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In an increasingly aging world, the effort to automate tasks associated with the care of elderly dependent individuals becomes more and more relevant if quality care provision at sustainable costs is desired. One of the tasks susceptible to automation in this field is the automatic detection of falls. The research effort undertaken to develop automatic fall detection systems has been quite substantial and has resulted in reliable fall detection systems. However, individuals who could benefit from these systems only consider their use in certain scenarios. Among them, a relevant scenario is the one associated to semi-supervised patients during the night who wake up and get out of bed, usually disoriented, feeling an urgent need to go to the toilet. Under these circumstances, usually, the person is not supervised, and a fall could go unnoticed until the next morning, delaying the arrival of urgently needed assistance. In this scenario, associated with nighttime rest, the patient prioritizes comfort, and in this situation, body-worn sensors typical of wearable systems are not a good option. Environmental systems, particularly visual-based ones with cameras deployed in the patient’s environment, could be the ideal option for this scenario. However, it is necessary to work with far-infrared (FIR) images in the low-light conditions of this environment. This work develops and implements, for the first time, a fall detection system that works with FIR imagery. The system integrates the output of a human pose estimation neural network with a detection methodology which uses the relative movement of the body’s most important joints in order to determine whether a fall has taken place. The pose estimation neural networks used represent the most relevant architectures in this field and have been trained using the first large public labeled FIR dataset. Thus, we have developed the first vision-based fall detection system working on FIR imagery able to operate in conditions of absolute darkness whose performance indexes are equivalent to the ones of equivalent systems working on conventional RGB images.

In past years, Parallel Sequence Spread Spectrum (PSSS) has attracted significant attention as a modulation technique for wireless communication systems targeting data rates of 100 Gb/s and beyond. PSSS allows designing high-speed baseband processors, which can be partially implemented in the analog domain. It uses multiple analog-to-digital converters (ADCs) to sample the received baseband signal in parallel, significantly relaxing the sampling rate and ADC complexity. However, due to the sidelobe effects of bipolar $m$ -sequences, PSSS shows lower performance than standard digital modulation schemes. This paper proposes real-valued PSSS spreading sequences with attenuated autocorrelation sidelobes. Such sequences show excellent bit error rate (BER) performance. Moreover, our sequences do not have length restrictions of 2m – 1, like in the case of $m$ -sequences, and reduce the chip area required to implement PSSS transceiver. The proposed sequences also reduce the peak-to-average power ratio (PAPR) of PSSS.

The COVID-19 pandemic has had severe consequences on the global economy, mainly due to indiscriminate geographical lockdowns. Moreover, the digital tracking tools developed to survey the spread of the virus have generated serious privacy concerns. In this paper, we present an algorithm that adaptively groups individuals according to their social contacts and their risk level of severe illness from COVID-19, instead of geographical criteria. The algorithm is fully distributed and therefore, individuals do not know any information about the group they belong to. Thus, we present a distributed clustering algorithm for adaptive pandemic control.