
This paper explains a few examples on how the EMG signal is used as a control input for rehabilitation robots. EMG-based control methods are categorized into two terms: pattern recognition-based and non-pattern recognition-based [54]. Both methods share the acquisition and the segmenting of the obtained signal [54]. The pattern recognition-based method, however, requires further processing to obtain an accurate EMG signal, where the acquired signal goes through three further stages, which are data segmenting, feature extraction and classification. Consequently, many assistive robots use the pattern recognition-based method, as it provides accurate information [55; 59].  
A few examples of EMG-driven upper limb robots and their control methods will be explained in the following subsections. A robot comparison based on the input signal, features used and mechanism of the controller will be also explained. Table 4 summarizes the EMG-driven rehabilitation robots and their control methods.  
Figure 6: Block diagram of extracting and processing the surface electromyograph (sEMG) signal.