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Glaucoma is a chronic optic neuropathy characterized by irreversible damage to the retinal nerve fiber layer (RNFL), resulting in changes in the visual field (VC). Glaucoma screening is performed through a complete ophthalmological... more
Glaucoma is a chronic optic neuropathy characterized by irreversible damage to the retinal nerve fiber layer (RNFL), resulting in changes in the visual field (VC). Glaucoma screening is performed through a complete ophthalmological examination, using images of the optic papilla obtained in vivo for the evaluation of glaucomatous characteristics, eye pressure, and visual field. Identifying the glaucomatous papilla is quite important, as optical papillary images are considered the gold standard for tracking. Therefore, this article presents a review of the diagnostic methods used to identify the glaucomatous papilla through technology over the last five years. Based on the analyzed works, the current state-of-the-art methods are identified, the current challenges are analyzed, and the shortcomings of these methods are investigated, especially from the point of view of automation and independence in performing these measurements. Finally, the topics for future work and the challenges t...
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Nowadays, Artificial Intelligence (AI) and its subfields, Machine Learning (ML) and Deep Learning (DL), are used for a variety of medical applications. It can help clinicians track the patient’s illness cycle, assist with diagnosis, and... more
Nowadays, Artificial Intelligence (AI) and its subfields, Machine Learning (ML) and Deep Learning (DL), are used for a variety of medical applications. It can help clinicians track the patient’s illness cycle, assist with diagnosis, and offer appropriate therapy alternatives. Each approach employed may address one or more AI problems, such as segmentation, prediction, recognition, classification, and regression. However, the amount of AI-featured research on Inherited Retinal Diseases (IRDs) is currently limited. Thus, this study aims to examine artificial intelligence approaches used in managing Inherited Retinal Disorders, from diagnosis to treatment. A total of 20,906 articles were identified using the Natural Language Processing (NLP) method from the IEEE Xplore, Springer, Elsevier, MDPI, and PubMed databases, and papers submitted from 2010 to 30 October 2021 are included in this systematic review. The resultant study demonstrates the AI approaches utilized on images from differ...
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The dataset presented in this paper presents a dataset related to three motionless activities, including driving, watching TV, and sleeping. During these activities, the mobile device may be positioned in different locations, including... more
The dataset presented in this paper presents a dataset related to three motionless activities, including driving, watching TV, and sleeping. During these activities, the mobile device may be positioned in different locations, including the pants pockets, in a wristband, over the bedside table, on a table, inside the car, or on other furniture, for the acquisition of accelerometer, magnetometer, gyroscope, GPS, and microphone data. The data was collected by 25 individuals (15 men and 10 women) in different environments in Covilhã and Fundão municipalities (Portugal). The dataset includes the sensors’ captures related to a minimum of 2000 captures for each motionless activity, which corresponds to 2.8 h (approximately) for each one. This dataset includes 8.4 h (approximately) of captures for further analysis with data processing techniques, and machine learning methods. It will be useful for the complementary creation of a robust method for the identification of these type of activities.
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Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring... more
Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring systems based on the accelerometer have been proposed for the fall detection. However, many of them mistakenly identify the daily life activities as fall or fall as daily life activity. To this aim, an efficient machine learning-based fall detection algorithm has been proposed in this paper. The proposed algorithm detects fall with efficient sensitivity, specificity, and accuracy as compared to the state-of-the-art techniques. A publicly available dataset with a very simple and computationally efficient set of features is used to accurately detect the fall incident. The proposed algorithm reports and accuracy of 99.98% with the Support Vector Machine(SVM) classifier.
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Ideally, to carry out screening for eye diseases, it is expected to use specialized medical equipment to capture retinal fundus images. However, since this kind of equipment is generally expensive and has low portability, and with the... more
Ideally, to carry out screening for eye diseases, it is expected to use specialized medical equipment to capture retinal fundus images. However, since this kind of equipment is generally expensive and has low portability, and with the development of technology and the emergence of smartphones, new portable and cheaper screening options have emerged, one of them being the D-Eye device. When compared to specialized equipment, this equipment and other similar devices associated with a smartphone present lower quality and less field-of-view in the retinal video captured, yet with sufficient quality to perform a medical pre-screening. Individuals can be referred for specialized screening to obtain a medical diagnosis if necessary. Two methods were proposed to extract the relevant regions from these lower-quality videos (the retinal zone). The first one is based on classical image processing approaches such as thresholds and Hough Circle transform. The other performs the extraction of the...
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This raw dataset presents the data related to the walking, running, standing, walking upstairs, and walking downstairs captured with accelerometer, gyroscope and magnetometer sensors available in off-the-shelf mobile devices.
Most mobile devices include motion, magnetic, acoustic, and location sensors. They allow the implementation of a framework for the recognition of Activities of Daily Living (ADL) and its environments, composed by the acquisition,... more
Most mobile devices include motion, magnetic, acoustic, and location sensors. They allow the implementation of a framework for the recognition of Activities of Daily Living (ADL) and its environments, composed by the acquisition, processing, fusion, and classification of data. This study compares different implementations of artificial neural networks, concluding that the obtained results were 85.89% and 100% for the recognition of standard ADL. Additionally, for the identification of standing activities with Deep Neural Networks (DNN) respectively, and 86.50% for the identification of the environments with Feedforward Neural Networks. Numerical results illustrate that the proposed framework can achieve robust performance from the data fusion of off-the-shelf mobile devices.
Large-scale labeled datasets are generally necessary for successfully training a deep neural network in the computer vision domain. In order to avoid the costly and tedious work of manually annotating image datasets, self-supervised... more
Large-scale labeled datasets are generally necessary for successfully training a deep neural network in the computer vision domain. In order to avoid the costly and tedious work of manually annotating image datasets, self-supervised learning methods have been proposed to learn general visual features automatically. In this paper, we first focus on image colorization with generative adversarial networks (GANs) because of their ability to generate the most realistic colorization results. Then, via transfer learning, we use this as a proxy task for visual understanding. Particularly, we propose to use conditional GANs (cGANs) for image colorization and transfer the gained knowledge to two other downstream tasks, namely, multilabel image classification and semantic segmentation. This is the first time that GANs have been used for self-supervised feature learning through image colorization. Through extensive experiments with the COCO and Pascal datasets, we show an increase of 5% for the...
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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,... more
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) using pattern recognition techniques. The system developed in this study includes data acquisition, data processing, data fusion, and artificial intelligence methods. Artificial Neural Networks (ANN) are included in artificial intelligence methods, which are used in this study for the recognition of ADL. The purpose of this study is the creation of a new method using ANN for the identification of ADL, comparing three types of ANN, in order to achieve results with a reliable accuracy. The best accuracy was obtained with Deep Learning, which, after the application of the L2 regularization and normalization techniques on the sensors data, report...
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This raw dataset presents the data related to the sleeping, watching TV, and driving captured with the data acquired from accelerometer, gyroscope, magnetometer, location, and environment available in off-the-shelf mobile devices.
These questionnaires were available in the mobile application named as CoviHealth. They are distributed at the beginning of the study, every week to evaluate the knowledge, and, finally, to evaluate the satisfaction.
Artificial intelligence techniques are now being applied in different medical solutions ranging from disease screening to activity recognition and computer-aided diagnosis. The combination of computer science methods and medical knowledge... more
Artificial intelligence techniques are now being applied in different medical solutions ranging from disease screening to activity recognition and computer-aided diagnosis. The combination of computer science methods and medical knowledge facilitates and improves the accuracy of the different processes and tools. Inspired by these advances, this paper performs a literature review focused on state-of-the-art glaucoma screening, segmentation, and classification based on images of the papilla and excavation using deep learning techniques. These techniques have been shown to have high sensitivity and specificity in glaucoma screening based on papilla and excavation images. The automatic segmentation of the contours of the optic disc and the excavation then allows the identification and assessment of the glaucomatous disease’s progression. As a result, we verified whether deep learning techniques may be helpful in performing accurate and low-cost measurements related to glaucoma, which m...
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The tremendous applications of human activity recognition are surging its span from health monitoring systems to virtual reality applications. Thus, the automatic recognition of daily life activities has become significant for numerous... more
The tremendous applications of human activity recognition are surging its span from health monitoring systems to virtual reality applications. Thus, the automatic recognition of daily life activities has become significant for numerous applications. In recent years, many datasets have been proposed to train the machine learning models for efficient monitoring and recognition of human daily living activities. However, the performance of machine learning models in activity recognition is crucially affected when there are incomplete activities in a dataset, i.e., having missing samples in dataset captures. Therefore, in this work, we propose a methodology for extrapolating the missing samples of a dataset to better recognize the human daily living activities. The proposed method efficiently pre-processes the data captures and utilizes the k-Nearest Neighbors (KNN) imputation technique to extrapolate the missing samples in dataset captures. The proposed methodology elegantly extrapolate...
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This raw dataset presents the data related to the library, and street environments captured with the data acquired from microphone available in off-the-shelf mobile devices.
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This paper presents a PhD project related to the use of multi-sensor data fusion techniques, applied to the sensors embedded in mobile devices, as a mean to identify user’s daily activities. It introduces some basic concepts, such as the... more
This paper presents a PhD project related to the use of multi-sensor data fusion techniques, applied to the sensors embedded in mobile devices, as a mean to identify user’s daily activities. It introduces some basic concepts, such as the definition of activities of daily living, mobile platforms/sensors, multisensor technologies, data fusion, and data imputation. These techniques have already been applied to fuse the data acquired with different sensors, but due to memory constraints, battery life and processing power of these devices, not all the techniques are suited to be used in these environments. This paper explains an overview about the state of the research in this topic, explaining the methodology to create a best effort method to recognize a large number of activities of daily living using a mobile device.
This dataset presents the data related to the walking, running, standing, walking upstairs, and walking downstairs captured with accelerometer, gyroscope and magnetometer sensors available in off-the-shelf mobile devices. This data was... more
This dataset presents the data related to the walking, running, standing, walking upstairs, and walking downstairs captured with accelerometer, gyroscope and magnetometer sensors available in off-the-shelf mobile devices. This data was processed with K-Nearest Neighbour (KNN) algorithm.
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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... more
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 ...
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This dataset includes accelerometer data related to institutionalized elderly people from Centre of Portugal during the performance of Sit-to-Stand Test.
Research Interests: Psychology and Mendeley
Stress is or could be one of the most talked-about and recurring things in recent years, because of the world that we live. Stress is our body's response to a pressure thing or situation in our life. In this way, countless things have... more
Stress is or could be one of the most talked-about and recurring things in recent years, because of the world that we live. Stress is our body's response to a pressure thing or situation in our life. In this way, countless things have a stressful impact on our lives. On the other hand, stress is usually due to something new or unexpected, that somehow is beyond our control. The effects on our body are evident, inevitably having symptoms. When we are exposed to stress, certain hormones are released in our body, and the immune system is working on self-defence. During this, breathing becomes faster, heart rate increases, muscles contract and blood pressure also increases. Thus, the organism is ready to act to protect itself. It is where our project comes in, because, with these symptoms of our body, they allow stress to be identified. This paper is focused on precisely that, because, by reading the person's vital data, we can establish standards of normality, which, when they ...
Research Interests: Computer Science and IEEE
This dataset includes the accelerometer data recorded by the accelerometer of a mobile phone and the accelerometer data recorded by BioPlux device at the same time.
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Data collected with a Smartphone equipped with accelerometer and magnetometer sensors, and a BITalino device with ECG and EEG sensors.
The accelerometer data were acquired with two different mobile devices during the 30-s Chair Stand Test with forty-two Portuguese community-dwelling older adults.
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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... more
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 peopl...
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The mobile devices caused a constant struggle for the pursuit of data privacy. Nowadays, it appears that the number of mobile devices in the world is increasing. With this increase and technological evolution, thousands of data associated... more
The mobile devices caused a constant struggle for the pursuit of data privacy. Nowadays, it appears that the number of mobile devices in the world is increasing. With this increase and technological evolution, thousands of data associated with everyone are generated and stored remotely. Thus, the topic of data privacy is highlighted in several areas. There is a need for control and management of data in circulation inherent to this theme. This article presents an approach of the interaction between the individual and the public environment, where this interaction will determine the access to information. This analysis was based on a data privacy management model in public environments created after reading and analyzing the current technologies. A mobile application based on location via Global Positioning System (GPS) was created to substantiate this model, which it considers the General Data Protection Regulation (GDPR) to control and manage access to the data of each individual.
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Most mobile devices include motion, magnetic, acoustic, and location sensors. They allow the implementation of a framework for the recognition of Activities of Daily Living (ADL) and its environments, composed by the acquisition,... more
Most mobile devices include motion, magnetic, acoustic, and location sensors. They allow the implementation of a framework for the recognition of Activities of Daily Living (ADL) and its environments, composed by the acquisition, processing, fusion, and classification of data. This study compares different implementations of artificial neural networks, concluding that the obtained results were 85.89% and 100% for the recognition of standard ADL. Additionally, for the identification of standing activities with Deep Neural Networks (DNN) respectively, and 86.50% for the identification of the environments with Feedforward Neural Networks. Numerical results illustrate that the proposed framework can achieve robust performance from the data fusion of off-the-shelf mobile devices.
Research Interests:
Excessive sun exposure during childhood increases the risk of skin cancer. This study characterized the knowledge and attitudes of caregivers regarding exposure and sun protection of children and adolescents. One hundred and ninety-eight... more
Excessive sun exposure during childhood increases the risk of skin cancer. This study characterized the knowledge and attitudes of caregivers regarding exposure and sun protection of children and adolescents. One hundred and ninety-eight caregivers (38.5 ± 8.0 years) who resorted to the consultations of a Pediatrics Service or a Family Health Unit answered a questionnaire. The age of children/adolescents was 6.5 ± 5.1 years. On average, caregivers presented a high level of knowledge regarding exposure and sun protection. One hundred and twenty-six caregivers indicated that they obtained more information about sun protection on social communication, and 66% considered the information provided by healthcare professionals to be enough. One-hundred and fifteen caregivers reported that the child/adolescent was more exposed to the sun after 4 a.m., and 88% reported applying sunscreen on the beach/pool and outdoor activities. Fifty-seven percent of caregivers renewed sunscreen application ...
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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... more
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, durati...
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Sensors available in a mobile device, e.g., a smartphone, a smartwatch, or others, allow the capture of several signals, that may be used to the estimation of the energy expenditure. This paper describes the adaption of a previous... more
Sensors available in a mobile device, e.g., a smartphone, a smartwatch, or others, allow the capture of several signals, that may be used to the estimation of the energy expenditure. This paper describes the adaption of a previous research, using different signals and validated with a golden standard, consisting in the comparison between the units of the data acquired by a tri-axial accelerometer and an electromyography signal and the data collected by a mobile device accelerometer. The validation of the system showed that the energy expenditure may not be as correct as expected. The data related to this research is available in an open repository and the platform is available for testing. The creation of a validated method for the measurement of energy expenditure during physical activities capable for the implementation in a mobile application is an important issue to increase the confidence of the mobile applications in this market area.
Research Interests: Computer Science and IEEE
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... more
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 innov...
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Fitness and sport have drawn significant attention in wearable and persuasive computing. Physical activities are worthwhile for health, well-being, improved fitness levels, lower mental pressure and tension levels. Nonetheless, during... more
Fitness and sport have drawn significant attention in wearable and persuasive computing. Physical activities are worthwhile for health, well-being, improved fitness levels, lower mental pressure and tension levels. Nonetheless, during high-power and commanding workouts, there is a high likelihood that physical fitness is seriously influenced. Jarring motions and improper posture during workouts can lead to temporary or permanent disability. With the advent of technological advances, activity acknowledgment dependent on wearable sensors has pulled in countless studies. Still, a fully portable smart fitness suite is not industrialized, which is the central need of today’s time, especially in the Covid-19 pandemic. Considering the effectiveness of this issue, we proposed a fully portable smart fitness suite for the household to carry on their routine exercises without any physical gym trainer and gym environment. The proposed system considers two exercises, i.e., T-bar and bicep curl w...
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Data on diagnosis of infection in the general population are strategic for different applications in the public and private spheres. Among them, the data related to symptoms and people displacement stand out, mainly considering highly... more
Data on diagnosis of infection in the general population are strategic for different applications in the public and private spheres. Among them, the data related to symptoms and people displacement stand out, mainly considering highly contagious diseases. This data is sensitive and requires data privacy initiatives to enable its large-scale use. The search for population-monitoring strategies aims at social tracking, supporting the surveillance of contagions to respond to the confrontation with Coronavirus 2 (COVID-19). There are several data privacy issues in environments where IoT devices are used for monitoring hospital processes. In this research, we compare works related to the subject of privacy in the health area. To this end, this research proposes a taxonomy to support the requirements necessary to control patient data privacy in a hospital environment. According to the tests and comparisons made between the variables compared, the application obtained results that contribu...
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Mindfulness-based interventions (MBIs) are reported by experimental studies as practical approaches to reduce burnout in primary healthcare professionals (PHCP). However, to date, no research has synthesized the evidence to determine the... more
Mindfulness-based interventions (MBIs) are reported by experimental studies as practical approaches to reduce burnout in primary healthcare professionals (PHCP). However, to date, no research has synthesized the evidence to determine the overall effects of MBIs for reducing burnout in PHCP. We conducted a systematic review and meta-analysis to analyze the effects of MBIs to reduce burnout in PHCP. We searched articles in the PubMed/MEDLINE, Web of Science, Cochrane, and Scopus databases from inception to September 2021 using MeSH terms: “mindfulness”, “burnout”, and “primary healthcare”. Two reviewers extracted the data and assessed the risk of bias. We used a random-effects meta-analysis to calculate the standardized mean differences (SMD) and mean differences (MD) with 95% confidence intervals (CI) of emotional exhaustion (EE), depersonalization (DP), and personal accomplishment (PA) domains of burnout. Of 61 records, ten were included (n = 417). Overall, the studies were rated as...
Research Interests: Medicine and Healthcare
The rapid development and widespread use of information and telecommunication technologies do not mitigate, in many situations, information exclusion, nor the physical isolation of people—mainly that of the elderly living in remote... more
The rapid development and widespread use of information and telecommunication technologies do not mitigate, in many situations, information exclusion, nor the physical isolation of people—mainly that of the elderly living in remote locations, whose mobile network coverage is deficient or non-existent, preventing them from accessing health care, be it routine follow-up procedures or emergencies. Addressing this, we raise the question that guides our study: how can we monitor the elderly’s residence and health conditions, detect falls, and track their movement in the vicinity of their homes in a non-intrusive manner? To answer this question, we present a system prototype that uses affordable, low-cost, and low-energy equipment with media and data processing, supported by LoRa (Long Range) and ESP32 microcontrollers, coupling several sensors. As a result, it is possible to monitor sensors that predict and detect falls or other risk events for the user, e.g., fire, with authorized perso...
Research Interests: Business and Electronics
Connected health is expected to introduce an improvement in providing healthcare and doctor-patient communication while at the same time reducing cost. Connected health would introduce an even more significant gap between healthcare... more
Connected health is expected to introduce an improvement in providing healthcare and doctor-patient communication while at the same time reducing cost. Connected health would introduce an even more significant gap between healthcare quality for urban areas with physical proximity and better communication to providers and the portion of rural areas with numerous connectivity issues. We identify these challenges using user scenarios and propose LoRa based architecture for addressing these challenges. We focus on the energy management of battery-powered, affordable IoT devices for long-term operation, providing important information about the care receivers’ well-being. Using an external ultra-low-power timer, we extended the battery life in the order of tens of times, compared to relying on low power modes of the microcontroller.
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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... more
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 t...
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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... more
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...
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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... more
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 contex...
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The recognition of Activities of Daily Living (ADL) using the sensors available in off-the-shelf mobile devices with high accuracy is significant for the development of their framework. Previously, a framework that comprehends data... more
The recognition of Activities of Daily Living (ADL) using the sensors available in off-the-shelf mobile devices with high accuracy is significant for the development of their framework. Previously, a framework that comprehends data acquisition, data processing, data cleaning, feature extraction, data fusion, and data classification was proposed. However, the results may be improved with the implementation of other methods. Similar to the initial proposal of the framework, this paper proposes the recognition of eight ADL, e.g., walking, running, standing, going upstairs, going downstairs, driving, sleeping, and watching television, and nine environments, e.g., bar, hall, kitchen, library, street, bedroom, living room, gym, and classroom, but using the Instance Based k-nearest neighbour (IBk) and AdaBoost methods as well. The primary purpose of this paper is to find the best machine learning method for ADL and environment recognition. The results obtained show that IBk and AdaBoost re...
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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... more
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 perspec...
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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... more
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 ...
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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,... more
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...
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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... more
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 incre...
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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... more
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....
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The mobile devices cause a constant struggle for the pursuit of data privacy. Nowadays, it appears that the number of mobile devices in the world is increasing. With this increase and technological evolution, thousands of data associated... more
The mobile devices cause a constant struggle for the pursuit of data privacy. Nowadays, it appears that the number of mobile devices in the world is increasing. With this increase and technological evolution, thousands of data associated with everyone are generated and stored remotely. Thus, the topic of data privacy is highlighted in several areas. There is a need for control and management of data in circulation inherent to this theme. This article presents an approach to the interaction between the individual and the public environment, where this interaction will determine the access to information. This analysis was based on a data privacy management model in open environments created after reading and analyzing the current technologies. A mobile application based on location by Global Positioning System (GPS) was developed to substantiate this model, which considers the General Data Protection Regulation (GDPR) to control and manage access to each individual’s data.
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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... more
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 ...
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The analysis of movements used in physiotherapy areas related to the elderly is becoming increasingly important due to factors such as the increase in the average life expectancy and the rate of elderly people over the whole population.... more
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.
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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... more
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-ar...
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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... more
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 normaliza...
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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... more
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 ...
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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... more
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 conn...
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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... more
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.
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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... more
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...
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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... more
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 ...
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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.... more
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-perceive...
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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,... more
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 motio...
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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... more
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 m...
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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... more
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 e...
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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... more
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 Centr...
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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... more
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, a...
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The automatic recognition of Activities of Daily Living (ADL) with a multi-sensor mobile device that can acquire different types of sensors' data, and rely on the use of machine learning methods to handle the recognition of ADL with... more
The automatic recognition of Activities of Daily Living (ADL) with a multi-sensor mobile device that can acquire different types of sensors' data, and rely on the use of machine learning methods to handle the recognition of ADL with reliable accuracy. This paper focuses on the literature review of the existing methods to make the identification of ADL in order to assess the efficiency of the different methods for the identification of ADL and their environments using off-the-shelf mobile devices. Data acquired from several sensors can be used for the identification of ADL, where the motion, magnetic and location sensors handle the recognition of activities with movement, and the acoustic sensors handle the recognition of activities related with the environment. Therefore, the main purpose of this study is to present a review of the machine learning methods already used on this field, relating them with the accuracy and number of ADL recognized.
This paper presents iAirBot, an assistive robot for indoor air quality monitoring based on Internet of Things. The system can communicate with occupants and triggers alerts automatically using social networks. The information can be... more
This paper presents iAirBot, an assistive robot for indoor air quality monitoring based on Internet of Things. The system can communicate with occupants and triggers alerts automatically using social networks. The information can be accessed by the caregiver to plan interventions for enhanced living environments in a timely manner. The results are promising, as the proposed architecture presents a cost-effective assistive robot for indoor quality monitoring. It connects several technological fields and knowledge areas, such as ambient assisted living, Internet of Things, wireless sensor networks, social networks, and indoor air quality. When compared to other systems, iAirBot stands out for the modularity and scalability of its sensors network, as well as the use of social networks for information sharing. Therefore, iAirBot is a significant system for enhanced living environments, occupational health, and well-being.
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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... more
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 v...
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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,... more
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) using pattern recognition techniques. The system developed in this study includes data acquisition, data processing, data fusion, and artificial intelligence methods. Artificial Neural Networks (ANN) are included in artificial intelligence methods, which are used in this study for the recognition of ADL. The purpose of this study is the creation of a new method using ANN for the identification of ADL, comparing three types of ANN, in order to achieve results with a reliable accuracy. The best accuracy was obtained with Deep Learning, which, after the application of the L2 regularization and normalization techniques on the sensors’ data, repor...
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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... more
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 r...
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Background:Off-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... more
Background:Off-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:T...
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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... more
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.
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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... more
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 ...
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Mobile applications have become a must in every user’s smart device, and many of these applications make use of the device sensors’ to achieve its goal. Nevertheless, it remains fairly unknown to the user to which extent the data the... more
Mobile applications have become a must in every user’s smart device, and many of these applications make use of the device sensors’ to achieve its goal. Nevertheless, it remains fairly unknown to the user to which extent the data the applications use can be relied upon and, therefore, to which extent the output of a given application is trustworthy or not. To help developers and researchers and to provide a common ground of data validation algorithms and techniques, this paper presents a review of the most commonly used data validation algorithms, along with its usage scenarios, and proposes a classification for these algorithms. This paper also discusses the process of achieving statistical significance and trust for the desired output.
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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... more
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).
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Due to higher life expectancy, the number of older people continues to increase, and with it the number of cases of chronic diseases. Estimates indicate that the percentage of people with at least one chronic disease living in modern... more
Due to higher life expectancy, the number of older people continues to increase, and with it the number of cases of chronic diseases. Estimates indicate that the percentage of people with at least one chronic disease living in modern societies can reach as much as 40%, making chronic diseases one of the major challenges for modern healthcare systems. In order to reduce healthcare costs, solutions based on information and communication technologies have emerged. The expansion of e-Health solutions is associated with the increased demand for flexible, comprehensive, and cost-effective chronic care models, and continues expanding, putting together a very comprehensive set of knowledge. This chapter presents an inclusive and widespread current state of the art of e-health solutions for chronic diseases, proposing a number of predictable future trends and scenarios.
This dataset includes accelerometer data related to institutionalized elderly people from Centre of Portugal during the performance of 30-s Chair Stand Test.