Air pollution data: A dataset gathered through a crowd sensing platform.

This paper introduces an extensive dataset on air pollution monitoring, collected through a crowd sensing IoT platform. The dataset contains real-time measurements of various pollutants, including PM, PM, NO, O, and CO, enriched with meteorological parameters such as temperature, humidity, and atmospheric pressure. Additionally, it includes noise level measurements, offering insights into urban noise pollution.

Applicability Assessment of Technologies for Predictive and Prescriptive Analytics of Nephrology Big Data.

The integration of big data into nephrology research will open new avenues for analyzing and understanding complex biological datasets, driving advances in personalized management of kidney diseases. This paper describes the multifaceted challenges and opportunities by incorporating big data in nephrology, emphasizing the importance of data standardization, advanced storage solutions, and advanced analytical methods. We discuss the role of data science workflows, including data collection, preprocessing, integration, and analysis, in facilitating comprehensive insights into disease mechanisms and patient outcomes.

Ten Meter Walk Test for motor function assessment with technological devices based on lower members' movements: A systematic review.

Objective: The Ten Meter Walk Test (10MWT) is a vital diagnostic tool for identifying neuromuscular and neurodegenerative conditions. This systematic review explores the potential of wearables, mobile devices, and sensors to enhance the 10MWT's use in medical gait analysis based on lower limb movements.

Methods: This systematic review explores the use of wearables, mobile devices, and sensors to improve the 10MWT in medical gait analysis based on lower limb movements.

Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution.

Air pollution constitutes a significant worldwide environmental challenge, presenting threats to both our well-being and the purity of our food supply. This study suggests employing Recurrent Neural Network (RNN) models featuring Long Short-Term Memory (LSTM) units for forecasting PM10 particle levels in multiple locations in Skopje simultaneously over a time span of 1, 6, 12, and 24 hours. Historical air quality measurement data were gathered from various local sensors positioned at different sites in Skopje, along with data on meteorological conditions from publicly available APIs.

Sensor-based systems for the measurement of Functional Reach Test results: a systematic review.

The measurement of Functional Reach Test (FRT) is a widely used assessment tool in various fields, including physical therapy, rehabilitation, and geriatrics. This test evaluates a person's balance, mobility, and functional ability to reach forward while maintaining stability. Recently, there has been a growing interest in utilizing sensor-based systems to objectively and accurately measure FRT results.

Ten meter walk test with mobile devices: A dataset with accelerometer, magnetometer, and gyroscope.

This paper presents a dataset related to the performance of the Ten Meter Walking Test, a test to allow locomotor capacity in different research and clinical settings. One of the most important parameters to measure is the gait speed during a path of ten meters. The data available in this dataset consists of accelerometer, magnetometer, and gyroscope data acquired with a mobile device in a waistband.

Mobile and wearable technologies for the analysis of Ten Meter Walk Test: A concise systematic review.

Physical issues started to receive more attention due to the sedentary lifestyle prevalent in modern culture. The Ten Meter Walk Test allows measuring the person's capacity to walk along 10 m and analyzing the advancement of various medical procedures for ailments, including stroke. This systematic review is related to the use of mobile or wearable devices to measure physical parameters while administering the Ten Meter Walk Test for the analysis of the performance of the test.

Advancing methods in big data capture, integration, classification and liberation.

This special issue focuses on the importance of advancing research techniques for managing and analyzing data in today's data-rich landscape. In this editorial, we set the context and invite contributions for a BMC Collection of articles titled 'Advancing methods in data capture, integration, classification and liberation'. The collection emphasizes the need for efficient ways to standardize, cleanse, integrate, enrich, and liberate data, highlighting recent advancements in research methods and industrial technologies that facilitate this.

Corrigendum to "Extraction of notable points from ECG data: A description of a dataset related to 30-s seated and 30-s stand up" [Data in Brief, volume 46 (2023) 108874].

[This corrects the article DOI: 10.1016/j.dib.

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review.

The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments.

Extraction of notable points from ECG data: A description of a dataset related to 30-s seated and 30-s stand up.

It is increasingly possible to acquire Electrocardiographic data with featured low-cost devices. The proposed dataset will help map different signals for various diseases related to Electrocardiography data. The dataset presented in this paper is related to the acquisition of electrocardiography data during the standing up and seated positions.

Correction: Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns.

[This corrects the article DOI: 10.2196/36553.].

Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns.

Background: Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)-infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work.

Thought on Food: A Systematic Review of Current Approaches and Challenges for Food Intake Detection.

Nowadays, individuals have very stressful lifestyles, affecting their nutritional habits. In the early stages of life, teenagers begin to exhibit bad habits and inadequate nutrition. Likewise, other people with dementia, Alzheimer's disease, or other conditions may not take food or medicine regularly.

Can the Eight Hop Test Be Measured with Sensors? A Systematic Review.

Rehabilitation aims to increase the independence and physical function after injury, surgery, or other trauma, so that patients can recover to their previous ability as much as possible. To be able to measure the degree of recovery and impact of the treatment, various functional performance tests are used. The Eight Hop Test is a hop exercise that is directly linked to the rehabilitation of people suffering from tendon and ligament injuries on the lower limb.

A Comprehensive Review of Methods and Equipment for Aiding Automatic Glaucoma Tracking.

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.

Daily motionless activities: A dataset with accelerometer, magnetometer, gyroscope, environment, and GPS data.

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).

GAN-Based Image Colorization for Self-Supervised Visual Feature Learning.

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.

Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification.

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.

Development Technologies for the Monitoring of Six-Minute Walk Test: A Systematic Review.

In the pandemic time, the monitoring of the progression of some diseases is affected and rehabilitation is more complicated. Remote monitoring may help solve this problem using mobile devices that embed low-cost sensors, which can help measure different physical parameters. Many tests can be applied remotely, one of which is the six-minute walk test (6MWT).

Mobile 5P-Medicine Approach for Cardiovascular Patients.

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.

Experimental Study on Wound Area Measurement with Mobile Devices.

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.

Rural Healthcare IoT Architecture Based on Low-Energy LoRa.

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.

Indoor and outdoor environmental data: A dataset with acoustic data acquired by the microphone embedded on mobile devices.

All mobile devices include a microphone that can be used for acoustic data acquisition. This article presents a dataset of acoustic signals related to nine environments, captured with a microphone embedded on off-the-shelf mobile devices. The mobile phone can be placed in the pants pockets, in a wristband, over the bedside table, on a table, or on other furniture.