não financiado

PIDI/CISeD/2021/001 • Industrial Augmented Reality (IAR)

[DDSS] Digital Decision Support Systems, concluído, interno, nacional, não competitivo, não financiado, viseu /

Principal Researcher:
José Silva
Duration: 2021 – 2023

Cised team members
Olga Contente
Serafim Oliveira
Daniel Gaspar

The constant changes in the contextual environment and the continuous globalization cause changes in the market, which push the companies to look for new technologies to increase productivity and profitability. In addition, customer requirements force companies to make their production systems more flexible, efficient and competitive.

It is due to these changes that the need arises to implement interconnected systems, integrated into an industrial IoT platform (IIoT - Industrial Internet of Things), supported by the connection of equipment and production systems so that companies can create networks along the value chain and thus control and command processes independently and in real-time.

The implementation in organizations of intelligent and interconnected ecosystems allows making decisions in a decentralized way. However, it is necessary to provide tools to assist in decision-making, monitoring operations, and reducing errors by the operator.
This project aims to respond to this challenge with the application of solutions developed in augmented reality (AR) in an industrial environment.

The integration of AR in a production system allows to reduce production errors and increase the efficiency of product development since there is constant monitoring during the performance of tasks.

This project is based on three fundamental objectives.
One of the objectives of this project is the implementation of solutions developed in AR, in various industrial applications, namely, in carrying out maintenance interventions (inspection with remote support or access to instructions in virtual windows), in guiding employees through procedures, either be it the first time or a recurring task, using holographic step-by-step instructions and issuing alerts to users with the details necessary for the execution of operations.
The other objective is the integration of the AR system with an existing IIoT platform, which will allow obtaining and viewing in real-time the desired information for each equipment, such as the operating temperature, energy consumption, or the condition of the components. that integrate the equipment.
It is also the objective of this project to develop the best visualization solution taking into account the particularities of each application so that the interaction with the projections is fluid and with reduced latency.
Digital objects, from industrial equipment, will be developed using programming algorithms and spatial digitization for referencing the anchoring points of the real space where these objects are immersed.

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PROJ/IPV/ID&I/002 • Sistema Inteligente de Controlo de Planos Alimentares para Doentes de Alzheimer e Outras Demências

[CDS] Connected Digital Society, concluído, interno, não competitivo, não financiado, viseu /

Principal Researcher: Carlos Augusto Cunha
Duration: 2020 – 2022

Cised team members
Valter Alves
Rui Pedro Duarte

Funding
CGD; PV

Alzheimer's disease is a progressive loss of mental function, characterized by degeneration of brain tissue, including loss of nerve cells, accumulation of an abnormal protein, and development of neurofibrillary braids. Alzheimer's patients become dependent on other people, even for the most basic tasks. Controlling feeding and hydrating an Alzheimer's patient is thus a crucial task performed by the person who supports their daily routine, called Informal Caregiver (IC).

Conditions of malnutrition, super nutrition, and dehydration are common in people with diseases causing dementia since the loss of their autonomy also manifests itself in the level of their inability to demonstrate food needs. The support of a nutritionist in the preparation and follow-up of a food plan aligned with the needs of the patient is, therefore, fundamental. The monitoring of the food plan is undoubtedly a process that demands from Cl a lot of discipline and the ability to deal with possible circumstantial adaptations, such as replacing foods prescribed in the food plan with other equivalents or changing the quantity of water consumed as a function of ambient temperature.

This project addresses the problem of the creation and monitoring of diet plans in patients with dementia such as Alzheimer's, through the creation of a computer solution. It allows the creation of nutritional plans by the nutritionists using a Web application and the follow-up of these plans by the ICs through an app to significantly increase the quality of life of the patient. The mobile app will be able to send to the CI notifications regarding proper nutrition and hydration in due moments, accompanied by the control of hydration using an intelligent water bottle, will allow ensuring the compliance of the indications of the nutritionist. Besides, the application will suggest alternatives to plan foods if they are unavailable. Another central feature of the solution is the dynamic adaptation of water administration to the patient as a function of the environmental conditions automatically collected by temperature and humidity sensors.

In scientific terms, the project includes the creation of innovative models for adapting food plans using machine learning algorithms and integration approaches, preprocessing and evaluation of data quality collected by external sensors.

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PROJ/IPV/ID&I/007 • Sistema Inteligente de Controlo de Planos Alimentares para o Desporto

[CDS] Connected Digital Society, concluído, interno, nacional, não competitivo, não financiado, viseu /

Principal Researcher:
Carlos Cunha
Duration: 2019 – 2022

Cised team members
Rui Pedro Duarte
Valter Alves

Funding
CGD; PV

The recent growth in the pursuit of sporting activities, motivated by a widespread increase in the perception of the importance of maintaining physical fitness, campaigns specifically aimed at combating physical inactivity, and opportunities created by the revelation of lesser known modalities, brought forward fundamental questions such as the correct nutrition of the practitioners. Numerous institutions involved in the practice of physical activity, which have since flourished, have been integrating these concerns into their scope, including through nutritionists.

For a nutritionist, the elaboration and monitoring of a food plan in line with the needs of the individual present two critical problems: 1) obtaining biometric data, eating habits and energy consumption to create the food plan, and 2) monitoring and the dynamic adaptation of the food plan.

Sports nutrition is one of the most complex areas of nutrition, since it requires observation of a rather comprehensive set of metrics, encompassing the athlete's physical aspects, physical activity, and eating habits. The use of measuring devices for specific parameters of physical activity represents a common practice among athletes. The integration of data automatically collected by these devices with other data not directly observable, such as dietary habits and subjective metrics, is part of the complexity of creating a global register that can be used by the nutritionist during the development of the plan to feed. Also, at a stage after the preparation of the food plan, the need to adapt it may arise. For example, variations in temperature or physical intensity may involve quick changes in the energy or hydration needs of an individual. In such situations, the data collected by the devices could be used to dynamically adjust the plan and send alerts, informing the sportsman of the need to eat food or water at the right time.

The objectives of this project include the creation of (a) a Web application where the nutritionist can record and follow up on dietary plans and (b) a mobile app for sportspersons, which can collect data provided by smart devices or manually entered, and where they can plan and receive notifications. In scientific terms, the project includes the creation of innovative models for adapting food plans using machine learning algorithms and integration approaches, preprocessing and evaluation of data quality collected by external devices.

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