Search

BackgroundMental health has become one of the fundamental priorities during the COVID-19 pandemic. Situations like physical distancing as well as being constantly tagged as the most vulnerable group could expose older adults to mental and psychosocial burdens. Nonetheless, there is little clarity about the impact of the COVID-19 pandemic or similar pandemics in the past on the mental illness, wellbeing, and psychosocial health of the older population compared to other age groups.ObjectivesTo describe the patterns of older adults’ mental and psychosocial health related to acute respiratory disease propagated-type epidemics and pandemics and to evaluate the differences with how other age groups respond.Eligibility criteriaquantitative and qualitative studies evaluating mental illness, wellbeing, or psychosocial health outcomes associated with respiratory propagated epidemics and pandemics exposure or periods (COVID-19, SARS-CoV, MERS, and Influenza) in people 65 years or older.Data sourceOriginal articles published until June 1st, 2020, in any language searched in the electronic healthcare and social sciences database: MEDLINE, Embase, CINAHL, PsycINFO, Scopus, WHO Global literature on coronavirus disease database, China National Knowledge Infrastructure (中国 知网 –CNKI). Furthermore, EPPI Centre’s COVID-19 living systematic map and the publicly available publication list of the COVID-19 living systematic review will be incorporated for preprints and recent COVID-19 publications.Data extractionTwo independent reviewers will extract predefined parameters. The risk of bias will be assessed.Data synthesisData synthesis will be performed according to study type and design, type of epidemic and pandemic, types of outcomes (mental health and psychosocial outcomes), and participant characteristics (e.g., sex, race, age, socioeconomic status, food security, presence of dependency in daily life activities independent/dependent older adults). Comparison between sex, race, and other age groups will be performed qualitatively, and quantitatively if enough data is available. The risk of bias and study heterogeneity will be reported for quantitative studies.ConclusionThis study will provide information to take actions to address potential mental health difficulties during the COVID-19 pandemic in older adults and to understand responses on this age group. Furthermore, it will be useful to identify potential groups that are more vulnerable or resilient to the mental-health challenges of the current worldwide pandemic.

Estimation of rock composition in mining plants is important for determining rock size and grindability which, in turn, may improve control of the grinding process. Variations in ore grindability and size distribution directly affect a mill's power consumption and throughput. It is therefore highly desirable to develop new methods to estimate rock lithological composition remotely. In this paper, a new method for remote lithological classification based on digital image analysis is proposed. The method is based on a single digital video camera for image acquisition from rocks on a conveyor belt. Each image is broken into sub-images to extract texture information using several different spatial scales. Gabor filters are used to extract features from each sub-image for five different spatial scales and eight orientations. After feature extraction, each sub-image is classified into a lithological class using a support-vector machine (SVM). Finally, information from the contour of each rock is used to select all sub-images that fall inside the contour and vote for the rock class. The method was tested on three databases, one of them containing sub-images of 64 × 43 pixels of five ore types assigned to three grindability classes (soft, medium and hard). The second database has seven ore types containing sub-images of 60 × 40 pixels. The third database was captured with higher resolution cameras and controlled illumination with five ore types and containing sub-images of 64 × 48. The databases were divided into 2 subsets, one for training with cross validation, and the other for testing. Classification accuracy was compared with previously published results on both databases. Comparisons show that our proposed method yielded significant improvements in classification accuracy, between 8.3% and 26%, relative to previously published results.

Gender classification from NIR iris image is a new topic with only a few papers published. All previous work on gender-from-iris tried to find the best feature extraction techniques to represent the information of the iris texture for gender classification using normalized, encoded or periocular images. However this is a new topic in deep-learning application with soft biometric. In this chapter, we show that learning gender-iris representations through the use of deep neural networks may increase the performance obtained on these tasks. To this end, we propose the application of deep-learning methods to separate the gender-from-iris images even when the amount of learning data is limited, using an unsupervised stage with Restricted Boltzmann Machine (RBM) and a supervised stage using a Convolutional Neural Network (CNN).

The Kintun Program is an innovating health intervention project launched in Chile in 2013. It aims to enhance participation of persons with dementia (PwD) in everyday activities, respite for the caregivers and improve home-based care. The program involves different components including day care center, home visits, training for caregivers, counseling and community education. These actions are developed by a multidisciplinary team. This program represents a novel approach to dementia care in Chile as a country with policies and dementia care strategies still under development. To analyze the effect of the Program Kintun on the PwD, a quasi-experimental study was conducted during the implementation of the program, with repeated measures at baseline (T.0.) and after 10–14 weeks of intervention (T1) during September 2013 and June 2014. Data were collected using: ADCS-ADL (activities of daily living), MMSE (cognition), TUG (fall risk) and NPI-Q (neuro-psychiatric behaviors and distress). Data were analyzed with t-student considering p

Face and iris localization is one of the most active research areas in image understanding for new applications in security and theft prevention, as well as in the development of human-machine interfaces. In the past, several methods for real-time face localization have been developed using face anthropometric templates which include face features such as eyes, eyebrows, nose and mouth. It has been shown that accuracy in face and iris localization is crucial to face recognition algorithms. An error of a few pixels in face or iris localization will produce significant reduction in face recognition rates. In this paper, we present a new method based on particle swarm optimization (PSO) to generate templates for frontal face localization in real time. The PSO templates were tested for face localization on the Yale B Face Database and compared to other methods based on anthropometric templates and Adaboost. Additionally, the PSO templates were compared in iris localization to a method using combined binary edge and intensity information in two subsets of the AR face database, and to a method based on SVM classifiers in a subset of the FERET database. Results show that the PSO templates exhibit better spatial selectivity for frontal faces resulting in a better performance in face localization and face size estimation. Correct face localization reached a rate of 97.4% on Yale B which was higher than 96.2% obtained with the anthropometric templates and much better than 60.5% obtained with the Adaboost face detection method. On the AR face subsets, different disparity errors were considered and for the smallest error, a 100% correct detection was reached in the AR-63 subset and 99.7% was obtained in the AR-564 subset. On the FERET subset a detection rate of 96.6% was achieved using the same criteria. In contrast to the Adaboost method, PSO templates were able to localize faces on high-contrast or poorly illuminated environments. Additionally, in comparison with the anthropometric templates, the PSO templates have fewer pixels, resulting in a 40% reduction in processing time thus making them more appropriate for real-time applications.

In Chile most of care for persons with dementia is delivered by family relatives and research of interventions targeting caregivers of persons with dementia is very limited. This qualitative study explored perceived benefits of an education and training program for family caregivers of older persons with dementia. Setting was the Program Kintun for persons with dementia in the city of Santiago, Chile. Intervention was founded using the theoretical frame of readiness in family caregivers of persons with dementia by Gitlin et al (2014), based on a progressive cycle in the development of skills that allow caregivers to move towards higher levels of self-efficacy.

Activities developed under hazardous conditions, such as mining, require an active investment from the industry to avoid serious injuries and even loss of lives. One of the safety measures taken to address this problem is to implement a video surveillance system able to track vehicles and workers to enforce safety protocols preventing dangerous events that could lead to accidents. In this paper, we propose an intelligent video surveillance system able to detect and predict possible dangerous interactions between workers and vehicles operating on a monitored environment. Our system is able to analyze video information from one or several cameras, detects and tracks objects and emits an alarm that warns the operator on possible safety hazards. A multiclass object detection is trained to detect vehicles and people. The system object detection stage was implemented in CUDA for parallel processing to achieve real time operation. A multitracking system was implemented to track detected objects that move from one camera field of view to a neighbor camera field of view. Experiments on a monitored parking space show that our proposed system yields good performance in tracking people and vehicles, detecting close interaction and generating relevant alarms.

Diabetic retinopathy is the leading cause of blindness in the working age population of the world. There is evidence that the digital image of the eye fundus is sensitive and specific for early signs of diabetic retinopathy. An automated image analysis method to detect early signs of diabetic retinopathy is highly desired for screening. The optic disc is one of the main structures visible in the eye fundus image that can be used as a reference to detect other abnormalities. In this paper, we propose a new cascade classifier based method for online optic disc detection. The cascade classifiers are trained using segmented images of optic discs and non-optic discs obtained from a training database (STARE). The method extracts Haar features from rectangular windows that are used to scan the digital image of the eye fundus. After training with segmented images from the STARE database, our optic disc detection method was tested with the DIARETDB1 database. Results showed high classification accuracy and fast detection capacity for online optic disc detection.

There are many applications in image analysis where it is important to detect accurately patterns that include color and texture, e.g., plastic or concrete traffic barriers. This paper proposes a new method and extends a general machine vision approach for on-line pattern detection using color and textural information. Our proposed method includes the following steps: division of each image into sub-images, use of the Haralick and Binary Quaternion-Moment-Preserving methods to extract texture and color features, support vector machines for classification, and a post processing stage using clustering. The method was tested in two databases. The first one with three pattern types and the results yielded a detection rate of 96.4% with 14 false positives. The second database has nine pattern types and the results yielded a detection rate of 98.4% with 9 false positives. The results were compared advantageously with Haralick and BQMP methods separately.

Lithological classification is important to improve control of the grinding process in a mining plant. Based on the lithological classification the hardness of the mineral can be estimated and mill operation can be optimized. In this paper we proposed a method for rock lithological classification based on Gabor texture analysis and support vector machine classification. We use images from a database formed using rocks extracted from a typical mining plant in Chile. Six different lithological classes were used. Ten images for each lithological class were used for each class for training and another ten images for each class was used for testing. Results on the testing database were measured using 5 cross-validations for the six classes. Our results show that extracting texture features with Gabor filters and rock segmentation based on the Watershed transform reached over 80% accuracy on the testing database.

In mining, rock classification plays a crucial role at different stages of the extraction process ranging from the design of the mine to mineral grading and plant control. In this paper we present a new method to improve rock classification using digital image analysis, feature selection based on mutual information and a voting process to take into account boundary information. We extract rock color and texture features and using mutual information we selected 14 from 36 features to represent the data in a lower dimensional space. The original image was divided into sub-images that are assigned to one class based on the selected color and texture features using a set of classifiers in cascade. Additionally, using rock boundary information, a voting process for the sub-images within the same blob is performed. We compare our results based on sub-image classification to those obtained after the voting process and to those previously published on the same rock image database. We show that the RMSE on rock composition classification on a test database decreased 8.8% by using our proposed voting method with the automatic segmentation with respect to direct sub-image classification. The RMSE decreased 29.5% relative to previously published results with the same database using a mixture of dry and wet rock images. The RMSE decreased even more if we considered separately dry and wet rocks. Our proposed method could be implemented in real-time to estimate mineral composition and can be used for online ore sorting and/or classification. (C) 2011 Elsevier B.V. All rights reserved.

Face detection methods are important in many applications such as face recognition, iris detection, eye-gaze estimation, gesture recognition and in man-machine interfaces, among others. In this paper, a new method for face detection of frontal faces is proposed. The method is based on a geometric transform we named Log-Linear Elliptic Transform, LET, that converts the lower semi-elliptical contour of the face into a vertical line in the transformed space. The vertical line can be detected by a simple projected histogram allowing a fast computation. The vertical line detection is sensitive to the face position, eccentricity and rotation. This allows the selection of an ellipse that better represents the face. The method was applied successfully in the detection of frontal faces in the FERET database.