In this paper, we present a fish-like underwater robot inspired by the black bass fish. This robot is composed of a deformable structure and muscle-like linear actuators based on shape memory alloy wires. Such actuators are used to bend a continuous structure representing the backbone of the fish. The prototype is also equipped with a bio-inspired synthetic skin made of liquid silicone rubber and Lycra microfiber mesh. We present the mechatronics of the prototype and its control scheme, which take advantage of flex sensors for proprioception. Experiments under different conditions (in air and in water) assess the effectiveness of the mechatronics design and demonstrate that a relatively simple PID controller provides high precision of the muscles' position control. Here, the implementation of biomimetic kinematics and silent actuation technology in bio-inspired underwater robotics are demonstrated. Progress in this technology could provide multiple applications, including fish farming, coastal protection and live animal monitoring where silent robotics are necessary.

This paper presents a novel system based on the Internet of Things (IoT) to Human Activity Recognition (HAR) by monitoring vital signs remotely. We use machine learning algorithms to determine the activity done within four pre-established categories (lie, sit, walk and jog). Meanwhile, it is able to give feedback during and after the activity is performed, using a remote monitoring component with remote visualization and programmable alarms. This system was successfully implemented with a 95.83% success ratio.

Animal collective behavior is often modeled with self-propelled particles, assuming each individual has “omniscient” knowledge of its neighbors. Yet, neighbors may be hidden from view and we do not know the effect of this information loss. To address this question, we propose a visual model of collective behavior where each particle moves according to bioplausible visual cues, in particular the optic flow. This visual model successfully reproduces three classical collective behaviors: swarming, schooling, and milling. This model offers a potential solution for controlling artificial swarms visually. Published by the American Physical Society 2024

El internet de las cosas, también conocido como IoT por sus siglas en inglés (Internet of Things), es un nuevo paradigma tecnológico que permite la interconexión de objetos (las cosas), servicios y/o personas por medio de internet. IoT introduce nuevas herramientas que enriquecen el trabajo en diferentes sectores industriales. Este artículo presenta un estudio del trabajo desarrollado en todo el mundo en tecnologías con base en el concepto de IoT en el marco de la industria del cuidado de la salud, incluyendo métodos, estadísticas y casos de éxito. En la última sección, se discute sobre el trabajo realizado en este ámbito en el contexto colombiano, incluyendo aplicaciones exitosas y proyectos que se encuentran en desarrollo.

The implementation of collective motion, traditionally, disregard the limited sensing capabilities of an individual, to instead assuming an omniscient perception of the environment. This study implements a visual flocking model in a ‘robot-in-the-loop’ approach to reproduce these behaviors with a flock composed of 10 independent spherical robots. The model achieves robotic collective motion by only using panoramic visual information of each robot, such as retinal position, optical size and optic flow of the neighboring robots. We introduce a virtual anchor to confine the collective robotic movements so to avoid wall interactions. For the first time, a simple visual robot-in-the-loop approach succeed in reproducing several collective motion phases, in particular, swarming, and milling. Another milestone achieved with by this model is bridging the gap between simulation and physical experiments by demonstrating nearly identical behaviors in both environments with the same visual model. To conclude, we show that our minimal visual collective motion model is sufficient to recreate most collective behaviors on a robot-in-the-loop system that be implemented using several individuals, behaves as numerical simulations predict and is easily comparable to traditional models.