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. This systematic review was performed in various scientific databases or publishers, including PubMed Central, IEEE Explore, Elsevier, Springer, the Multidisciplinary Digital Publishing Institute (MDPI), and the Association for Computing Machinery (ACM), and considered studies published between January 2017 and October 2022, related to methods for the automation of the measurement of the Functional Reach Test variables and results with sensors. Camera-based devices and motion-based sensors are used for Functional Reach Tests, with statistical models extracting meaningful information. Sensor-based systems offer several advantages over traditional manual measurement techniques, as they can provide objective and precise measurements of the reach distance, quantify postural sway, and capture additional parameters related to the movement.

Controller-driven vector autoregression model for predicting content popularity in programmable named data networking devices.

Named Data Networking (NDN) has emerged as a promising network architecture for content delivery in edge infrastructures, primarily due to its name-based routing and integrated in-network caching. Despite these advantages, sub-optimal performance often results from the decentralized decision-making processes of caching devices. This article introduces a paradigm shift by implementing a Software Defined Networking (SDN) controller to optimize the placement of highly popular content in NDN nodes. The optimization process considers critical networking factors, including network congestion, security, topology modification, and flowrules alterations, which are essential for shaping content caching strategies. The article presents a novel content caching framework, Popularity-aware Caching in Popular Programmable NDN nodes (PaCPn). Employing a multi-variant vector autoregression (VAR) model driven by an SDN controller, PaCPn periodically updates content popularity based on time-series data, including 'request rates' and 'past popularity'. It also introduces a controller-driven heuristic algorithm that evaluates the proximity of caching points to consumers, considering factors such as 'distance cost,' 'delivery time,' and the specific 'status of the requested content'. PaCPn utilizes customized DATA named packets to ensure the source stores content with a valid residual freshness period while preventing intermediate nodes from caching it. The experimental results demonstrate significant improvements achieved by the proposed technique PaCPn compared to existing schemes. Specifically, the technique enhances cache hit rates by 20% across various metrics, including cache size, Zipf parameter, and exchanged traffic within edge infrastructure. Moreover, it reduces content retrieval delays by 28%, considering metrics such as cache capacity, the number of consumers, and network throughput. This research advances NDN content caching and offers potential optimizations for edge infrastructures.

Mobile Data Gathering and Preliminary Analysis for the Functional Reach Test.

The functional reach test (FRT) is a clinical tool used to evaluate dynamic balance and fall risk in older adults and those with certain neurological diseases. It provides crucial information for developing rehabilitation programs to improve balance and reduce fall risk. This paper aims to describe a new tool to gather and analyze the data from inertial sensors to allow automation and increased reliability in the future by removing practitioner bias and facilitating the FRT procedure. A new tool for gathering and analyzing data from inertial sensors has been developed to remove practitioner bias and streamline the FRT procedure. The study involved 54 senior citizens using smartphones with sensors to execute FRT. The methods included using a mobile app to gather data, using sensor-fusion algorithms like the Madgwick algorithm to estimate orientation, and attempting to estimate location by twice integrating accelerometer data. However, accurate position estimation was difficult, highlighting the need for more research and development. The study highlights the benefits and drawbacks of automated balance assessment testing with mobile device sensors, highlighting the potential of technology to enhance conventional health evaluations.

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. The experiments were performed two times by 109 individuals (30 males and 79 females) in different senior residences in the Fundão municipality (Portugal). The dataset includes 208 samples because the sensors reported some failures. The acquisition of the sensors data allows the creation of a technological method for the automatic measurement of features related to the Ten Meter Walk Test, promoting patient independence in measuring their physical health status.

Normalized effect size (NES): a novel feature selection model for Urdu fake news classification.

Social media has become an essential source of news for everyday users. However, the rise of fake news on social media has made it more difficult for users to trust the information on these platforms. Most research studies focus on fake news detection in the English language, and only a limited number of studies deal with fake news in resource-poor languages such as Urdu. This article proposes a globally weighted term selection approach named normalized effect size (NES) to select highly discriminative features for Urdu fake news classification. The proposed model is based on the traditional inverse document frequency (TF-IDF) weighting measure. TF-IDF transforms the textual data into a weighted term-document matrix and is usually prone to the curse of dimensionality. Our novel statistical model filters the most discriminative terms to reduce the data's dimensionality and improve classification accuracy. We compare the proposed approach with the seven well-known feature selection and ranking techniques, namely normalized difference measure (NDM), bi-normal separation (BNS), odds ratio (OR), GINI, distinguished feature selector (DFS), information gain (IG), and Chi square (Chi). Our ensemble-based approach achieves high performance on two benchmark datasets, BET and UFN, achieving an accuracy of 88% and 90%, respectively.

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. We applied the PRISMA methodology for searching the papers related to the Ten Meter Walk Test. Natural Language Processing (NLP) algorithms were used to automate the screening process. Various papers published in two decades from multiple scientific databases, including IEEE Xplore, Elsevier, Springer, EMBASE, SCOPUS, Multidisciplinary Digital Publishing Institute (MDPI), and PubMed Central were analyzed, focusing on various diseases, devices, features, and methods. The study reveals that chronometer and accelerometer sensors measuring spatiotemporal features are the most pertinent in the Gait characterization of most diseases. Likewise, all studies emphasized the close relation between the quality of the sensor's data obtained and the system's ultimate accuracy. In other words, calibration procedures are needed because of the body part where the sensor is worn and the type of sensor. In addition, using ambient sensors providing kinematic and kinetic features in conjunction with wearable sensors and consistently acquiring walking signals can enhance the system's performance. The most common weaknesses in the analyzed studies are the sample size and the unavailability of continuous monitoring devices for measuring the Ten Meter Walk Test.

Estimating the relative load from movement velocity in the seated chest press exercise in older adults.

AIM: This study aimed to i) determine the load-velocity relationship in the seated chest press in older adults, ii) compare the magnitude of the relationship between peak and mean velocity with the relative load, and iii) analyze the differences between sexes in movement velocity for each relative load in the chest press. MATERIAL AND METHODS: Thirty-two older adults (17 women and 15 men; 79.6±7.7 years) performed a chest press progressive loading test up to the one-repetition maximum (1RM). The fastest peak and mean velocity reached with each weight were analyzed. Quadratic equations were developed for both sexes and the effectiveness of the regression model was analyzed through a residual analysis. The equations were cross-validated, considering the holdout method. The independent samples t-test analyzed i) the differences in the magnitude of the relationship between peak and mean velocity with the relative load and ii) the differences between sexes in the peak and mean velocity for each relative load. RESULTS: It was possible to observe very strong quadratic load-velocity relationships in the seated chest press in women (peak velocity: r2 = 0.97, standard error of the estimate (SEE) = 4.5% 1RM; mean velocity: r2 = 0.96, SEE = 5.3% 1RM) and men (peak velocity: r2 = 0.98, SEE = 3.8% 1RM; mean velocity: r2 = 0.98, SEE = 3.8% 1RM) without differences (p>0.05) in the magnitude of the relationship between peak and mean velocity with the relative load. Furthermore, there was no overfitting in the regression models due to the high and positive correlation coefficients (r = 0.98-0.99). Finally, men presented higher (p<0.001) lifting velocities than women in almost all relative loads, except for 95-100% 1RM (p>0.05). CONCLUSION: Measuring repetition velocity during the seated chest press is an objective approach to estimating the relative load in older adults. Furthermore, given the velocity differences between older women and men at submaximal loads, it is recommended to use sex-specific equations to estimate and prescribe the relative loads in older adults.

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. We invite researchers to submit their best work to the collection and to showcase the latest advancements and additions to research techniques.

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.2022.108874.].

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. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient's autonomy.

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. The data was collected from 219 individuals (112 men, 106 women, and one other) in different environments, but they are in the Covilhã municipality. The dataset includes the 219 recordings and corresponds to the sensors' recordings of a 30 s sitting and a 30 s standing test, which checks to approximately 1 min for each one. This dataset includes 3.7 h (approximately) of recordings for further analysis with data processing techniques and machine learning methods. It will be helpful for the complementary creation of a robust method for identifying the characteristics of individuals related to Electrocardiography signals.

Load-power relationship in older adults: The influence of maximal mean and peak power values and their associations with lower and upper-limb functional capacity.

Identifying the relative loads (%1RM) that maximize power output (Pmax-load) in resistance exercises can help design interventions to optimize muscle power in older adults. Moreover, examining the maximal mean power (MPmax) and peak power (PPmax) values (Watts) would allow an understanding of their differences and associations with functionality markers in older adults. Therefore, this research aimed to 1) analyze the load-mean and peak power relationships in the leg press and chest press in older adults, 2) examine the differences between mean Pmax-load (MPmax-load) and peak Pmax-load (PPmax-load) within resistance exercises, 3) identify the differences between resistance exercises in MPmax-load and PPmax-load, and 4) explore the associations between MPmax and PPmax in the leg press and chest press with functional capacity indicators. Thirty-two older adults (79.3 ± 7.3 years) performed the following tests: medicine ball throw (MBT), five-repetition sit-to-stand (STS), 10-m walking (10 W), and a progressive loading test in the leg press and chest press. Quadratic regressions analyzed 1) the load-mean and peak power relationships and identified the MPmax-load, MPmax, PPmax-load, and PPmax in both exercises, 2) the associations between MPmax and PPmax in the chest press with MBT, and 3) the associations between MPmax and PPmax in the leg press with STSpower and 10Wvelocity. In the leg press, the MPmax-load was ∼66% 1RM, and the PPmax-load was ∼62% 1RM, both for women and men (p > 0.05). In the chest press, the MPmax-load was ∼62% 1RM, and the PPmax-load was ∼56% 1RM, both for women and men (p > 0.05). There were differences between MPmax-load and PPmax-load within exercises (p < 0.01) and differences between exercises in MPmax-load and PPmax-load (p < 0.01). The MPmax and PPmax in the chest press explained ∼48% and ∼52% of the MBT-1 kg and MBT-3 kg variance, respectively. In the leg press, the MPmax and PPmax explained ∼59% of STSpower variance; however, both variables could not explain the 10Wvelocity performance (r 2 ∼ 0.02). This study shows that the Pmax-load is similar between sexes, is resistance exercise-specific, and varies within exercises depending on the mechanical power variable used in older adults. Furthermore, this research demonstrates the influence of the MBT as an upper-limb power marker in older adults.

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. Therefore, the ability to monitor could be beneficial for them and for the doctors that can analyze the patterns of eating habits and their correlation with overall health. Many sensors help accurately detect food intake episodes, including electrogastrography, cameras, microphones, and inertial sensors. Accurate detection may provide better control to enable healthy nutrition habits. This paper presents a systematic review of the use of technology for food intake detection, focusing on the different sensors and methodologies used. The search was performed with a Natural Language Processing (NLP) framework that helps screen irrelevant studies while following the PRISMA methodology. It automatically searched and filtered the research studies in different databases, including PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Then, the manual analysis selected 30 papers based on the results of the framework for further analysis, which support the interest in using sensors for food intake detection and nutrition assessment. The mainly used sensors are cameras, inertial, and acoustic sensors that handle the recognition of food intake episodes with artificial intelligence techniques. This research identifies the most used sensors and data processing methodologies to detect food intake.

A Convolutional Neural Network-enabled IoT framework to verify COVID-19 hygiene conditions and authorize access to facilities.

COVID-19 has infected several million of individuals while claiming numerous lives. This fact raised the need to apply the measure to prevent its transmission. The use of disinfection products, wearing masks, and avoiding touching doors are important measures to avoid its spread. Thus, this work proposes a framework supported by a Convolutional Neural Network (CNN) model checking the hygienic conditions of the individuals requiring authorization to access facilities. The experimental work takes IoT devices with sensors to check: whether the users have disinfection product in their hands and a trained model to check whether individuals are also wearing masks. The achieved results highlighted the effectiveness of the proposed framework.

Retinal Glaucoma Public Datasets: What Do We Have and What Is Missing?

Public databases for glaucoma studies contain color images of the retina, emphasizing the optic papilla. These databases are intended for research and standardized automated methodologies such as those using deep learning techniques. These techniques are used to solve complex problems in medical imaging, particularly in the automated screening of glaucomatous disease. The development of deep learning techniques has demonstrated potential for implementing protocols for large-scale glaucoma screening in the population, eliminating possible diagnostic doubts among specialists, and benefiting early treatment to delay the onset of blindness. However, the images are obtained by different cameras, in distinct locations, and from various population groups and are centered on multiple parts of the retina. We can also cite the small number of data, the lack of segmentation of the optic papillae, and the excavation. This work is intended to offer contributions to the structure and presentation of public databases used in the automated screening of glaucomatous papillae, adding relevant information from a medical point of view. The gold standard public databases present images with segmentations of the disc and cupping made by experts and division between training and test groups, serving as a reference for use in deep learning architectures. However, the data offered are not interchangeable. The quality and presentation of images are heterogeneous. Moreover, the databases use different criteria for binary classification with and without glaucoma, do not offer simultaneous pictures of the two eyes, and do not contain elements for early diagnosis.

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. This paper presents a systematic review on the use of sensors for measuring functional movements during the execution of the Eight Hop Test, focusing primarily on the use of sensors, related diseases, and different methods implemented. Firstly, an automated search was performed on the publication databases: PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Secondly, the publications related to the Eight-Hop Test and sensors were filtered according to several search criteria and 15 papers were finally selected to be analyzed in detail. Our analysis found that the Eight Hop Test measurements can be performed with motion, force, and imaging sensors.

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. 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 that need to be solved are proposed.

A Systematic Review of Artificial Intelligence Applications Used for Inherited Retinal Disease Management.

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 different IRD patient categories and the most utilized AI architectures and models with their imaging modalities, identifying the main benefits and challenges of using such methods.

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). 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.