There are more than 800 million people in the world with chronic diseases. Many of these people do not have easy access to healthcare facilities for recovery. Telerehabilitation seeks to provide a solution to this problem. According to the researchers, the topic has been treated as medical aid, making an exchange between technological issues such as the Internet of Things and virtual reality. The main objective of this work is to design a distributed platform to monitor the patient’s movements and status during rehabilitation exercises. Later, this information can be processed and analyzed remotely by the doctor assigned to the patient. In this way, the doctor can follow the patient’s progress, enhancing the improvement and recovery process. To achieve this, a case study has been made using a PANGEA-based multi-agent system that coordinates different parts of the architecture using ubiquitous computing techniques. In addition, the system uses real-time feedback from the patient. This feedback system makes the patients aware of their errors so that they can improve their performance in later executions. An evaluation was carried out with real patients, achieving promising results.

The usage of drones is increasingly spreading into new fields of application, ranging from agriculture to security. One of these new applications is sound recording in areas of difficult access. The challenge that arises when using drones for this purpose is that the sound of the recorded sources must be separated from the noise produced by the drone. The intensity of the noise emitted by the drone depends on several factors such as engine power, propeller rotation speed, or propeller type. Noise reduction is thus one of the greatest challenges for the next generations of unmanned aerial vehicles (UAVs) and unmanned aerial systems (UAS). Even though some advances have been made on that matter, drones still produce a considerable noise. In this article, we approach the problem of removing drone noise from single-channel audio recordings using blind source separation (BSS) techniques, and in particular, the singular spectrum analysis algorithm (SSA). Furthermore, we propose an optimization of this algorithm with a spatial complexity of O (nt), which is significantly lower than the naive implementation which has a spatial complexity of O(tk ² ) (where n is the number of sounds to be recovered, t is the signal length and k is the window size). The best value for each parameter (window length and number of components used to reconstruct the source) is selected by testing a wide range of values on different noise-sound ratios. Our system can greatly reduce the noise produced by the drone on said recordings. On average, after the recording has been processed by our method, the noise is reduced by 1.41 decibels.