**4. Virtual prostheses**

As described earlier, great effort has been devoted to devise new strategies for the control of artificial limbs fitted to patients with congenital defects or who have lost their limbs in accidents or surgery. In general, the devices do not properly resemble the real counterpart, do not react in the same manner, do not provide proper feedback and cannot be controlled using the "natural" interfaces, i.e., signals emanated from the central nervous system. Therefore, a number of difficulties arise when a new user tries to control an artificial limb, since he/she will have to devise a completely new strategy to generate input signals for the prostheses, so that it will act according to his/her wishes. This leads to a lengthy, tiring, and sometimes frustrating, training period. That is true for the great majority of the strategies for prosthesis control that have been designed to date.

Recently, a number of research groups turned their attention to VR and AR, in an attempt to overcome some of those problems. Although many works can be found in the literature, we have chosen just a few to illustrate the concept.

Pons *et al.* [36] describe the use of VR to support the training process for a multifunctional myoelectric hand prosthesis (MANUS) capable of generating up to four grasping modes (cylindrical, precision, lateral and hook grasps, in addition to wrist pronation-supination).

**Figure 9.** One of the MANUS users performing a combined cylindrical grasp and wrist rotation (extracted from [36]).

As expected, multifunctional prostheses pose an additional problem for users: the more dexterous the device, the higher the number of command channels required to control it. As a result, a large number of different EMG commands, generally obtained by extra EMG channels, are required for successful management of the prosthesis. To minimize the number of channels, the authors proposed a three-bit ternary EMG command strategy. The users were asked to produce EMG bursts (by sudden contraction of a single muscle) and, if proper EMG thresholds could be defined, each burst was classified in three different levels. Each of those three levels were then given the digital values "0", "1" or "2" (no signal, low, high), corresponding to one ternary bit. In so doing, if the user generates three bits, he/she could generate up to 27 different combinations (commands) from a single muscle. However, since the commands starting with "0" (i.e., "0XY") were not valid, the three-bit ternary strategy allowed the generation of 18 effective commands. This means that, from a single muscle, the user could control up to 18 different functions/actions of the prosthesis. However, that is no easy task to learn. Hence, a special training device, based on VR simulation of the multifunctional prosthesis, was created to enable the learning of that "EMG command language". Only after the training process was finished, the prosthesis was fitted and real manipulative operation started. The authors report that all of the volunteers were able to successfully perform basic commands after about 45 minutes.

Computational Intelligence in Electromyography Analysis – 418 A Perspective on Current Applications and Future Challenges

prosthesis control that have been designed to date.

have chosen just a few to illustrate the concept.

training of prosthetic users.

**4. Virtual prostheses** 

(extracted from [36]).

Based on the discussions above, we can infer that VR and AR incorporate a number of features with great potential to overcome some of the difficulties associated with the

As described earlier, great effort has been devoted to devise new strategies for the control of artificial limbs fitted to patients with congenital defects or who have lost their limbs in accidents or surgery. In general, the devices do not properly resemble the real counterpart, do not react in the same manner, do not provide proper feedback and cannot be controlled using the "natural" interfaces, i.e., signals emanated from the central nervous system. Therefore, a number of difficulties arise when a new user tries to control an artificial limb, since he/she will have to devise a completely new strategy to generate input signals for the prostheses, so that it will act according to his/her wishes. This leads to a lengthy, tiring, and sometimes frustrating, training period. That is true for the great majority of the strategies for

Recently, a number of research groups turned their attention to VR and AR, in an attempt to overcome some of those problems. Although many works can be found in the literature, we

Pons *et al.* [36] describe the use of VR to support the training process for a multifunctional myoelectric hand prosthesis (MANUS) capable of generating up to four grasping modes (cylindrical, precision, lateral and hook grasps, in addition to wrist pronation-supination).

**Figure 9.** One of the MANUS users performing a combined cylindrical grasp and wrist rotation

As expected, multifunctional prostheses pose an additional problem for users: the more dexterous the device, the higher the number of command channels required to control it. As a result, a large number of different EMG commands, generally obtained by extra EMG In similar fashion, Resnik *et al.* [37] show the use of VR as an aiding tool for training users of advanced upper-limb prostheses. The device known as DEKA Arm (DEKA Research & Development Corporation) allows users 10 powered degrees of movement (Figure 10a). A VR environment program (Figure 10b) was created to allow users to practice controlling an avatar, using the controls designed to operate the DEKA Arm in the real world.

**Figure 10.** (a) DEKA Arm displayed on manikin; (b) VR avatar (extracted from [37]).

The authors report that the VR environment allows a gradual acclimatization to the arm, as the experience with the arm-control scheme prior to use of the physical arm allows a staged introduction of the new elements of the system. However, the system did not allow for interaction with virtual objects, i.e., it was not possible, for instance, the manipulation of an object with the virtual hand. Nevertheless, the system proved to be an important asset for upper-limb users who must master a large number of controls and for those who need a structured learning environment, due to cognitive deficits.
