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Objetivos: Pretende-se desenvolver uma aplicação de internet e mobile do teste funcional Heel-rise Test, já existente na literatura, mas cuja realização é manual. Materiais e métodos: O acelerómetro, instrumento capaz de capturar dados de aceleração instantânea de um sujeito ou objeto, permite obter dados que, depois de processados, identificam atividades da vida diária utilizando por exemplo, o reconhecimento de padrões. Os dados capturados precisam ser pré – processados (por exemplo: exclusão de ruídos), analisados e classificados. Análise Estatística: Através de um algoritmo implementado numa aplicação móvel, os dados recolhidos, após processados, permitem verificar a validade do exercício executado recorrendo à identificação de padrões de acelerometria para a validação dos exercícios realizados no decorrer de testes funcionais. Resultados: Foi desenvolvido um protótipo de uma aplicação móvel para o teste funcional Heel-rise Test em que, está implementado o algoritmo, baseado na literatura e atividades experimentais prévias que, ainda assim, necessita de uma exaustiva validação com diferentes populações (idades, estilo de vida, condições específicas) para ser válida. Conclusões: O protótipo de aplicação do Heel-rise Test mostrou estabilidade e o algoritmo resultou. No futuro, esta aplicação requer avaliação da validade e fiabilidade de forma a poder ser usadas por fisioterapeutas, outros profissionais e público em geral de forma a avaliar a funcionalidade dos indivíduos associada à componente física de força muscular do músculo tricípite sural.

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The analysis of movements used in physiotherapy areas related to the elderly is becoming increasingly important due to factors such as the increase in the average life expectancy and the rate of elderly people over the whole population. In this systematic review, we try to determine how the inertial sensors embedded in mobile devices are exploited for the measurement of the different parameters involved in the Timed-Up and Go test. The results show the mobile devices equipped with onboard motion sensors can be exploited for these types of studies: the most commonly used sensors are the magnetometer, accelerometer and gyroscope available in consumer off-the-shelf smartphones. Other features typically used to evaluate the Timed-Up and Go test are the time duration, the angular velocity and the number of steps, allowing for the recognition of some diseases as well as the measurement of the subject’s performance during the test execution.

Dissertação apresentada à Escola Superior de Tecnologia do Instituto Politécnico de Castelo Branco para obtenção do grau de Mestre em Desenvolvimento de Software e Sistemas Interativos.

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.

Electrocardiography (ECG) and electroencephalography (EEG) are powerful tools in medicine for the analysis of various diseases. The emergence of affordable ECG and EEG sensors and ubiquitous mobile devices provides an opportunity to make such analysis accessible to everyone. In this paper, we propose the implementation of a neural network-based method for the automatic identification of the relationship between the previously known conditions of older adults and the different features calculated from the various signals. The data were collected using a smartphone and low-cost ECG and EEG sensors during the performance of the timed-up and go test. Different patterns related to the features extracted, such as heart rate, heart rate variability, average QRS amplitude, average R-R interval, and average R-S interval from ECG data, and the frequency and variability from the EEG data were identified. A combination of these parameters allowed us to identify the presence of certain diseases accurately. The analysis revealed that the different institutions and ages were mainly identified. Still, the various diseases and groups of diseases were difficult to recognize, because the frequency of the different diseases was rare in the considered population. Therefore, the test should be performed with more people to achieve better results.

The use of smartphones, coupled with different sensors, makes it an attractive solution for measuring different physical and physiological features, allowing for the monitoring of various parameters and even identifying some diseases. The BITalino device allows the use of different sensors, including Electroencephalography (EEG) and Electrocardiography (ECG) sensors, to study different health parameters. With these devices, the acquisition of signals is straightforward, and it is possible to connect them using a Bluetooth connection. With the acquired data, it is possible to measure parameters such as calculating the QRS complex and its variation with ECG data to control the individual’s heartbeat. Similarly, by using the EEG sensor, one could analyze the individual’s brain activity and frequency. The purpose of this paper is to present a method for recognition of the diseases related to ECG and EEG data, with sensors available in off-the-shelf mobile devices and sensors connected to a BITalino device. The data were collected during the elderly’s experiences, performing the Timed-Up and Go test, and the different diseases found in the sample in the study. The data were analyzed, and the following features were extracted from the ECG, including heart rate, linear heart rate variability, the average QRS interval, the average R-R interval, and the average R-S interval, and the EEG, including frequency and variability. Finally, the diseases are correlated with different parameters, proving that there are relations between the individuals and the different health conditions.

With the evolution of the time and together with the evolution of ubiquitous systems with high processing capacity, various forms of use and that allow the realisation of several studies and the joining of areas of knowledge quite heterogeneous as computer science and physiotherapy. The use of martphones, in conjunction with inertial sensors, represents not only an excellent opportunity for the development of support and research applications but also a way to create cheaper solutions close to people. In this study, we also propose that as an experimental form the use of this type of sensors to capture movements using the timed up and go test and with the objectives and to create tools that allow the detection of diseases related to the action in elderly individuals. This paper presents the results of the data capture with different perspectives and using various features such as the time of the test the time of getting up from the chair, sitting in the chair, reaction to sound signalling during the trial, time reversal and the time it takes for the individual to sit down.

Nowadays, cardiac and brain disorders are dispersed over the world, where an early detection allows the treatment and prevention of other related healthcare problems. Technologically, this detection is difficult to perform, and the use of technology and artificial intelligence techniques may automate the accurate detection of different diseases. This paper presents the research on the different techniques and parameters for the detection of diseases related to Electrocardiography (ECG) and Electroencephalography (EEG) signals. Previously experiments related to the performance of the Timed-Up and Go test with elderly people acquired different signals from people with different diseases. This study identifies different parameters and methods that may be used for the identification of different diseases based on the acquired data.

We present a dataset related to the acquisition of different sensors data during the performance of the Timed-Up and Go test with the mobile device positioned in a waistband for the acquisition of accelerometer and magnetometer data, and a BITalino device positioned in a chest band for the acquisition of Electrocardiography and Electroencephalography for further processing. The data acquired from the BITalino device is acquired simultaneously by a Bluetooth connection with the same mobile application. The data was acquired in five institutions, including Centro Comunitário das Lameiras, Lar Nossa Senhora de Fátima, Centro Comunitário das Minas da Panasqueira, Lar da Misericórdia da Santa Casa da Misericórdia do Fundão, and Lar da Aldeia de Joanes da Santa Casa da Misericórdia do Fundão from Fundão and Covilhã municipalities (Portugal). This article describes the data acquired from a several subjects from the different institutions for the acquisition of accelerometer and magnetometer data, where each person performed the Timed-Up and Go test three times, where each output from the sensors was acquired with a sampling rate of 100 Hz. Related to the data acquired by the sensors connected to the BITalino device, 31 persons performed the different experiments related to the Timed-Up and Go Test. Following the data acquired from Electroencephalography and Electrocardiography sensors, only the data acquired from 14 individuals was considered valid. The data acquired by a BITalino device has a sampling rate of 100 Hz. These data can be reused for testing machine learning methods for the evaluation of the performance of the Timed-Up and Go test with older adults.

The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject's performance during the test execution.

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.

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.

The Timed-Up and Go test is a very used test in the physiotherapy area. For the measurement of the results of the test, we propose to use a smartphone with several embedded sensors, including accelerometer, magnetometer, gyroscope, a Bitalino device with the Electromyography (EMG) and Electrocardiography (ECG) sensors, and a second Bitalino device with a pressure sensor connected and positioned in the back of the chair. This architecture allows to capture several types of data from the sensors easily. In this paper, we present a structured method to implement the measurement of the different parameters involved in the Timed-up and Go test, for acquiring, processing and cleaning the collected measurements. This data will help in the classification of the test results initially, and later on to discover more complex patterns and related conditions, such as equilibrium changes, neurological pathologies, degenerative pathologies, lesions of lower limbs and chronic venous diseases.

Nowadays, individuals have very stressful lifestyles, affecting their nutritional habits. In the early stages of life, teenagers begin to exhibit bad habits and inadequate nutrition. Likewise, other people with dementia, Alzheimer’s disease, or other conditions may not take food or medicine regularly. Therefore, the ability to monitor could be beneficial for them and for the doctors that can analyze the patterns of eating habits and their correlation with overall health. Many sensors help accurately detect food intake episodes, including electrogastrography, cameras, microphones, and inertial sensors. Accurate detection may provide better control to enable healthy nutrition habits. This paper presents a systematic review of the use of technology for food intake detection, focusing on the different sensors and methodologies used. The search was performed with a Natural Language Processing (NLP) framework that helps screen irrelevant studies while following the PRISMA methodology. It automatically searched and filtered the research studies in different databases, including PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Then, the manual analysis selected 30 papers based on the results of the framework for further analysis, which support the interest in using sensors for food intake detection and nutrition assessment. The mainly used sensors are cameras, inertial, and acoustic sensors that handle the recognition of food intake episodes with artificial intelligence techniques. This research identifies the most used sensors and data processing methodologies to detect food intake.