Recently, the US Department of Transportation's Federal Aviation Administration and other international organizations have proposed a set of requirements for small unmanned aerial vehicles (UAVs) to operate for nonrecreational purposes. However, existing UAV architectures fulfill only some of the established requirements, and not all in one solution. This article presents an event-driven serviceoriented architecture that allows autonomous UAVs to satisfy all these requirements and to detect critical situations, performing real-time decision making.
In the last few years, telerehabilitation and telecare have become important topics in healthcare since they enable people to remain independent in their own homes by providing person-centered technologies to support the individual. These technologies allows elderly people to be assisted in their home, instead of traveling to a clinic, providing them wellbeing and personalized health care. The literature shows a great number of interesting proposals to address telerehabilitation and telecare scenarios, which may be mainly categorized into two broad groups, namely wearable devices and context-aware systems. However, we believe that these apparently different scenarios may be addressed by a single context-aware approach, concretely a vision-based system that can operate automatically in a non-intrusive way for the elderly, and this is the goal of this paper. We present a general approach based on 3D cameras and neural network algorithms that offers an efficient solution for two different scenarios of telerehabilitation and telecare for elderly people. Our empirical analysis reveals the effectiveness and accuracy of the algorithms presented in our approach and provides more than promising results when the neural network parameters are properly adjusted.
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