**6. Future directions**

Computational Intelligence in Electromyography Analysis – 422 A Perspective on Current Applications and Future Challenges

Figure 14.

**5. Conclusion** 

results showed a near perfect performance of the classifier (95% to 100% rate of success). The output of the neural network is then used as control input (position and motion) to the virtual device, which can be rendered and mixed with the real world scene, as shown in

Note that this system allows the user to interact within the virtual environment - the virtual myoelectric prosthesis can touch and grab other virtual objects embedded in the real scene (such as the cube and the kettle in Figure 14). Also, a strong cognitive feedback is provided by this real time mixture of virtual objects with the real environment, given the feeling that

This chapter presented an overview on the search of human beings for artificial devices capable of restoring, if not all, at least part of the functionality lost when we are affected by diseases, congenital disorders or trauma that results in the loss of a limb. Focusing on upper limb prosthesis, a series of sophisticated technical solutions have been proposed during the past decade to design devices whose behavior and control approach that of their healthy natural counterparts. However, as described along this chapter, operating a highly complex artificial limb is not a simple task. This is especially true for myoelectric multifunctional prostheses with many degrees of freedom. Since the necessary control commands, in most instances, can be very different from the "natural" commands, learning how to produce them is extremely difficult and time consuming. With the advent of Virtual and Augmented Reality, those technologies have been proposed as relevant tools to address some of the limitations of conventional training techniques. It is possible to design a virtual device very similar, in shape and behavior, to a real one. Also, it is even possible to collect commands from the real world (EMG signals generated by remnant muscles) and use them as inputs to control the actions of a virtual prosthesis in an Augment Reality Environment, according to the training stage of the user, or any other setup defined by the therapist. In so doing, those techniques allow for a considerable reduction of physical and metal efforts usually

**Figure 14.** User´s point of view within the AR environment (extracted from [39]).

it is almost possible to touch the virtual arm with the real one, and vice-versa.

necessary to master the control of a prosthetic device.

Despite the progress achieved so far, the authors believe that, as technology advances, the use of virtual and augmented reality for controlling myoelectric prostheses should also undergo continuous improvements. These future developments should be focused on issues such as: (i) improving the modeling of the virtual devices, in order to increase the sense of realism when compared to actual prostheses; (ii) new adaptive protocols for controlling the virtual prosthesis, so that it could emulated different strategies and different joint actuators; and (iii) the design of new devices to provide physiological feedback, allowing the user to "feel" what the virtual prosthesis is actually doing, thus, increasing the feeling of a complete mix between the real and virtual worlds.
