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

Is the timed-up and go test feasible in mobile devices? A systematic review

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.info:eu-repo/semantics/publishedVersio

Experimental study for determining the parameters required for detecting ECG and EEG related diseases during the timed-up and go test

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.info:eu-repo/semantics/publishedVersio

A Research on the Classification and Applicability of the Mobile Health Applications

Mobile health applications are applied for different purposes. Healthcare professionals and other users can use this type of mobile applications for specific tasks, such as diagnosis, information, prevention, treatment, and communication. This paper presents an analysis of mobile health applications used by healthcare professionals and their patients. A secondary objective of this article is to evaluate the scientific validation of these mobile health applications and to verify if the results provided by these applications have an underlying sound scientific foundation. This study also analyzed literature references and the use of mobile health applications available in online application stores. In general, a large part of these mobile health applications provides information about scientific validation. However, some mobile health applications are not validated. Therefore, the main contribution of this paper is to provide a comprehensive analysis of the usability and user-perceived quality of mobile health applications and the challenges related to scientific validation of these mobile applications.This work was 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)

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

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

Detecção de doenças baseadas em sinais de eletrocardiografia e eletroencefalografia incorporados em diferentes dispositivos: um estudo exploratório

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.info:eu-repo/semantics/publishedVersio

Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases during the Timed-Up and Go Test

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

Sensors are capable to help in the measurement of the results of the timed-up and go test? A systematic review

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.info:eu-repo/semantics/publishedVersio

Mobile application for inclusive tourism

“© © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”Tourism is one of the most important economic sectors for Portugal and many countries in the world. With the emergence of low-cost aviation companies, this sector's growth has been exponential. Hence, operators, municipalities, and governments have to adapt to this new world order. A part of the world population that intends to visit has some type of disabilities. On the other hand, as the development of digital platforms, namely at the level of mobile devices, here opens many opportunities to be explored in this binomial between people with disabilities, their willingness to practice tourism, and the use of mobile devices for this purpose. This article intends to present a mobile application developed that allows the practice of inclusive tourism, using google maps and using an algorithm that helps classify the level of accessibility of each point of tourist interest. Finally, it allows the person with disabilities to know atinfo:eu-repo/semantics/publishedVersio