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Worldwide, cardiovascular diseases are some of the primary causes of death; yet the early detection and diagnosis of such diseases have the potential to save many lives. Technological means of detection are becoming increasingly essential and numerous techniques have been created for this purpose, such as forecasting. Of these techniques, the time series forecasting technique seeks to predict future events. The long-term time series forecasting of physiological data could assist medical professionals in predicting and treating patients based on very early diagnosis. This article presents a model that utilizes a deep learning technique to predict long-term ECG signals. The forecasting model can learn signals' nonlinearity, nonstationarity, and complexity based on a long short-term memory architecture. However, this is not a trivial task as the correct forecasting of a signal that closely resembles the original complex signal's structure and behavior while minimizing any differences in amplitude continues to pose challenges. To achieve this goal, we used a dataset available on the Physio net database, called MIT-BIH, with 48 ECG recordings of 30 min each. The developed model starts with pre-processing to reduce interference in the original signals, then applies a deep learning algorithm, based on a long short-term memory (LTSM) neural network with two hidden layers. Next, we applied the root mean square error (RMSE) and mean absolute error (MAE) metrics to evaluate the performance of the model and obtained an average RMSE of 0.0070 ± 0.0028 and an average MAE of 0.0522 ± 0.0098 across all simulations. The results indicate that the proposed LSTM model is a promising technique for ECG forecasting, considering the trends of the changes in the original data series, most notably in R-peak amplitude. Given the model's accuracy and the features of the physiological signals, the system could be used to improve existing predictive healthcare systems for cardiovascular monitoring. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Rehabilitation aims to increase the independence and physical function after injury, surgery, or other trauma, so that patients can recover to their previous ability as much as possible. To be able to measure the degree of recovery and impact of the treatment, various functional performance tests are used. The Eight Hop Test is a hop exercise that is directly linked to the rehabilitation of people suffering from tendon and ligament injuries on the lower limb. This paper presents a systematic review on the use of sensors for measuring functional movements during the execution of the Eight Hop Test, focusing primarily on the use of sensors, related diseases, and different methods implemented. Firstly, an automated search was performed on the publication databases: PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Secondly, the publications related to the Eight-Hop Test and sensors were filtered according to several search criteria and 15 papers were finally selected to be analyzed in detail. Our analysis found that the Eight Hop Test measurements can be performed with motion, force, and imaging sensors. [ABSTRACT FROM AUTHOR]

Sensors available on mobile devices allow the automatic identification of Activities of Daily Living (ADL). This paper describes an approach for the creation of a framework for the identification of ADL, taking into account several concepts, including data acquisition, data processing, data fusion, and pattern recognition. These concepts can be mapped onto different modules of the framework. The proposed framework should perform the identification of ADL without Internet connection, performing these tasks locally on the mobile device, taking in account the hardware and software limitations of these devices. The main purpose of this paper is to present a new approach for the creation of a framework for the recognition of ADL, analyzing the allowed sensors available in the mobile devices, and the existing methods available in the literature. [ABSTRACT FROM AUTHOR]

An increase in the accuracy of identification of Activities of Daily Living (ADL) is very important for different goals of Enhanced Living Environments and for Ambient Assisted Living (AAL) tasks. This increase may be achieved through identification of the surrounding environment. Although this is usually used to identify the location, ADL recognition can be improved with the identification of the sound in that particular environment. This paper reviews audio fingerprinting techniques that can be used with the acoustic data acquired from mobile devices. A comprehensive literature search was conducted in order to identify relevant English language works aimed at the identification of the environment of ADLs using data acquired with mobile devices, published between 2002 and 2017. In total, 40 studies were analyzed and selected from 115 citations. The results highlight several audio fingerprinting techniques, including Modified discrete cosine transform (MDCT), Mel-frequency cepstrum coefficients (MFCC), Principal Component Analysis (PCA), Fast Fourier Transform (FFT), Gaussian mixture models (GMM), likelihood estimation, logarithmic moduled complex lapped transform (LMCLT), support vector machine (SVM), constant Q transform (CQT), symmetric pairwise boosting (SPB), Philips robust hash (PRH), linear discriminant analysis (LDA) and discrete cosine transform (DCT). [ABSTRACT FROM AUTHOR]

This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user's daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs). [ABSTRACT FROM AUTHOR]

Medicine is heading towards personalized care based on individual situations and conditions. With smartphones and increasingly miniaturized wearable devices, the sensors available on these devices can perform long-term continuous monitoring of several user health-related parameters, making them a powerful tool for a new medicine approach for these patients. Our proposed system, described in this article, aims to develop innovative solutions based on artificial intelligence techniques to empower patients with cardiovascular disease. These solutions will realize a novel 5P (Predictive, Preventive, Participatory, Personalized, and Precision) medicine approach by providing patients with personalized plans for treatment and increasing their ability for self-monitoring. Such capabilities will be derived by learning algorithms from physiological data and behavioral information, collected using wearables and smart devices worn by patients with health conditions. Further, developing an innovative system of smart algorithms will also focus on providing monitoring techniques, predicting extreme events, generating alarms with varying health parameters, and offering opportunities to maintain active engagement of patients in the healthcare process by promoting the adoption of healthy behaviors and well-being outcomes. The multiple features of this future system will increase the quality of life for cardiovascular diseases patients and provide seamless contact with a healthcare professional. [ABSTRACT FROM AUTHOR]

Healthcare treatments might benefit from advances in artificial intelligence and technological equipment such as smartphones and smartwatches. The presence of cameras in these devices with increasingly robust and precise pattern recognition techniques can facilitate the estimation of the wound area and other telemedicine measurements. Currently, telemedicine is vital to the maintenance of the quality of the treatments remotely. This study proposes a method for measuring the wound area with mobile devices. The proposed approach relies on a multi-step process consisting of image capture, conversion to grayscale, blurring, application of a threshold with segmentation, identification of the wound part, dilation and erosion of the detected wound section, identification of accurate data related to the image, and measurement of the wound area. The proposed method was implemented with the OpenCV framework. Thus, it is a solution for healthcare systems by which to investigate and treat people with skin-related diseases. The proof-of-concept was performed with a static dataset of camera images on a desktop computer. After we validated the approach's feasibility, we implemented the method in a mobile application that allows for communication between patients, caregivers, and healthcare professionals. [ABSTRACT FROM AUTHOR]

Pneumonia caused by COVID-19 is a severe health risk that sometimes leads to fatal outcomes. Due to constraints in medical care systems, technological solutions should be applied to diagnose, monitor, and alert about the disease's progress for patients receiving care at home. Some sleep disturbances, such as obstructive sleep apnea syndrome, can increase the risk for COVID-19 patients. This paper proposes an approach to evaluating patients' sleep quality with the aim of detecting sleep disturbances caused by pneumonia and other COVID-19-related pathologies. We describe a non-invasive sensor network that is used for sleep monitoring and evaluate the feasibility of an approach for training a machine learning model to detect possible COVID-19-related sleep disturbances. We also discuss a cloud-based approach for the implementation of the proposed system for processing the data streams. Based on the preliminary results, we conclude that sleep disturbances are detectable with affordable and non-invasive sensors. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices' security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established. However, due to the resource constraint property of IoT devices and the distinct behavior of IoT protocols, the existing security mechanisms cannot be deployed directly for securing the IoT devices and network from the cyber-attacks. To enhance the level of security for IoT, researchers need IoT-specific tools, methods, and datasets. To address the mentioned problem, we provide a framework for developing IoT context-aware security solutions to detect malicious traffic in IoT use cases. The proposed framework consists of a newly created, open-source IoT data generator tool named IoT-Flock. The IoT-Flock tool allows researchers to develop an IoT use-case comprised of both normal and malicious IoT devices and generate traffic. Additionally, the proposed framework provides an open-source utility for converting the captured traffic generated by IoT-Flock into an IoT dataset. Using the proposed framework in this research, we first generated an IoT healthcare dataset which comprises both normal and IoT attack traffic. Afterwards, we applied different machine learning techniques to the generated dataset to detect the cyber-attacks and protect the healthcare system from cyber-attacks. The proposed framework will help in developing the context-aware IoT security solutions, especially for a sensitive use case like IoT healthcare environment. [ABSTRACT FROM AUTHOR]

The wide-spread use of wearables and the adoption of the Internet of Things (IoT) paradigm provide an opportunity to use mobile-device sensors for medical applications. Sensors available in the commonly used devices may inspire innovative solutions for physiotherapy striving for accurate and early identification of various pathologies. An essential and reliable performance measure is the ten-meter walk test, which is employed to determine functional mobility, gait, and vestibular function. Sensor-based approaches can identify the various test phases and their segmented duration, among other parameters. The measurement parameter primarily used is related to the tests' duration, and after identifying patterns, a variety of physical treatments can be recommended. This paper reviews multiple studies focusing on automated measurements of the ten-meter walk test with different sensors. Most of the analyzed studies measure similar parameters as traditional methods, such as velocity, duration, and other involuntary and dangerous patients' movements after stroke. That provides an opportunity to measure different parameters that can be later fed into machine learning models for analyzing more complex patterns. [ABSTRACT FROM AUTHOR]

In recent years, research in tracking and assessing wound severity using computerized image processing has increased. With the emergence of mobile devices, powerful functionalities and processing capabilities have provided multiple non-invasive wound evaluation opportunities in both clinical and non-clinical settings. With current imaging technologies, objective and reliable techniques provide qualitative information that can be further processed to provide quantitative information on the size, structure, and color characteristics of wounds. These efficient image analysis algorithms help determine the injury features and the progress of healing in a short time. This paper presents a systematic investigation of articles that specifically address the measurement of wounds' sizes with image processing techniques, promoting the connection between computer science and health. Of the 208 studies identified by searching electronic databases, 20 were included in the review. From the perspective of image processing color models, the most dominant model was the hue, saturation, and value (HSV) color space. We proposed that a method for measuring the wound area must implement different stages, including conversion to grayscale for further implementation of the threshold and a segmentation method to measure the wound area as the number of pixels for further conversion to metric units. Regarding devices, mobile technology is shown to have reached the level of reliable accuracy. [ABSTRACT FROM AUTHOR]

Smartphone sensors have often been proposed as pervasive measurement systems to assess mobility in older adults due to their ease of use and low-cost. This study analyzes a smartphone-based application's validity and reliability to quantify temporal variables during the single sit-to-stand test with institutionalized older adults. Forty older adults (20 women and 20 men; 78.9 ± 8.6 years) volunteered to participate in this study. All participants performed the single sit-to-stand test. Each sit-to-stand repetition was performed after an acoustic signal was emitted by the smartphone app. All data were acquired simultaneously with a smartphone and a digital video camera. The measured temporal variables were stand-up time and total time. The relative reliability and systematic bias inter-device were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots. In contrast, absolute reliability was assessed using the standard error of measurement and coefficient of variation (CV). Inter-device concurrent validity was assessed through correlation analysis. The absolute percent error (APE) and the accuracy were also calculated. The results showed excellent reliability (ICC = 0.92–0.97; CV = 1.85–3.03) and very strong relationships inter-devices for the stand-up time (r = 0.94) and the total time (r = 0.98). The APE was lower than 6%, and the accuracy was higher than 94%. Based on our data, the findings suggest that the smartphone application is valid and reliable to collect the stand-up time and total time during the single sit-to-stand test with older adults. [ABSTRACT FROM AUTHOR]

Air pollution is becoming a rising and serious environmental problem, especially in urban areas affected by an increasing migration rate. The large availability of sensor data enables the adoption of analytical tools to provide decision support capabilities. Employing sensors facilitates air pollution monitoring, but the lack of predictive capability limits such systems' potential in practical scenarios. On the other hand, forecasting methods offer the opportunity to predict the future pollution in specific areas, potentially suggesting useful preventive measures. To date, many works tackled the problem of air pollution forecasting, most of which are based on sequence models. These models are trained with raw pollution data and are subsequently utilized to make predictions. This paper proposes a novel approach evaluating four different architectures that utilize camera images to estimate the air pollution in those areas. These images are further enhanced with weather data to boost the classification accuracy. The proposed approach exploits generative adversarial networks combined with data augmentation techniques to mitigate the class imbalance problem. The experiments show that the proposed method achieves robust accuracy of up to 0.88, which is comparable to sequence models and conventional models that utilize air pollution data. This is a remarkable result considering that the historic air pollution data is directly related to the output—future air pollution data, whereas the proposed architecture uses camera images to recognize the air pollution—which is an inherently much more difficult problem. [ABSTRACT FROM AUTHOR]

Simple Summary: Technological advancements have led to modern DNA sequencing methods, capable of generating large amounts of data describing the microorganisms that live in samples taken from the environment. Metagenomics, the field that studies the different genomes within these samples, is becoming increasingly popular, as it has many real-world applications, such as the discovery of new antibiotics, personalized medicine, forensics, and many more. From a computer science point of view, it is interesting to see how these large volumes of data can be processed efficiently to accurately identify (classify) the microorganisms from the input DNA data. This scoping review aims to give an insight into the existing state of the art computational methods for processing metagenomic data through the prism of machine learning, data science, and big data. We provide an overview of the state of the art metagenomic classification methods, as well as the challenges researchers face when tackling this complex problem. The end goal of this review is to help researchers be up to date with current trends, as well as identify opportunities for further research and improvements. Applied machine learning in bioinformatics is growing as computer science slowly invades all research spheres. With the arrival of modern next-generation DNA sequencing algorithms, metagenomics is becoming an increasingly interesting research field as it finds countless practical applications exploiting the vast amounts of generated data. This study aims to scope the scientific literature in the field of metagenomic classification in the time interval 2008–2019 and provide an evolutionary timeline of data processing and machine learning in this field. This study follows the scoping review methodology and PRISMA guidelines to identify and process the available literature. Natural Language Processing (NLP) is deployed to ensure efficient and exhaustive search of the literary corpus of three large digital libraries: IEEE, PubMed, and Springer. The search is based on keywords and properties looked up using the digital libraries' search engines. The scoping review results reveal an increasing number of research papers related to metagenomic classification over the past decade. The research is mainly focused on metagenomic classifiers, identifying scope specific metrics for model evaluation, data set sanitization, and dimensionality reduction. Out of all of these subproblems, data preprocessing is the least researched with considerable potential for improvement. [ABSTRACT FROM AUTHOR]

One class of applications for human activity recognition methods is found in mobile devices for monitoring older adults and people with special needs. Recently, many studies were performed to create intelligent methods for the recognition of human activities. However, the different mobile devices in the market acquire the data from sensors at different frequencies. This paper focuses on implementing four data normalization techniques, i.e., MaxAbsScaler, MinMaxScaler, RobustScaler, and Z-Score. Subsequently, we evaluate the impact of the normalization algorithms with deep neural networks (DNN) for the classification of the human activities. The impact of the data normalization was counterintuitive, resulting in a degradation of performance. Namely, when using the accelerometer data, the accuracy dropped from about 79% to only 53% for the best normalization approach. Similarly, for the gyroscope data, the accuracy without normalization was about 81.5%, whereas with the best normalization, it was only 60%. It can be concluded that data normalization techniques are not helpful in classification problems with homogeneous data. [ABSTRACT FROM AUTHOR]

Educating teenagers about nutrition and promoting active lifestyles is essential in reducing the long-term health risks and one idea to achieve this is by using mobile applications. Previous studies showed that the existing mobile applications have similar functionalities, such as intervention with questionnaires, and the use of gamification techniques to improve interactiveness. However, unlike our study, some studies are not validated and verified by healthcare professionals. Additionally, this study intends to promote the interaction between the teenagers and the medical communities. In this study, we analyze the benefits of the proposed mobile application, which features monitoring of physical activity, daily tips and curiosities, questionnaires, and gamification through earning points. Most of the teenagers were satisfied with the physical activity monitoring and found the tips, curiosities, and weekly questionnaires useful. The study started with 26 teenagers from two schools in the center of Portugal that would use the mobile application for five weeks. Still, at the end of the study, only 7 teenagers finalized the study. The decreasing number of teenagers in the study was affected by the lack of social interaction caused by the pandemic situation. During the period, the mobile application would engage the users with notifications on nutrition and physical activity, challenges concerning the number of steps and calories they would have to spend, and questionnaires related to the curiosities and suggestions from the previous week. We used Fisher's test to investigate the relationship between the assessment obtained in the responses to the questionnaires, and the adoption of healthier eating and sports practices. In summary, participants were satisfied with the mobile application and experienced some improvements in diet and habits. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The automatic recognition of human activities with sensors available in off-the-shelf mobile devices has been the subject of different research studies in recent years. It may be useful for the monitoring of elderly people to present warning situations, monitoring the activity of sports people, and other possibilities. However, the acquisition of the data from different sensors may fail for different reasons, and the human activities are recognized with better accuracy if the different datasets are fulfilled. This paper focused on two stages of a system for the recognition of human activities: data imputation and data classification. Regarding the data imputation, a methodology for extrapolating the missing samples of a dataset to better recognize the human activities was proposed. The K-Nearest Neighbors (KNN) imputation technique was used to extrapolate the missing samples in dataset captures. Regarding the data classification, the accuracy of the previously implemented method, i.e., Deep Neural Networks (DNN) with normalized and non-normalized data, was improved in relation to the previous results without data imputation. [ABSTRACT FROM AUTHOR]

Strokes are a constant concern for people and pose a major health concern. Tests that allow detection and the rehabilitation of patients have started to become more important and essential. There are several tests used by physiotherapists to speed up the recovery process of patients. This article presents a systematic review of existing studies using the Heel-Rise Test and sensors (i.e., accelerometers, gyroscopes, pressure and tilt sensors) to estimate the different levels and health statuses of individuals. It was found that the most measured parameter was related to the number of repetitions, and the maximum number of repetitions for a healthy adult is 25 repetitions. As for future work, the implementation of these methods with a simple mobile device will facilitate the different measurements on this subject. [ABSTRACT FROM AUTHOR]

The test of physical conditions is important to treat and presents several diseases related to the movement. These diseases are mainly related to the physiotherapy and orthopedy, but it can be applied in a wide range of medical specialties. The Functional Reach Test is one of the most common physical tests used to measure the limit of stability that is highly important for older adults because their stability is reduced with aging. Thus, older adults are part of the population more exposed to stroke. This test may help in the measurement of the conditions related to post-stroke and stroke treatment. The movements related to this test may be recorded and recognized with the inertial sensors available in off-the-shelf mobile devices. This systematic review aims to determine how to determine the conditions related to this test, which can be detected, and which of the sensors are used for this purpose. The main contribution of this paper is to present the research on the state-of-the-art use of sensors available on off-the-shelf mobile devices to measure Functional Reach Test results. This research shows that the sensors that are used in the literature studies are inertial sensors and force sensors. The features extracted from the different studies are categorized as dynamic balance, quantitative, and raw statistics. These features are mainly used to recognize the different parameters of the test, and several accidents, including falling. The execution of this test may allow the early detection of different diseases. [ABSTRACT FROM AUTHOR]

Fitness and physical exercise are preferred in the pursuit of healthier and active lifestyles. The number of mobile applications aiming to replace or complement a personal trainer is increasing. However, this also raises questions about the reliability, integrity, and even safety of the information provided by such applications. In this study, we review mobile applications that serve as virtual personal trainers. We present a systematic review of 36 related mobile applications, updated between 2017 and 2020, classifying them according to their characteristics. The selection criteria considers the following combination of keywords: "workout", "personal trainer", "physical activity", "fitness", "gymnasium", and "daily plan". Based on the analysis of the identified mobile applications, we propose a new taxonomy and present detailed guidelines on creating mobile applications for personalised workouts. Finally, we investigated how can mobile applications promote health and well-being of users and whether the identified applications are used in any scientific studies. [ABSTRACT FROM AUTHOR]

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 signals for the automatic recognition of different diseases, and it may empower the different treatments with the use of less invasive and painful techniques for patients. This paper aims to provide a systematic review of the studies available in the literature for the automatic recognition of different diseases by exploiting accelerometer sensors. The most reliably detectable disease using accelerometer sensors, available in 54% of the analyzed studies, is the Parkinson's disease. The machine learning methods implemented for the automatic recognition of Parkinson's disease reported an accuracy of 94%. The recognition of other diseases is investigated in a few other papers, and it appears to be the target of further analysis in the future. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs' identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN). [ABSTRACT FROM AUTHOR]

The use of mobile devices connected continuously to the cloud is increasing, and the development of a cloud-based solution may power the function of these devices in mobility. Several types of sensors available in the mobile devices may allow the acquisition of different kinds of data, including inertial sensors, magnetic sensors, location sensors, acoustic sensors, and imaging sensors. The primary purpose of this study is to review the methods, features, and studies related to the identification of road conditions and warning situations. We performed systematic research to discover relevant studies written in English for the identification of different situations using the sensors available in the mobile devices, published between 2011 and 2019. After that, we analyzed the remaining studies to verify its reproducibility. The major part of the studies does not report the accuracy in the detection of warning situations. As future work, we intend to develop a system based on the Centre of Portugal for the detection of warning situations, road problems, and other issues verified during driving activities. As future work, we intend to develop a system using only a mobile device for the acquisition of sensors data in the centre of Portugal. We verified that the majority of the studies were performed in big lands, but in small areas, the number of accidents and road abnormalities is also high. [ABSTRACT FROM AUTHOR]

Amid obesity problems in the young population and apparent trends of spending a significant amount of time in a stationary position, promoting healthy nutrition and physical activities to teenagers is becoming increasingly important. It can rely on different methodologies, including a paper diary and mobile applications. However, the widespread use of mobile applications by teenagers suggests that they could be a more suitable tool for this purpose. This paper reviews the methodologies for promoting physical activities to healthy teenagers explored in different studies, excluding the analysis of different diseases. We found only nine studies working with teenagers and mobile applications to promote active lifestyles, including the focus on nutrition and physical activity. Studies report using different techniques to captivate the teenagers, including questionnaires and gamification techniques. We identified the common features used in different studies, which are: paper diary, diet diary, exercise diary, notifications, diet plan, physical activity registration, gamification, smoking cessation, pictures, game, and SMS, among others. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Nowadays, it is possible to buy clothing using online platforms, either by accessing online brand stores, general online stores or circular economy platforms. This paper presents a study on mobile applications that support online commerce for clothing, focusing on the review of the mobile applications with features that are characteristic of the circular economy paradigm. Findings include the fact that almost all the mobile applications analysed have pictures illustrative of the clothes and accessories that are available for trading as well as their brief description. Furthermore, this paper presents a study of various scientific articles about the circular economy of clothes and how it can be beneficial to the future of the environment. It is a junction with a Web platform for its growth and its disclosure. The paper builds conclusions upon the assumption that the circular economy is a growing business that is part of a sustainable development where the main goal is to reduce the environmental impact. The paper proposes the analysis of an innovative taxonomy of mobile applications about the circular economy. [ABSTRACT FROM AUTHOR]

Smart grid systems have become popular and necessary for the development of a sustainable power grid. These systems use different technologies to provide optimized services to the users of the network. Regarding computing, these systems optimize electrical services by processing a large amount of the data generated. However, privacy and security are essential in this kind of system. With a large amount of data generated, it is necessary to protect the privacy of users, because this data may reveal the users' personal information. Today, blockchain technology has proven to be an efficient architecture for solving privacy and security problems in different scenarios. Over the years, different blockchain platforms have emerged, attempting to solve specific problems in different areas. However, the use of different platforms fragmented the market, which was no different in the smart grid scenario. This work proposes a blockchain architecture that uses sidechains to make the system scalable and adaptable. We used three blockchains to ensure privacy, security, and trust in the system. To universalize the proposed solution, we used the Open Smart Grid Protocol and smart contracts. The results show that architecture security and privacy are guaranteed, making it feasible for implementation in real systems; although scalability issues regarding the storage of the data generated still exist. [ABSTRACT FROM AUTHOR]

Using the AdaBoost method may increase the accuracy and reliability of a framework for daily activities and environment recognition. Mobile devices have several types of sensors, including motion, magnetic, and location sensors, that allow accurate identification of daily activities and environment. This paper focuses on the review of the studies that use the AdaBoost method with the sensors available in mobile devices. This research identified the research works written in English about the recognition of daily activities and environment recognition using the AdaBoost method with the data obtained from the sensors available in mobile devices that were published between 2012 and 2018. Thus, 13 studies were selected and analysed from 151 identified records in the searched databases. The results proved the reliability of the method for daily activities and environment recognition, highlighting the use of several features, including the mean, standard deviation, pitch, roll, azimuth, and median absolute deviation of the signal of motion sensors, and the mean of the signal of magnetic sensors. When reported, the analysed studies presented an accuracy higher than 80% in recognition of daily activities and environments with the Adaboost method. [ABSTRACT FROM AUTHOR]

The identification of Activities of Daily Living (ADL) is intrinsic with the user's environment recognition. This detection can be executed through standard sensors present in every-day mobile devices. On the one hand, the main proposal is to recognize users' environment and standing activities. On the other hand, these features are included in a framework for the ADL and environment identification. Therefore, this paper is divided into two parts—firstly, acoustic sensors are used for the collection of data towards the recognition of the environment and, secondly, the information of the environment recognized is fused with the information gathered by motion and magnetic sensors. The environment and ADL recognition are performed by pattern recognition techniques that aim for the development of a system, including data collection, processing, fusion and classification procedures. These classification techniques include distinctive types of Artificial Neural Networks (ANN), analyzing various implementations of ANN and choosing the most suitable for further inclusion in the following different stages of the developed system. The results present 85.89% accuracy using Deep Neural Networks (DNN) with normalized data for the ADL recognition and 86.50% accuracy using Feedforward Neural Networks (FNN) with non-normalized data for environment recognition. Furthermore, the tests conducted present 100% accuracy for standing activities recognition using DNN with normalized data, which is the most suited for the intended purpose. [ABSTRACT FROM AUTHOR]

With the popularization of the Internet-of-Things, various applications have emerged to make life easier. These applications generate a large amount of user data. Analyzing the data obtained from these applications, one can infer personal information about each user. Considering this, it is clear that ensuring privacy in this type of application is essential. To guarantee privacy various solutions exist, one of them is UbiPri middleware. This paper presents a decentralized implementation of UbiPri middleware using the Ethereum blockchain. Smart contracts were used in conjunction with a communication gateway and a distributed storage service to ensure users privacy. The results obtained show that the implementation of this work ensures privacy at different levels, data storage security, and performance regarding scalability in the Internet of Things environments. [ABSTRACT FROM AUTHOR]

This paper addresses the significant need for improvements in device version reporting and practice across the academic and technical activity monitoring literature, and it recommends assessments for new and updated consumer sensing devices. Reproducibility and data veracity are central to good scholarship, and particularly significant in clinical and health applications. Across the literature there is an absence of device version reporting and a failure to recognize that device validity is not maintained when firmware and software updates can, and do, change device performance and parameter estimation. In this paper, we propose the use of tractable methods to assess devices at their current version and provide an example empirical approach. Experimental results for heart rate and step count acquisitions during walking and everyday living activities from Garmin Vivosmart 3 (v4.10) wristband monitors are presented and analyzed, and the reliability issues of optically-acquired heart rates, especially during periods of activity, are demonstrated and discussed. In conclusion, the paper recommends the empirical assessment of new and updated activity monitors and improvements in device version reporting across the academic and technical literature. [ABSTRACT FROM AUTHOR]