According to the world report in 2019 about vision health presented by the World Health Organization (WHO), there are 2.2 billion people in the world with some kind of visual impairment. Furthermore, in a recent National Health Interview Survey report, 25.5 million adult Americans 18 and older reported experiencing vision loss. Of these 25.5 million American adults, 15.3 million women and 10.1 million men report experiencing significant vision loss. As a result, this population is constantly at risk of a fall and its consequences. This paper presents the first module of our fall prevention system for visually impaired people. The module corresponds to a system of floor and path recognition in outdoor environments. At the core of the system is a Mask R-CNN trained with around 40.000 outdoor images. The proposed approach has reached a performance in a combination of training and testing of 92%, using a combined set of indoor and outdoor environment images.

This paper presents the development of a tool that allows people to communicate, by using only their voluntary eyes blinks. This tool provides a communication link mainly for people with motor disabilities, who cannot communicate through voice. the electroencephalographic (EEG) signal provided by the Mindwave Mobile 2 headset is the source of information to detect the voluntary blinks. In order to perform the digital processing of the EEG signal, a Long-Short Term Memory (LSTM) neural network was implemented, for the classification of the EEG signal in one of 5 possible classes: No blink, One blink, Two blinks, Three blinks and Other. the results of the trained model achieved an average accuracy of 92%. the neural network was embedded in a native Android mobile application, that shows a virtual keyboard consisting of the 27 letters of the Spanish alphabet, plus three characters, that are the commands “delete”, “space”, and “enter”. Each character can be selected by the user only through a determined number of voluntary blinks executed at certain times. When the user types a word and selects the “enter” command, the word is converted to audio using a text-to-speech engine. Finally, to verify the performance of the application, an experiment with eight people was executed, and as result the system achieved an average spelling precision of 91,26%.

We tackle the problem of multiple quadrotors transporting a cable-suspended point-mass load. The quadrotors are treated as a virtual leader-follower algorithm, where a multi-layer graph encapsulates the communication and physical interaction. On the one hand, the communication stands for the approach of following the reference trajectory of a virtual leader. On the other hand, the load exerts a distributed tension force on each cable which is modeled as the well-known spring-damping system to each quadrotor establishing an interconnected dynamic. We assume cables are stretchable and have neglectable mass. Both objectives are accomplished through a Model Reference Adaptive Control approach with a robust modification that treats uncertainties and perturbations given by error in parameters, noise in the signal, and the wind drag forces. We prove stability based on Lyapunov approach and the results are shown through simulation.

Monitoring environmental variables in lower layers of the atmosphere is an important activity to measure changes that result from natural events, and human interventions. Volcano eruptions, commercial aviation, and the massive spread of pesticides using light aircraft are just some examples of low layer atmosphere polluters. Twice a day, every day of the year, weather balloons are released simultaneously from almost 900 locations worldwide to monitor environmental variables. The flight of these synthetic rubber balloons last for around 2 hours, then they become pollution too. Recent advances in small unmanned aerial vehicles (UAVs) with built in sensors, and their emerging role in business supply chain make UAVs ideal participants for environmental monitoring. In this paper, we present an incentive mechanism for UAV-Crowdsensing. The core of the proposed mechanism consists of a recurrent reverse action and a recruitment model. By these two components, the system encourages UAVs sensing from locations that maximize volume coverage within a given budget. Through extensive simulations, we evaluate the performance of the proposed incentive mechanism.

A recent study showed that around 77% of the U.S. population experiences regularly physiological symptoms of stress such as sleep deprivation, fatigue, and migraines, and 73% psychological symptoms such anxiety, angry, and lack of focus. Thus, psychological stress remains an important issue of public health. In this paper, propose a mechanism for the selection of stress interventions based on fuzzy control theory. Here, we model the user as a dynamical system, in particular, we use the well-known analogy of a fluid reservoir or tank to model the user's accumulation of stress. In this model, inputs correspond to stressors which increase the level of fluid and the outputs correspond to interventions which release stress and drain the tank. Thus, we tackle the problem keeping a user's healthy stress level by using a fuzzy logic approach. Through several examples and extensive simulations we evaluate the performance of our proposed mechanism for the selection of stress interventions.

An important area of research in humanoid robots is energy consumption, as it limits autonomy, and can harm task performance. This work focuses on power aware motion planning. Its principal aim is to find joint trajectories to allow for a humanoid robot to go from crouch to stand position while minimizing power consumption. Q-Learning (QL) is used to search for optimal joint paths subject to angular position and torque restrictions. A planar model of the humanoid is used, which interacts with QL during a simulated offline learning phase. The best joint trajectories found during learning are then executed by a physical humanoid robot, the Aldebaran NAO. Position, velocity, acceleration, and current of the humanoid system are measured to evaluate energy, mechanical power, and Center of Mass (CoM) in order to estimate the performance of the new trajectory which yield a considerable reduction in power consumption.