Unobtrusive Cognitive Assessment in Smart-Homes: Leveraging Visual Encoding and Synthetic Movement Traces Data Mining.

The ubiquity of sensors in smart-homes facilitates the support of independent living for older adults and enables cognitive assessment. Notably, there has been a growing interest in utilizing movement traces for identifying signs of cognitive impairment in recent years. In this study, we introduce an innovative approach to identify abnormal indoor movement patterns that may signal cognitive decline.

PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points.

Photoplethysmography is a non-invasive technique used for measuring several vital signs and for the identification of individuals with an increased disease risk. Its principle of work is based on detecting changes in blood volume in the microvasculature of the skin through the absorption of light. The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task, where various feature extraction methods have been proposed in the literature.

State of the Art of Non-Invasive Technologies for Bladder Monitoring: A Scoping Review.

Bladder monitoring, including urinary incontinence management and bladder urinary volume monitoring, is a vital part of urological care. Urinary incontinence is a common medical condition affecting the quality of life of more than 420 million people worldwide, and bladder urinary volume is an important indicator to evaluate the function and health of the bladder. Previous studies on non-invasive techniques for urinary incontinence management technology, bladder activity and bladder urine volume monitoring have been conducted.

Effects of flavonoids on membrane adaptation of food-associated bacteria.

The effects of naringenin and the biflavonoids amentoflavone and tetrahydroamentoflavone on select bacterial lipids (carotenoids, fatty acids, and menaquinones) and membrane fluidity based on Laurdan generalized polarization were investigated. For this purpose, the pigment-forming food-associated microorganisms Staphylococcus xylosus (DSM 20266 and J70), Staphylococcus carnosus DSM 20501, and Micrococcus luteus (ATCC 9341 and J3) were studied. The results suggest an envelope stress response by microorganisms due to flavonoids and an employment of adaptive mechanisms using carotenoids, fatty acids, and menaquinones.

Automatic lesion segmentation using atrous convolutional deep neural networks in dermoscopic skin cancer images.

Background: Melanoma is the most dangerous and aggressive form among skin cancers, exhibiting a high mortality rate worldwide. Biopsy and histopathological analysis are standard procedures for skin cancer detection and prevention in clinical settings. A significant step in the diagnosis process is the deep understanding of the patterns, size, color, and structure of lesions based on images obtained through dermatoscopes for the infected area.

Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images.

Automatic melanoma detection from dermoscopic skin samples is a very challenging task. However, using a deep learning approach as a machine vision tool can overcome some challenges. This research proposes an automated melanoma classifier based on a deep convolutional neural network (DCNN) to accurately classify malignant vs.

A Systematic Review of Wearable Sensors for Monitoring Physical Activity.

This article reviews the use of wearable sensors for the monitoring of physical activity (PA) for different purposes, including assessment of gait and balance, prevention and/or detection of falls, recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoring of neurological disease progression. The article provides in-depth information on the retrieved articles and discusses study shortcomings related to demographic factors, i.e.

Evaluation of a Series of Overdentures Made in a Higher Education Dental Institution in Southern Brazil.

Aim And Objective: This study aimed to evaluate the epidemiological profile, oral health self-perception index, and level of satisfaction of users of complete implant-supported overdentures that had been used for at least 1 year and were made at the School of Dentistry of the University of Passo Fundo, Rio Grande do Sul, Brazil (FO/UPF), between 2014 and 2019.

Materials And Methods: The sample consisted of 30 patients with overdentures, who were selected from the dental records filed at the institution. Data on general health and the dental implants involved (brand, type of prosthetic connection, number of implants, and additional overdenture retention system) were collected from the medical records.

Evaluation of the Genotoxicity of Tobacco and Alcohol in Oral Mucosa Cells: A Pilot Study.

Aim And Objective: To define the genotoxic potential of tobacco and alcohol in the oral mucosa through a micronuclei (MN) test.

Materials And Methods: Samples of exfoliative cells from oral mucosa were collected using superficial scraping of the right- and left-cheek mucosa of 83 patients divided into four groups, namely: (G1) 24 individuals abstaining from tobacco and alcoholic beverages; (G2) 23 individuals who smoke and abstain from alcoholic beverages; (G3) 24 smokers and alcoholics; and (G4) 12 individuals who consume alcohol and abstain from tobacco. The samples were stained with Giemsa-Wright, and the frequencies of MN, binucleated cells, and metanuclear changes were recorded in the samples of each group (1,000 cells per patient).

Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements.

Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.

Quality of life of patients following orthognathic surgery.

The aim of this study was to evaluate how patients with dentofacial deficiency who have undergone orthognathic surgery perceive their quality of life (QoL) with respect to functional, esthetic, and psychosocial issues. In an observational, cross-sectional, descriptive, and quantitative study, 10 patients who had undergone orthognathic surgery answered questionnaires used internationally for assessing QoL: the Short Form Health Survey (SF-36), Oral Health Impact Profile 14 (OHIP-14), and Orthognathic Quality of Life Questionnaire (OQLQ). In addition, the patients completed the Self-Perception Questionnaire of Esteem, Appearance, and Interpersonal Relationships (ASR-26), which explored the differences between their current self-esteem, appearance satisfaction, and interpersonal relationships and their memories of their presurgical feelings about those topics.

A systematic review of rapid response applications based on early warning score for early detection of inpatient deterioration.

Aim: The aim of this study was to investigate the effectiveness of current rapid response applications available in acute care settings for escalation of patient deterioration. Current challenges and barriers, as well as key recommendations, were also discussed.

Methods: We adopted PRISMA review methodology and screened a total of 559 articles.

Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications.

Background: Prediabetes and type 2 diabetes mellitus (T2DM) are one of the major long-term health conditions affecting global healthcare delivery. One of the few effective approaches is to actively manage diabetes via a healthy and active lifestyle.

Objectives: This research is focused on early detection of prediabetes and T2DM using wearable technology and Internet-of-Things-based monitoring applications.

A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition.

Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activity Recognition (HAR) is one of the fields that has seen an explosive research interest among the ML community due to its wide range of applications. HAR is one of the most helpful technology tools to support the elderly's daily life and to help people suffering from cognitive disorders, Parkinson's disease, dementia, etc.

Oral leukoplakia-epidemiological survey and histochemical analysis of 107 cases in Brazil.

Objective: To perform an epidemiological survey comparing the cell proliferative activity of 107 cases of oral leukoplakia with their clinical and histopathological characteristics.

Materials And Methods: A cross-sectional, observational, and histological-histochemical study. The cases came from the Histopathological Diagnostic Service of UPF/RS and the School of Dentistry of Araçatuba FOA/UNESP/SP (1986-2016).

Effects of structural differences on the antibacterial activity of biflavonoids from fruits of the Brazilian peppertree (Schinus terebinthifolius Raddi).

Flavonoids, synthesized by plants across all families and therefore found in a huge variety, possess a diverse range of pharmacological properties. Direct antibacterial and synergistic activities as well as the inhibition of several bacterial virulence factors are known. Besides the mode of action, it is important to understand the structure-activity relationship to identify key structural characteristics.

A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis.

The use of wearable body sensors for health monitoring is a quickly growing field with the potential of offering a reliable means for clinical and remote health management. This includes both real-time monitoring and health trend monitoring with the aim to detect/predict health deterioration and also to act as a prevention tool. The aim of this systematic review was to provide a qualitative synthesis of studies using wearable body sensors for health monitoring.

Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes.

As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods. While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system.

Evaluation of surface EMG-based recognition algorithms for decoding hand movements.

Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses.

Estimating Systolic Blood Pressure Using Convolutional Neural Networks.

Continuous blood pressure (BP) monitoring can produce a significant amount of digital data, which increases the chance of early diagnosis and improve the rate of survival for people diagnosed with hypertension and Cardiovascular diseases (CVDs). However, mining and processing this vast amount of data are challenging. This research is aimed to address this challenge by proposing a deep learning technique, convolutional neural network (CNN), to estimate the systolic blood pressure (SBP) using electrocardiogram (ECG) and photoplethysmography (PPG) signals.

A Novel Medical Device for Early Detection of Melanoma.

Melanoma is the deadliest form of skin cancer. Early detection of melanoma is vital, as it helps in decreasing the death rate as well as treatment costs. Dermatologists are using image-based diagnostic tools to assist them in decision-making and detecting melanoma at an early stage.

Obesity Risk Assessment Model Using Wearable Technology with Personalized Activity, Calorie Expenditure and Health Profile.

There is a worldwide increase in the rate of obesity and its related long-term conditions, emphasizing an immediate need to address this modern-age global epidemic of healthy living. Moreover, healthcare spending on long-term or chronic care conditions such as obesity is increasing to the point that requires effective interventions and advancements to reduce the burden of the healthcare. This research focuses on the early risk assessment of overweight/obesity using wearable technology.

Morse taper performance: A finite element analysis study.

Objectives: To evaluate and compare the magnitude and distribution of stresses generated on implants, abutments and first molar metal-ceramic crowns using finite element analysis.

Methods: Preliminary three-dimensional models were created using the computer-aided design software SolidWorks. Stress and strain values were observed for two distinct virtual models: model 1 - Morse taper and solid abutment; model 2 - Morse taper and abutment with screw.

A Comparison of Torque Stress on Abutment Screw of External Hexagon and Morse Taper Implant.

Aim: This three-dimensional (3D) finite element analysis observed the stress distribution on the prosthetic screws on external hexagon implant and morse taper implant with different tightening loads.

Materials And Methods: For this, two different 3D models assembly were obtained from the manufacturer and transferred to finite element analysis software: External hex implant model (EHM), and Morse taper implant model (MTM), both compounds by 3.75 × 7 mm implant, abutment and abutment screw.