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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...