People suffering from rheumatoid arthritis are normally prescribed with physical exercises. The realization of these exercise routines should be supervised to prevent wrongly adopted postures or bad movements that end up harming the affected articulations. The motivational component associated with a supervised session is also important. On the contrary, having access to qualified supervision is not always possible due to different reasons, such as tight schedules, limited mobility, or economic reasons. This article presents a system for at-home rehabilitation, based on Microsoft Kinect, that resembles the role played by the physiotherapist during the supervision of an exercise session. The system not only supervises the realization of exercises but it also collects and manages information about the exercise-session history. Additionally, and despite affecting young people, rheumatoid arthritis is a disease that mainly affects seniors. For this reason, the system provides a natural interface to ease the interaction. Finally, the performance of the proposed system has been evaluated using a three-dimensional dynamic measurement system as a gold standard to validate the accuracy of the measurements. Obtained results demonstrate the potential of the Kinect-based system for at-home rehabilitation exercise routines.

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COVID-19 has become a global pandemic during 2020 due to its high contagiousness and the high mobility of the world's population today. In just one year, this virus has caused millions of infections and deaths worldwide. These numbers will continue to grow until the population becomes immune to the virus thanks to an effective vaccine. Until this is possible, the only viable strategy is to try to stop its expansion through preventive measures such as limiting mobility, the use of masks, etc. In order to support these measures, this article presents a service to provide safe navigation solutions to reduce the likelihood of infection by avoiding potential conflict areas in the city. To identify these hotspots, a strategy that combines a rule-based system and a common-sense knowledge base is proposed. Through this strategy, an occupation model and a danger model are inferred. This requires the prior capture of knowledge about the general functioning of the city, its inhabitants and the virus. The proposed service makes decisions from these two models. Finally, a validation process has been carried out through surveys to evaluate the proposed solution. Obtained results demonstrate the potential of the proposed solution as a tool to identify safe routes that allow citizens to move around the city with low exposure to COVID-19.INDEX TERMS commonsense knowledge, covid-19, rule-based systems, smart city

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