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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.
- Identification of activities of daily living through data fusion on motion and magnetic sensors embedded on mobile devicesPublication . Pires, Ivan M.; Pombo, Nuno; Flórez-Revuelta, Francisco; Spinsante, Susanna; Teixeira, M.C.C.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.
- Android library for recognition of activities of daily living: implementation considerations, challenges, and solutionsPublication . Pires, Ivan M.; Teixeira, M.C.C.; Pombo, Nuno; Garcia, Nuno M.; Flórez-Revuelta, Francisco; Spinsante, Susanna; Goleva, Rossitza; Zdravevski, EftimOff-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.
- 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.
- Thought on food: a systematic review of current approaches and challenges for food intake detectionPublication . Neves, Paulo Alexandre; Simões, João; Costa, Ricardo; Pimenta, Luís; Gonçalves, Norberto Jorge; Albuquerque, Carlos; Cunha, Carlos; Zdravevski, Eftim; Lameski, Petre; Garcia, Nuno M.; Pires, Ivan M.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.
- 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.