Design of a Healthcare Monitoring and Communication System for Locked-In Patients Using Machine Learning, IOTs, and Brain-Computer Interface Technologies

Machine learning (ML) models have shown great promise in advancing brain-computer interface (BCI) signal processing and in enhancing the capabilities of Internet of Things (IoT) mobile devices. By combining these advancements into a comprehensive healthcare monitoring and communication system, we may significantly improve the quality of life for patients living with locked-in syndrome. To that effect, we present a three-tiered approach to systems design using known ML models: data collection, local integrated system deployed on IoT hardware, and administrative management. The first tier focuses on IoT sensors and non-invasive recording of brain signals, their calibration and data collection, and data processing. The second tier focuses on aggregating and directing the data, an alert system for caregivers, and a BCI for personalized communication. The last tier focuses on accountability and essential management tools. This research-in-progress demonstrates the feasibility of integrating current technologies to improve care for locked-in patients

Dynamic knowledge model evolution in SWoT: a way to improve services selection relevancy over time

Semantic web technologies are gaining momentum in the WoT (Web of Things) community for its promising ability to manage the increasing semantic heterogeneity between devices (Semantic Web of Things, SWoT) in ambient environments. However, most of the approaches rely on ad-hoc and static knowledge models (ontologies) designed for specific domains and applications. While it is a solution for handling the semantic heterogeneity issue, it offers no perspective in term of ontology evolution over time. We study in this paper several approaches allowing: (1) to handle the semantic heterogeneity issue; (2) to capitalize the knowledge contributions throughout the life of the system allowing it to potentially better assist people in their environment over time. One of the approaches is validated on two real use-cases

Usages of Semantic Web Services Technologies in IoT Ecosystems and its Impact in Services Delivery: A survey

Internet of things (IoT) has begun to emerge in our daily life through the huge number of smart services provided by the devices that deploy around us.  Vague and uncertainty in attributes that using in describing services, different levels of quality of each service and the limitation in capabilities of IoT devices are affect and hinder the process of discovering or selecting services.   The services in IoT need to be well described to enable users to receive their services that relevant to their query. This survey will investigate the most popular semantic services models and explore the use of these models in enhancing services discovery and services selection in IoT domain. Furthermore, the survey will investigate the evaluation metrics used by each study and compare the results that they obtained.&nbsp

Sistema colaborativo de medición de parámetros ambientales basado en IoT

International audienceLas alertas ambientales declaradas en Bogotá, los informes mundiales sobre la situación actual del medio ambiente y los efectos de la contaminación sobre la salud de las personas son las principales causas por las que el grupo de investigación GISSIC (Grupo de Investigación en Seguridad y Sistemas de Comunicación) decidiera elaborar un proyecto que pudiera entregar una solución tecnológica alternativa, en tiempo real y a una escala importante.El despliegue de nodos IoT capaces de realizar las medidas de las variables contaminantes que se encuentran suspendidas en el aire se realizará inicialmente en el campus Nueva Granada que se encuentra ubicado en Cajicá y posteriormente a las demás sedes de la universidad Militar Nueva Granada. Los datos recolectados serán enviados a una interfaz de usuario en la que se podrá visualizar el comportamiento de las variables seleccionadas. Estos datos serán públicos para que la comunidad tenga acceso a esta información y pintos de voluntariado podrán acrecentar la red para hacerla colaborativa.La arquitectura que se está desarrollando se compone por sensores distribuidos y una plataforma escalable con capacidades de administración y gestión remota, así como procesamiento en edge computing. La plataforma tendrá la capacidad de integrarse con la plataforma de administración de servicios que se está implementando en el WIRID LAB y las plataformas de Future Internet of Things de INSA en Francia

Architectures of Energy Harvesting Systems

This article develops the literary review of the architectures of energy harvesting systems, identifying parameters such as: frequency, type of antenna, architecture (elements), among others. The methodology has four stages: a) Search for documentation, 10 systems were obtained, which were designated with the letter "S" accompanied by the article number; b) Reading of scientific documents; c) Extraction of information and architecture of the systems, where the stages of each system are detailed (which vary from 2 to 4), the working frequencies (300 KHz to 3.43 GHz, 2.45 GHz being the most used in systems for collecting radio frequency energy). In addition to using in certain systems, multiplier and rectifier circuits in different configurations: half wave, full wave; to later be stored in batteries or directly applied to devices; d) Documentation of the information extracted. Finally, after completing the literary review, it was observed that, in most articles, the systems have 3 stages: antenna, coupling, and rectification that transforms the received energy (alternating current) into direct current, their operation varies in frequency intervals of 1.8 to 2.4 GHz depending on the configuration of each system. Likewise, the product obtained is a consultation APP, with a selection menu of the different architectures investigated, which is a very beneficial contribution for researchers who wish to work in this area

Dynamic service orchestration in heterogeneous internet of things environments

Internet of Things (IoT) presents a dynamic global revolution in the Internet where physical and virtual “things” will communicate and share information. As the number of devices increases, there is a need for a plug-and–interoperate approach of deploying “things” to the existing network with less or no human need for configuration. The plug-and-interoperate approach allows heterogeneous “things” to seamlessly interoperate, interact and exchange information and subsequently share services. Services are represented as functionalities that are offered by the “things”. Service orchestration provides an approach to integration and interoperability that decouples applications from each other, enhancing capabilities to centrally manage and monitor components. This work investigated requirements for semantic interoperability and exposed current challenges in IoT interoperability as a means of facilitating services orchestration in IoT. The research proposes a platform that allows heterogeneous devices to collaborate thereby enabling dynamic service orchestration. The platform provides a common framework for representing semantics allowing for a consistent information exchange format. The information is stored and presented in an ontology thereby preserving semantics and making the information comprehensible to machines allowing for automated addressing, tracking and discovery as well as information representation, storage, and exchange. Process mining techniques were used to discover service orchestrations. Process mining techniques enabled the analysis of runtime behavior of service orchestrations and the semantic breakdown of the service request and creation in real time. This enabled the research to draw observations that led to conclusions presented in this work. The research noted that the use of semantic technologies facilitates interoperability in heterogeneous devices and can be implemented as a means to bypass challenges presented by differences in IoT “things”

Radar sensing for ambient assisted living application with artificial intelligence

In a time characterized by rapid technological advancements and a noticeable trend towards an older average population, the need for automated systems to monitor movements and actions has become increasingly important. This thesis delves into the application of radar, specifically Frequency Modulated Continuous Wave (FMCW) radar, as an emerging and effective sensor in the field of "Activity Recognition." This area involves capturing motion data through sensors and integrating it with machine learning algorithms to autonomously classify human activities. Radar is distinguished by its ability to accurately track complex bodily movements while ensuring privacy compliance. The research provides an in-depth examination of FMCW radar, detailing its operational principles and exploring radar information domains such as range-time and micro-Doppler signatures. Following this, the thesis presents a state-of-the-art review in activity recognition, discussing key papers and significant works that have shaped the field. The thesis then focuses on research topics where contributions were made. The first topic is human activity recognition (HAR) with different physiology, presenting a comprehensive experimental setup with radar sensors to capture various human activities. The analysis of classification results reveals the effectiveness of different radar representations. Advancing into the domain of resource-constrained system platforms. It introduces adaptive thresholding for efficient data processing and discusses the optimization of these methods using artificial intelligence, particularly focusing on the evolution algorithm such as Self-Adaptive Differential Evolution Algorithm (SADEA). The final chapter discusses the use of Long Short-Term Memory (LSTM) networks for short-range personnel recognition using radar signals. It details the training and testing methodologies and provides an analysis of LSTM networks performance in temporal classification tasks. Overall, this thesis demonstrates the effectiveness of merging radar technology with machine learning in HAR, particularly in assisted living. It contributes to the field by introducing methods optimized for resource-limited settings and innovative approaches in temporal classification using LSTM networks