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- Non-invasive measurement of results of timed-up and go test: preliminary results.Publication . Ponciano, Vasco Rafael Gaspar; Pires, Ivan M.; Ribeiro, Fernando Reinaldo; Garcia, Nuno M.; Pombo, NunoWith 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.
- Machine learning techniques with ECG and EEG data: an exploratory studyPublication . Ponciano, Vasco Rafael Gaspar; Pires, Ivan M.; Ribeiro, Fernando Reinaldo; Garcia, Nuno M.; Villasana, María Vanessa; Lameski, Petre; Zdravevski, EftimElectrocardiography (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.
- Mobile application for inclusive tourismPublication . Ponciano, Vasco Rafael Gaspar; Pires, Ivan M.; Ribeiro, Fernando Reinaldo; Garcia, Nuno M.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 at
- Identification of diseases based on the use of inertial sensors: a systematic reviewPublication . Ponciano, Vasco Rafael Gaspar; Pires, Ivan M.; Ribeiro, Fernando Reinaldo; Marques, Gonçalo Santos; Villasana, María Vanessa; Garcia, Nuno M.; Zdravevski, Eftim; Spinsante, SusannaInertial 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.
- Sensors are capable to help in the measurement of the results of the timed-up and go test? A systematic reviewPublication . Ponciano, Vasco Rafael Gaspar; Pires, Ivan M.; Ribeiro, Fernando Reinaldo; Spinsate, SusannaThe 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.
- Detection of diseases based on electrocardiography and electroencephalography signals embedded in different devices: an exploratory studyPublication . Ponciano, Vasco Rafael Gaspar; Pires, Ivan M.; Ribeiro, Fernando Reinaldo; Villasana, María Vanessa; Garcia, Nuno M.; Leithardt, ValderiNowadays, 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.
- Smartphone-based automatic measurement of the results of the timed-up and go testPublication . Ponciano, Vasco Rafael Gaspar; Pires, Ivan M.; Ribeiro, Fernando Reinaldo; Garcia, Nuno M.; Pombo, Nuno; Spinsante, Susanna; Crisóstomo, RuteThe 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.
- Is the timed-up and go test feasible in mobile devices? A systematic reviewPublication . Ponciano, Vasco Rafael Gaspar; Pires, Ivan M.; Ribeiro, Fernando Reinaldo; Marques, Gonçalo Santos; Garcia, Nuno M.; Pombo, Nuno; Spinsante, Susanna; Zdravevski, EftimThe 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.
- Experimental study for determining the parameters required for detecting ECG and EEG related diseases during the timed-up and go testPublication . Ponciano, Vasco Rafael Gaspar; Pires, Ivan M.; Ribeiro, Fernando Reinaldo; Villasana, María Vanessa; Teixeira, M.C.C.; Zdravevski, EftimThe 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.
- Mobile computing technologies for health and mobility assessment: research design and results of the ttmed up and go test in older adultsPublication . Ponciano, Vasco Rafael Gaspar; Pires, Ivan M.; Ribeiro, Fernando Reinaldo; Villasana, María Vanessa; Crisóstomo, Rute; Teixeira, M.C.C.; Zdravevski, EftimDue 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.