On the road to making self-driving cars a reality, academic and industrial researchers are working hard to continue to increase safety while meeting technical and regulatory constraints Understanding the surrounding environment is a fundamental task in self-driving cars. It requires combining complex computer vision algorithms. Although state-of-the-art algorithms achieve good accuracy, their implementations often require powerful computing platforms with high power consumption. In some cases, the processing speed does not meet real-time constraints. FPGA platforms are often used to implement a category of latency-critical algorithms that demand maximum performance and energy efficiency. Since self-driving car computer vision functions fall into this category, one could expect to see a wide adoption of FPGAs in autonomous cars. In this paper, we survey the computer vision FPGA-based works from the literature targeting automotive applications over the last decade. Based on the survey, we identify the strengths and weaknesses of FPGAs in this domain and future research opportunities and challenges.

Augmented reality is showing a continuous evolution due to the increasing number of smart glasses that are being used for different applications (e.g. training, marketing, industry, risk avoidance, etc.). In this paper, we present an implementation that uses augmented reality (AR) for emergency situations in smart buildings by means of indoor localization through the use of sub-GHz beacons. This also includes the mapping of emergency elements in the three-dimensional building, together with some example cases.

The Internet of Medical Things (IoMT) extends the concept of eHealth and mHealth for patients with continuous monitoring requirements. This research concentrates on the use of wearable devices based on the use of inertial measurement units (IMUs) that account for a gait analysis for its use in three health cases, equilibrium evaluation, fall prevention and surgery recovery, that impact a large elderly population. We also analyze two different scenarios for data capture: supervised by clinicians and unsupervised during activities of daily life (ADLs). The continuous monitoring of patients produces large amounts of data that are analyzed in specific IoMT platforms that must be connected to the health system platforms containing the health records of the patients. The aim of this study is to evaluate the factors that impact the cost of the deployment of such an IoMT solution. We use population data from Catalonia together with an IoMT deployment model for costs from the current deployment of connected devices for monitoring diabetic patients. Our study reveals the critical dependencies of the proposed IoMT platforms: from the devices and cloud cost, the size of the population using these services and the savings from the current model under key parameters such as fall reduction or rehabilitation duration. Future research should investigate the benefit of continuous monitoring in improving the quality of life of patients.

Constant monitoring of gait in real life conditions is considered the best way to assess Fall Risk Index (FRI) since most falls happen out of the ideal conditions in which clinicians are currently analyzing the patient's behavior. This paper presents the WIISEL platform and results obtained through the use of the first full-wireless insole devices that can measure almost all gait related data directly on the feet (not in the upper part of the body as most existing wearable solutions). The platform consists of a complete tool-chain: insoles, smartphone & app, server & analysis tool, FRI estimation and user access. Results are obtained by combining parameters in a personalized way to build individual fall risk index assessed by experts with the help of data analytics. New FRI has been compared with standards that validate the quality of its prediction in a statistically significant way. That qualitatively relevant information is being provided to the platform users, being either end-users/patients, relatives or caregivers and the related clinicians to ideally assess about their long term evolution.