In this article, we present an obstacle avoidance controller implemented in a field programmable gate array for an electric wheelchair. It is based on a traditional approach with ultrasonic sensors and fuzzy logic. Various tests were conducted to characterize the prototype and to evaluate the controller performance. The results showed that the system is able to acquire data from sensors and make decisions 46.16 times per second. The sensors' coverage extends 3 m to the front, rear, left, and right sides of the wheelchair; moreover, the sensors detect 0.95-cm diameter objects at 40 cm. The power consumption was evaluated, and it was found that the hardware architecture reduces the battery life by only 0.87%. Furthermore, the controller helped to navigate in confined areas, avoiding obstacles with cautious movements and decreasing the likelihood of collision. The proposed methodology uses data from eight sonars distributed around the wheelchair to make navigation decisions, besides the hardware-based architecture guarantees real-time control and on-time response.

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Control theory is used to design automatic systems, which are able to maintain a desired behaviour despite of the disturbances. It is present in different machines we use every day; in fact, technical systems in our homes and all the industries are hard to imagine today without these concepts. Moreover, the same theories can be used for modelling life processes as a collection of inputs, outputs, plants and control loops. Feedback is one of the main concepts behind control; in particular, several examples of physiological control mechanisms for regulating life aspects can be found in the human anatomy, for example, blood pressure, cholesterol levels, body movements, the equilibrium, etc. Those processes can be damaged by the aging effects, diseases, accidents or when the mechanism has been broken and cannot be recovered naturally; consequently, it will be required external assistance. A relative new field in control theory is related with developing technology for helping with physiological and medicals problems. However, in comparison with machines, those physiological processes are highly nonlinear, with delays and slow responses. Another problem is when human becomes the operators using their capacities of decision making to close the control loop, as they are prone to errors and mistakes. For those reasons, the biomedical system needs to be carefully designed and several aspects have to be considered. This chapter gives a small review of some internal and external control processes within the human body and discusses how to interact with them for designing biomedical devices. Under this design scheme, a practical application of a smart electric wheelchair for assisting persons with strong disabilities is presented. These assistive robotic systems are in close contact with the user, and thus, it is determinant to have a user-friendly relation between the human and the interface. Therefore, intuitive interfaces were included in the design and an intelligent navigation assistant to guarantee a collision-free path.

Motor neuron diseases (MNDs) are a group of chronic neurological disorders characterized by the progressive failure of the motor system. Currently, these disorders do not have a definitive treatment; therefore, it is of huge importance to propose new and more advanced diagnoses and treatment options for MNDs. Nowadays, artificial intelligence is being applied to solve several real-life problems in different areas, including healthcare. It has shown great potential to accelerate the understanding and management of many health disorders, including neurological ones. Therefore, the main objective of this work is to offer a review of the most important research that has been done on the application of artificial intelligence models for analyzing motor disorders. This review includes a general description of the most commonly used AI algorithms and their usage in MND diagnosis, prognosis, and treatment. Finally, we highlight the main issues that must be overcome to take full advantage of what AI can offer us when dealing with MNDs.

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