**4.2. EMG based control methods of upper-limb prostheses**

This section reviews recent EMG based controllers of upper-limb prostheses. Controlling of a prosthetic device using EMG signals is cumbersome task compared to controlling an exoskel‐ eton device using EMG signals, since already the person who wears the device has lost the best site of the body to get the required EMG signals for the controlling of the prosthesis. It makes the number of inputs for the control system to be less and obviously causes to under‐ perform a conventional control system. So far researchers [21-23] have proposed different control methods to control prosthetic devices. In addition, the introduction of more advanced technologies such as targeted muscle reinnervation and implantable electrodes marks new boundaries in prosthesis controlling. The ensuing subsections review EMG based control methods of upper-limb prostheses. The logic for selecting a particular control method is its significance and novelty.

**Figure 17.** Experimental setup of EMG signals based Controller of Saga prosthetic arm [22]

signals. The RMS is determined as,

where *vi*

can be found in [22].

correct human motion intention.

Information from the raw EMG signals is extracted by taking the RMS value of the raw EMG

*i*=1 *N vi*

is the voltage value at *i th* sampling and *N* is the number of samples in a segment.

The EBC is provided with four EMG RMSs. Three kinds of fuzzy linguistic variables for each input were defined. Ten kinds of fuzzy if-then rules for elbow joint torque control and seven kinds of if-then rules for hand torque control were prepared. In addition, details on the KBC

Even though the prosthetic arm is capable of catering to the human motion intension to a certain degree, still some improvements can be made. Since the KBC is trained to offline Vicon data for a given set of daily activities, the orientations of the limbs other than those trained for cannot be achieved using the KBC. Therefore, an inertial measurement unit (IMU) can be fixed to the stem arm of the amputee and used as an interface to read the real time kinematic data. By using the real time kinematic data as an input to the controller it can be improved to reach almost all the orientations of daily activities. In addition a hybrid control method - EMG coupled with EEG - can be used to enhance the performance of the controller by obtaining

<sup>2</sup> (3)

Recent Trends in EMG-Based Control Methods for Assistive Robots

http://dx.doi.org/10.5772/56174

257

 *RMS* = 1 / *N* ∑
