Mobile computing technologies for health and mobility assessment: research design and results of the ttmed up and go test in older adults

Due to the increasing age of the European population, there is a growing interest in performing research that will aid in the timely and unobtrusive detection of emerging diseases. For such tasks, mobile devices have several sensors, facilitating the acquisition of diverse data. This study focuses on the analysis of the data collected from the mobile devices sensors and a pressure sensor connected to a Bitalino device for the measurement of the Timed-Up and Go test. The data acquisition was performed within different environments from multiple individuals with distinct types of diseases. Then this data was analyzed to estimate the various parameters of the Timed-Up and Go test. Firstly, the pressure sensor is used to extract the reaction and total test time. Secondly, the magnetometer sensors are used to identify the total test time and different parameters related to turning around. Finally, the accelerometer sensor is used to extract the reaction time, total test time, duration of turning around, going time, return time, and many other derived metrics. Our experiments showed that these parameters could be automatically and reliably detected with a mobile device. Moreover, we identified that the time to perform the Timed-Up and Go test increases with age and the presence of diseases related to locomotion.info:eu-repo/semantics/publishedVersio

Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs’ identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN).This work is funded by FCT/MCTES through national funds, and when applicable, co-funded EU funds under the project UIDB/EEA/50008/2020 (Este trabalho é financiado pela FCT/MCTES através de fundos nacionais e quando aplicável cofinanciado por fundos comunitários no âmbito do projeto UIDB/EEA/50008/2020)

Android Library for Recognition of Activities of Daily Living: Implementation Considerations, Challenges, and Solutions

Background: Off-the-shelf-mobile devices have several sensors available onboard that may be used for the recognition of Activities of Daily Living (ADL) and the environments where they are performed. This research is focused on the development of Ambient Assisted Living (AAL) systems, using mobile devices for the acquisition of the different types of data related to the physical and physiological conditions of the subjects and the environments. Mobile devices with the Android Operating Systems are the least expensive and exhibit the biggest market while providing a variety of models and onboard sensors. Objective: This paper describes the implementation considerations, challenges and solutions about a framework for the recognition of ADL and the environments, provided as an Android library. The framework is a function of the number of sensors available in different mobile devices and utilizes a variety of activity recognition algorithms to provide a rapid feedback to the user. Methods: The Android library includes data fusion, data processing, features engineering and classification methods. The sensors that may be used are the accelerometer, the gyroscope, the magnetometer, the Global Positioning System (GPS) receiver and the microphone. The data processing includes the application of data cleaning methods and the extraction of features, which are used with Deep Neural Networks (DNN) for the classification of ADL and environment. Throughout this work, the limitations of the mobile devices were explored and their effects have been minimized. Results: The implementation of the Android library reported an overall accuracy between 58.02% and 89.15%, depending on the number of sensors used and the number of ADL and environments recognized. Compared with the results available in the literature, the performance of the library reported a mean improvement of 2.93%, and they do not differ at the maximum found in prior work, that based on the Student’s t-test. Conclusion: This study proves that ADL like walking, going upstairs and downstairs, running, watching TV, driving, sleeping and standing activities, and the bedroom, cooking/kitchen, gym, classroom, hall, living room, bar, library and street environments may be recognized with the sensors available in off-the-shelf mobile devices. Finally, these results may act as a preliminary research for the development of a personal digital life coach with a multi-sensor mobile device commonly used daily.This work was supported by FCT project UID/EEA/50008/2013 (Este trabalho foi suportado pelo projecto FCT UID/EEA/50008/2013). The authors would also like to acknowledge the contribution of the COST Action IC1303 – AAPELE – Architectures, Algorithms and Protocols for Enhanced Living Environments

Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices

The identification of Activities of Daily Living (ADL) is intrinsic with the user’s environment recognition. This detection can be executed through standard sensors present in every-day mobile devices. On the one hand, the main proposal is to recognize users’ environment and standing activities. On the other hand, these features are included in a framework for the ADL and environment identification. Therefore, this paper is divided into two parts—firstly, acoustic sensors are used for the collection of data towards the recognition of the environment and, secondly, the information of the environment recognized is fused with the information gathered by motion and magnetic sensors. The environment and ADL recognition are performed by pattern recognition techniques that aim for the development of a system, including data collection, processing, fusion and classification procedures. These classification techniques include distinctive types of Artificial Neural Networks (ANN), analyzing various implementations of ANN and choosing the most suitable for further inclusion in the following different stages of the developed system. The results present 85.89% accuracy using Deep Neural Networks (DNN) with normalized data for the ADL recognition and 86.50% accuracy using Feedforward Neural Networks (FNN) with non-normalized data for environment recognition. Furthermore, the tests conducted present 100% accuracy for standing activities recognition using DNN with normalized data, which is the most suited for the intended purpose.This work is funded by FCT/MEC through national funds and co-funded by FEDER-PT2020 partnership agreement under the project UID/EEA/50008/2019

Estudo de conservação sob atmosfera controlada na qualidade da cereja cv. Satin

A cereja é muito apreciada e apresenta um tempo de comercialização muito curto devido a ser um fruto altamente perecível. Técnicas de conservação pós-colheita são essenciais para manter a qualidade da cereja até serem consumidas. Baixas temperaturas são utilizadas para retardar o processo de deterioração da fruta e como complemento a aplicação de atmosferas controladas permite retardar o processo de amadurecimento e envelhecimento. A diminuição de oxigénio e o aumento de dióxido de carbono e azoto inibe o amadurecimento, mantendo o sabor e a qualidade da fruta. Neste trabalho experimental, cerejas da cultivar Satin foram conservadas em câmaras de refrigeração no produtor e nas instalações do CATAA com equipamento de atmosferas controladas. Quatro atmosferas controladas com diferentes combinações de oxigénio e dióxido de carbono foram testadas e o seu efeito na qualidade das cerejas foi avaliado. Ao longo do tempo de conservação as cerejas foram analisadas a diferentes níveis: qualidade (peso, dureza, cor e sólidos solúveis totais), microbiológico e organolético. Os resultados de temperatura e humidade no produtor e no CATAA, foram comparados e indicam que ambas as situações apresentam ótimas condições de conservação. No entanto, complementar a conservação com atmosferas controladas sugere que a qualidade da cereja é mantida por mais tempo, através da minimização do envelhecimento e processo de amadurecimento.info:eu-repo/semantics/publishedVersio

Estudo de conservação sob atmosfera controlada na qualidade da cereja cv. Satin.

A cerejeira (Prunus avium L.) é uma espécie pertencente à subfamília das Prunóideas e a produção de cereja apresenta elevada importância económica na região da Beira Interior, que, embora não seja a região com maior área de produção é a principal região de produção de Portugal. A cereja apresenta um elevado teor de compostos bioativos como vitamina C, fibra, antocianinas, quercetina e carotenóides relacionados com a prevenção de doenças cardiovasculares, diabetes e cancro (McCune et al., 2011; Wang et al., 2016). No entanto, este fruto não climatérico deteriora-se rapidamente após a colheita apresentando alterações na cor da pele, acastanhamento do pedúnculo, desidratação, amolecimento da polpa, diminuição da acidez e apodrecimento (Dugan & Roberts, 1997; Wang et al., 2016). A refrigeração, combinada com a utilização de atmosferas controladas, visa o atraso da deterioração e o consequente prolongamento da vida útil alargando o período de oferta. Esta técnica consiste no armazenamento a baixa temperatura num ambiente com uma concentração elevada de CO2, uma concentração baixa de O2 e uma humidade relativa elevada (Andrade et al., 2019). Os valores indicados na bibliografia relativos à concentração de CO2 variam entre 5% e 20% (Gross et al., 2016) e, para a concentração de O2, encontram-se entre 1% (Gross et al., 2016) e 10% (Ben-Yehoshua et al., 2005)info:eu-repo/semantics/publishedVersio

Modelos de programação linear inteira mista para o planeamento da produção de uma empresa de tintas

Dissertação apresentada ao Departamento de Estatística e Investigação Operacional da Faculdade de Ciências da Universidade de Lisboa para obtenção do Grau de Mestre em Investigação Operacional.Os Problemas de Sequenciamento ocorrem num grande leque de indústrias, incluindo as de engenharia química. A produção em lotes (batch production) é, desde há muito, o procedimento adoptado na manufactura de diversos tipos de produtos químicos, em particular naqueles em que a produção é feita em pequenas quantidades e para a qual os padrões de procura estão sempre a variar. Nesta tese é estudado o Problema de Planeamento e Sequenciamento de tarefas numa planta multi-uso/multi-produto, com o objectivo de conseguir encontrar soluções para um problema real de produção de tintas numa determinada empresa. É usado o procedimento de produção em lotes, uma vez que a tinta, dadas as suas características, não pode ser produzida através de um processo contínuo. Numa primeira fase, é construído um modelo de planeamento da produção que consiste em fazer a afectação de produtos a máquinas, de modo a satisfazer os pedidos diários dos clientes, sempre com o objectivo principal de minimizar ao máximo as encomendas entregues com atraso, sendo o objectivo secundário a minimização das quantidades enviadas para stock. O modelo de planeamento é modelado em programação linear inteira mista, onde as variáveis inteiras servem para indicar os lotes de produtos que são produzidos, assim como em que máquina. De seguida, perante a solução obtida através do planeamento, o modelo de sequenciamento das tarefas pretende encontrar uma ordem de produção para um horizonte temporal de um dia. Foi também utilizado um modelo de programação linear inteira com variáveis binárias apenas, onde estas servem para indicar o início das tarefas. Numa fase posterior, depois de efectuados alguns testes aos modelos com instâncias reais do problema de diferentes dimensões, houve necessidade de proceder a algumas modificações nas instâncias e/ou nos modelos de forma a conseguir encontrar uma solução considerada “boa” para a empresa. Resultados numéricos extensivos dos problemas descritos anteriormente são apresentados para todas as variantes desenvolvidas durante o trabalho, para ilustrar as performances das modificações introduzidas nos modelos propostos. Conseguem obter-se soluções razoáveis quando se diminui a carga de produção no modelo de planeamento, tendo-se como consequência um ganho em termos de tempo para fazer o sequenciamento. Minimizar o tempo total de ocupação das máquinas no modelo de sequenciamento revelou-se eficiente, em conjugação com o critério anterior, mas existe uma grande limitação na passagem do modelo de planeamento para o de sequenciamento, que consiste na afectação prévia dos produtos às máquinas de um modelo para o outro. Introduzir alterações no modelo de planeamento, na recolha de dados e eventualmente no esquema de trabalho da referida empresa, podem ser caminhos a seguir na tentativa de encontrar o óptimo.Fundo Social Europeu - Programa PRODEP II

An overview of maritime pine private non-industrial forest in the centre of Portugal: A 19-year case study

Portuguese national policies for forests were developed considering related themes such as climate change, forest health, fire and the protective functions of forests. In Portugal, maritime pine forest is mainly private non-industrial and its area is in decline. Therefore, the aim of this study was two-fold: first, to assess maritime pine forest characteristics over a 19-year period; second, to analyse forest cover change over that period. In the end, the implementation of state policies was explored. A study area highly forested by continuous areas of naturally regenerated maritime pine in the centre of Portugal was used. To assess maritime pine forest characteristics, two sets of inventory data collected in previous studies (1991-1996 and 2007-2010) were used. To analyse forest cover change, the official land cover maps for 1990 and 2007 were used. This study findings highlighted that study area’s trends over the past years were the following: first, the decrease of maritime pine forest areas and its management decline (stands less stable, under-stocked, with large amounts of small-diameter poles and enlarged tree size variability); second, the increase of scrubland areas; third, the increase of eucalyptus afforestation with no regard for protection areas; and fourth, the absence of native oaks or introduction of other broadleaves as recommended by the state policies. Therefore, it is argued that there is a need for effective field monitoring actions with regard to the implementation of state policies. Additionally, selective incentives are key to mobilise private non-industrial forest to achieve the goals of state forest policies

Identification of activities of daily living through data fusion on motion and magnetic sensors embedded on mobile devices

Several types of sensors have been available in off-the-shelf mobile devices, including motion, magnetic, vision, acoustic, and location sensors. This paper focuses on the fusion of the data acquired from motion and magnetic sensors, i.e., accelerometer, gyroscope and magnetometer sensors, for the recognition of Activities of Daily Living (ADL). Based on pattern recognition techniques, the system developed in this study includes data acquisition, data processing, data fusion, and classification methods like Artificial Neural Networks (ANN). Multiple settings of the ANN were implemented and evaluated in which the best accuracy obtained, with Deep Neural Networks (DNN), was 89.51%. This novel approach applies L2 regularization and normalization techniques on the sensors’ data proved it suitability and reliability for the ADL recognition.This work was supported by FCT project UID/EEA/50008/2013 (Este trabalho foi suportado pelo projecto FCT UID/EEA/50008/2013). The authors would also like to acknowledge the contribution of the COST Action IC1303 – AAPELE – Architectures, Algorithms and Protocols for Enhanced Living Environments