Identification of diseases based on the use of inertial sensors: a systematic review

Inertial sensors are commonly embedded in several devices, including smartphones, and other specific devices. This type of sensors may be used for different purposes, including the recognition of different diseases. Several studies are focused on the use of accelerometer for the automatic recognition of different diseases, and it may powerful the different treatments with the use of less invasive and painful techniques for patients. This paper is focused in the systematic review of the studies available in the literature for the automatic recognition of different diseases with accelerometer sensors. The disease that is the most reliably detectable disease using accelerometer sensors, available in 54% of the analyzed studies, is the Parkinson’s disease. The machine learning methods implements for the recognition of Parkinson’s disease reported an accuracy of 94%. Other diseases are recognized in less number that will be subject of further analysis in the future.info:eu-repo/semantics/publishedVersio

Signal processing for the measurement of the results of the timed-up and go test using sensors

Dissertação de Mestrado apresentada à Escola Superior de Tecnologia do Instituto Politécnico de Castelo Branco para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Desenvolvimento de Software e Sistemas Interativos.Os recentes avanços tecnológicos e o crescente uso dos dispositivos móveis tem permitido o surgimento de vários estudos em diferentes áreas da vida humana. Estes dispositivos estão equipados com diversos sensores que permitem adquirir diferentes parâmetros físicos e fisiológicos de diferentes indivíduos. Os dispositivos móveis apresentam-se com cada vez mais soluções, funcionalidades e capacidade de processamento. A presença de sensores nos dispositivos móveis, como o acelerómetro, magnetómetro e giroscópio, permite a aquisição de sinais relacionados com atividade física e movimento do ser humano. Em acréscimo, dado que estes dispositivos incluem possibilidade de ligação via Bluetooth, outros sensores podem ser utilizados em conjunto com os sensores incluídos no dispositivo móvel. O desenvolvimento deste tipo de sistemas inteligentes com sensores é um dos temas abordados no desenvolvimento de sistemas de Ambient Assisted Living (AAL). Diversas áreas da medicina têm beneficiado com estes avanços, proporcionando cuidados de saúde à distância, mas o foco desta dissertação é um dos testes funcionais focados na fisioterapia, o Timed-Up and Go test. O Timed-Up and Go test define-se como um teste muito utilizado por fisioterapeutas na recuperação de lesões e é constituído por seis fases, onde o individuo se encontra sentado numa cadeira, levanta-se, caminha três metros, inverte a marcha, caminha três metros e volta a sentar-se na cadeira. O âmbito desta dissertação consiste na análise estatística e com inteligência artificial dos dados recolhidos durante a execução do Timed-Up and Go test com recurso a diversos sensores, sendo que para isso foi desenvolvida uma aplicação móvel que permite adquirir os dados de diversos sensores durante a execução do teste com pessoas idosas institucionalizadas. A dissertação foca-se na criação de um método de análise dos resultados do Timed-Up and Go test com recurso ao acelerómetro e magnetómetro do dispositivo móvel e um sensor de pressão, ligado a um dispositivo BITalino, posicionado na cadeira. Ao mesmo tempo, foram recolhidos sinais de sensores de Eletrocardiografia e Eletroencefalografia, conectados a outro dispositivo BITalino, para análise de diferentes problemas de saúde. Assim, implementaram-se métodos estatísticos e de inteligência artificial para a análise dos dados recolhidos a partir destes sensores com recurso ao procedimento experimental inicialmente executado. Inicialmente, foi realizada a revisão da literatura relacionada com o Timed- Up and Go test e o uso de sensores, sendo que a revisão de literatura terminou com a identificação das doenças passíveis de serem identificadas com recurso aos sensores inerciais. Seguidamente, apresentou-se a proposta de arquitetura a ser utilizada para a recolha dos dados, tendo em conta os sensores anteriormente referidos. Os dados presentes neste estudo foram recolhidos de 40 idosos institucionalizados da região do Fundão (Portugal), equipados com um dispositivo móvel e um dispositivo BITalino, bem como os restantes sensores. Por fim, passou-se então à análise dos dados recolhidos que foi dividida em 3 estágios, começando pela análise do acelerómetro, magnetómetro e sensor de pressão para identificação dos parâmetros do Timed-Up and Go test, utilizando métodos estatísticos para a análise dos dados recolhidos. No segundo estágio foram implementados métodos estatísticos para correlacionar as doenças passiveis de serem detetadas por sensores de Eletrocardiografia e Eletroencefalografia. Por fim, no terceiro estágio foram implementados métodos de inteligência artificial, i.e., redes neuronais artificiais, para relacionar as doenças do foro cardíaco e nervoso com os dados dos diferentes indivíduos de modo a aferir as suas características. Como trabalho futuro, os resultados apresentados nesta dissertação podem servir para a criação de sistemas de baixo-custo, e de acesso a todos os cidadãos, que permitam a deteção mais atempada de determinados distúrbios e possam servir de auxílio aos profissionais de saúde no diagnóstico e tratamento de doenças

Children’s Fitness and Quality of Movement

Introduction: Movement is essential to life and plays a key role in development throughout childhood. Movement can be assessed by its quantity and quality. Movement is important to measure as it can aid early intervention. Current research suggests that global levels of fitness are declining, with a lack of research surrounding children’s natural fitness levels as they get older. Quantity of movement is commonly studied, however quality is becoming increasingly popular. A clear understanding of the methods of technology used to measure quality of movement is important as understanding this area will aid in designing appropriate interventions.Methods: This thesis comprises of two experimental studies. Study one is a repeated measures design using previously collected Swanlinx data to investigate how components of children’s fitness change over a one-year period. Study two is a scoping review investigating the measurement of quality of movement with technology in the form of MEM’s devices, while aiming to gain clarity on the definition of quality.Results: Study one revealed that children’s fitness levels increase across a one-year period, in all components of fitness, except sit and reach. Boys performed significantly better in all fitness components, apart from sit and reach. Study two demonstrated the broad field that is included under the term of quality, showing clarity is needed in this area. A large number of devices, movements and populations are being observed, with multiple definitions of quality which is dependent on the metrics collected.Conclusion: Study one concludes that children’s fitness levels increase over one-year, with boys performing better than girls. This can be used to understand children’s natural fitness levels and aid future interventions in participation. Study two concludes that there are multiple ways to assess quality of movement however a clear definition of the quality should be stated, aiding comparison of quality

A Systematic Review and Meta-Analysis of the Incidence of Injury in Professional Female Soccer

The epidemiology of injury in male professional football is well documented and has been used as a basis to monitor injury trends and implement injury prevention strategies. There are no systematic reviews that have investigated injury incidence in women’s professional football. Therefore, the extent of injury burden in women’s professional football remains unknown. PURPOSE: The primary aim of this study was to calculate an overall incidence rate of injury in senior female professional soccer. The secondary aims were to provide an incidence rate for training and match play. METHODS: PubMed, Discover, EBSCO, Embase and ScienceDirect electronic databases were searched from inception to September 2018. Two reviewers independently assessed study quality using the Strengthening the Reporting of Observational Studies in Epidemiology statement using a 22-item STROBE checklist. Seven prospective studies (n=1137 professional players) were combined in a pooled analysis of injury incidence using a mixed effects model. Heterogeneity was evaluated using the Cochrane Q statistic and I2. RESULTS: The epidemiological incidence proportion over one season was 0.62 (95% CI 0.59 - 0.64). Mean total incidence of injury was 3.15 (95% CI 1.54 - 4.75) injuries per 1000 hours. The mean incidence of injury during match play was 10.72 (95% CI 9.11 - 12.33) and during training was 2.21 (95% CI 0.96 - 3.45). Data analysis found a significant level of heterogeneity (total Incidence, X2 = 16.57 P < 0.05; I2 = 63.8%) and during subsequent sub group analyses in those studies reviewed (match incidence, X2 = 76.4 (d.f. = 7), P <0.05; I2 = 90.8%, training incidence, X2 = 16.97 (d.f. = 7), P < 0.05; I2 = 58.8%). Appraisal of the study methodologies revealed inconsistency in the use of injury terminology, data collection procedures and calculation of exposure by researchers. Such inconsistencies likely contribute to the large variance in the incidence and prevalence of injury reported. CONCLUSIONS: The estimated risk of sustaining at least one injury over one football season is 62%. Continued reporting of heterogeneous results in population samples limits meaningful comparison of studies. Standardising the criteria used to attribute injury and activity coupled with more accurate methods of calculating exposure will overcome such limitations

Computing the Partial Correlation of ICA Models for Non-Gaussian Graph Signal Processing

[EN] Conventional partial correlation coefficients (PCC) were extended to the non-Gaussian case, in particular to independent component analysis (ICA) models of the observed multivariate samples. Thus, the usual methods that define the pairwise connections of a graph from the precision matrix were correspondingly extended. The basic concept involved replacing the implicit linear estimation of conventional PCC with a nonlinear estimation (conditional mean) assuming ICA. Thus, it is better eliminated the correlation between a given pair of nodes induced by the rest of nodes, and hence the specific connectivity weights can be better estimated. Some synthetic and real data examples illustrate the approach in a graph signal processing context.This research was funded by Spanish Administration and European Union under grants TEC2014-58438-R and TEC2017-84743-P.Belda, J.; Vergara Domínguez, L.; Safont Armero, G.; Salazar Afanador, A. (2019). 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Efficient Blind Source Separation Algorithms with Applications in Speech and Biomedical Signal Processing

Blind source separation/extraction (BSS/BSE) is a powerful signal processing method and has been applied extensively in many fields such as biomedical sciences and speech signal processing, to extract a set of unknown input sources from a set of observations. Different algorithms of BSS were proposed in the literature, that need more investigations, related to the extraction approach, computational complexity, convergence speed, type of domain (time or frequency), mixture properties, and extraction performances. This work presents a three new BSS/BSE algorithms based on computing new transformation matrices used to extract the unknown signals. Type of signals considered in this dissertation are speech, Gaussian, and ECG signals. The first algorithm, named as the BSE-parallel linear predictor filter (BSE-PLP), computes a transformation matrix from the the covariance matrix of the whitened data. Then, use the matrix as an input to linear predictor filters whose coefficients being the unknown sources. The algorithm has very fast convergence in two iterations. Simulation results, using speech, Gaussian, and ECG signals, show that the model is capable of extracting the unknown source signals and removing noise when the input signal to noise ratio is varied from -20 dB to 80 dB. The second algorithm, named as the BSE-idempotent transformation matrix (BSE-ITM), computes its transformation matrix in iterative form, with less computational complexity. The proposed method is tested using speech, Gaussian, and ECG signals. Simulation results show that the proposed algorithm significantly separate the source signals with better performance measures as compared with other approaches used in the dissertation. The third algorithm, named null space idempotent transformation matrix (NSITM) has been designed using the principle of null space of the ITM, to separate the unknown sources. Simulation results show that the method is successfully separating speech, Gaussian, and ECG signals from their mixture. The algorithm has been used also to estimate average FECG heart rate. Results indicated considerable improvement in estimating the peaks over other algorithms used in this work