Smart Footwear Insole for Recognition of Foot Pronation and Supination Using Neural Networks

Abnormal foot postures during gait are common sources of pain and pathologies of the lower limbs. Measurements of foot plantar pressures in both dynamic and static conditions can detect these abnormal foot postures and prevent possible pathologies. In this work, a plantar pressure measurement system is developed to identify areas with higher or lower pressure load. This system is composed of an embedded system placed in the insole and a user application. The instrumented insole consists of a low-power microcontroller, seven pressure sensors and a low-energy bluetooth module. The user application receives and shows the insole pressure information in real-time and, finally, provides information about the foot posture. In order to identify the different pressure states and obtain the final information of the study with greater accuracy, a Deep Learning neural network system has been integrated into the user application. The neural network can be trained using a stored dataset in order to obtain the classification results in real-time. Results prove that this system provides an accuracy over 90% using a training dataset of 3000+ steps from 6 different users.Ministerio de Economía y Competitividad TEC2016-77785-

Multi-agent and embedded system technologies applied to improve the management of power systems

This article explores a number of improvements made on Supervisory Control and Data Acquisition (SCADA) systems which allow them to be successfully used for automated surveillance. Even telecontrol operators who have limited experience with computers were able to employ the system without any difficulties. Other advances made by taking advantage of the strongest features of embedded and multi-agent system technologies are also featured in this article. These developments have been tested in a true industrial environment. Positive results and feedback have been provided by the tests

Simulation and Implementation of a Neural Network in a Multiagent System

This paper presents the simulation and the implementation of a model of a neural network applied to a multiagent system by using the Neuroph framework. This tool enables several tests to be carried out and verify which structure is the best structure of our neural network for a specific application. In our case, we simulated the neural network for a sun-tracking control system in a solar farm. Initial implementation shows good results in performance, thereby providing an alternative to traditional solar-tracking systems.Junta de Andalucía P08-TIC-03862 (CARISMA

Multiagent System powered by Neural Network for positioning control of solar panels: An optimization for sun tracking systems

This paper presents a model of neural network for position control of solar panels in multiagent-based control systems. This neural network is integrated within agents in order to optimize and predict the best positioning of solar panels depending on the position of the sun and other variables. The agents in this system can cooperate and coordinate to achieve a sun tracking system optimized, simple and adaptive.Junta de Andalucía P08-TIC-03862 (CARISMA

Multiple intelligences in a MultiAgent System applied to telecontrol

This paper presents a control system, based on artificial intelligence technologies, that implements multiple intelligences. This system aims to support and improve automatic telecontrol of solar power plants, by either automatically triggering actuators or dynamically giving recommendations to human operators. For this purpose, the development of a MultiAgent System is combined with a variety of inference systems, such as Expert Systems, Neural Networks, and Bayesian Networks. This diversity of intelligent technologies is shown to result in an efficient way to mimic the reasoning process in human operators.Junta de Andalucía P08-TIC-0386

PeMMAS: A Tool for Studying the Performance of Multiagent Systems Developed in JADE

This paper describes the performance measurement for multiagent systems (PeMMAS) tool, a system designed to study and measure the performance of any multiagent system (MAS) de-veloped in JADE. The tool itself is another MAS which is deployed and coexists alongside the one being studied. This characteristic allows us to adapt PeMMAS to any scenario in which MAS de-ployment in JADE is used. PeMMAS extracts information from the target MAS regarding the use of system resources, the flight time for comprehensive messages according to agent type, as well as the processing time for actions. After processing this information, PeMMAS sends a report to the final user for subsequent analysis

Aprendizaje basado en juegos online para la mejora de la adquisición de competencias en Patología Médica Bucal

Estamos viviendo una situación complicada debido a la pandemia por el COVID-19. Durante el presente curso académico parte de nuestras asignaturas han pasado de una docencia presencial a una docencia online, esto ha sucedido en la asignatura Patología Médica Bucal del grado en Odontología. Este paso de la presencialidad a la docencia online requiere de nuevas medidas de motivación para aumentar la adquisición de conocimientos y mantener el aprendizaje activo. El objetivo de este proyecto ha sido aplicar diferentes juegos online para mejorar el aprendizaje en Patología Médica Bucal y la transferencia de conocimiento entre alumnos y profesores

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries