The rehabilitation of patients should not only be limited to the first phases during intense hospital care but also support and therapy should be guaranteed in later stages, especially during daily life activities if the patient’s state requires this. However, aid should only be given to the patient if needed and as much as it is required. To allow this, automatic self-initiated movement support and patient-cooperative control strategies have to be developed and integrated into assistive systems. In this work, we first give an overview of different kinds of neuromuscular diseases, review different forms of therapy, and explain possible fields of rehabilitation and benefits of robotic aided rehabilitation. Next, the mechanical design and control scheme of an upper limb orthosis for rehabilitation are presented. Two control models for the orthosis are explained which compute the triggering function and the level of assistance provided by the device. As input to the model fused sensor data from the orthosis and physiology data in terms of electromyography (EMG) signals are used.

In this work an upper limb active orthosis for assistive rehabilitation is presented. The design and torque control scheme of the orthosis that take into account important aspects of human rehabilitation, are described. Furthermore, first results of successful muscle activity detection and processing for the operation of the orthosis in two movement directions are presented. The proposed system is the first step towards an adaptive support of patients with respect to the strength of their muscle activity. To allow an adaptive support, different methods for EMG analysis have to be applied which allow to correlate muscle activity strength with the recorded signal and thus enable to adapt the support of the orthosis to the needs of the patient and state of therapy. 1 Neuronal plasticity is the ability of brain to reorganize itself by forming new neural connections. This form of adjustment allows the brain to compensate injury and disease and to adjust activities in response to new situations or to changes in the environment (Johnston, 2009).

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In addition to areas of application in people’s everyday lives and the area of education and services, robots are primarily envisioned in non-immediate living environments by the society—i.e., in inaccessible or even hostile environments to humans. The results of this population survey clearly demonstrate that such application options come across with a high level of acceptance and application potential among the population. Nevertheless, it is expected that the underlying AI in such systems works reliably and that safety for humans is guaranteed.In this chapter, the results of the study are compared with state-of-the-art systems from classical application environments for robots, like the deep-sea and space. Here, systems have to interact with their environment to a large extent on their own over longer periods of time. Although typically the designs are such that humans are able to intervene in specific situations and so external decisions are possible, the requirements for autonomy are also extremely high. From this perspective one can easily derive what kind of requirements are also necessary, and what challenges are still in front of us, when robots should be acting largely autonomous in our everyday life.

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