PURPOSE\nTo measure macular choroidal thickness in normal eyes at different points using enhanced depth imaging (EDI) optical coherence tomography (OCT) and to evaluate the association of choroidal thickness and age.\n\n\nDESIGN\nRetrospective, observational case series.\n\n\nMETHODS\nEDI OCT images were obtained in patients without significant retinal or choroidal pathologic features. The images were obtained by positioning a spectral-domain OCT device close enough to the eye to acquire an inverted image. Seven sections were obtained within a 5 x 30-degree area centered at the fovea, with 100 scans averaged for each section. The choroid was measured from the outer border of the retinal pigment epithelium to the inner scleral border at 500-microm intervals of a horizontal section from 3 mm temporal to the fovea to 3 mm nasal to the fovea. Statistical analysis was performed to evaluate variations of choroidal thickness at each location and to correlate choroidal thickness and patient age.\n\n\nRESULTS\nThe mean age of the 30 patients (54 eyes) was 50.4 years (range, 19 to 85 years), and 14 patients (46.7%) were female. The choroid was thickest underneath the fovea (mean, 287 microm; standard deviation, +/- 76 microm). Choroidal thickness decreased rapidly in the nasal direction and averaged 145 microm (+/- 57 microm) at 3 mm nasal to the fovea. Increasing age was correlated significantly with decreasing choroidal thickness at all points measured. Regression analysis suggested that the subfoveal choroidal thickness decreased by 15.6 microm for each decade of life.\n\n\nCONCLUSIONS\nChoroidal thickness seems to vary topographically within the posterior pole. The thickness of the choroid showed a negative correlation with age. The decrease in the thickness of the choroid may play a role in the pathophysiologic features of various age-related ocular conditions.

Enhanced Depth Imaging Optical Coherence Tomography of the Choroid in Normal Eyes

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Knowing What Students Know The Science And Design Of Educational Assessment

The objective assessment of physical activity levels through wearable inertial-based motion detectors for the automatic, continuous and long-term monitoring of people in free-living environments is a well-known research area in the literature However, their application to older adults can present particular constraints This paper reviews the adoption of wearable devices in senior citizens by describing various researches for monitoring physical activity indicators, such as energy expenditure, posture transitions, activity classification, fall detection and prediction, gait and balance analysis, also by adopting consumer-grade fitness trackers with the associated limitations regarding acceptability This review also describes and compares existing commercial products encompassing activity trackers tailored for older adults, thus providing a comprehensive outlook of the status of commercially available motion tracking systems Finally, the impact of wearable devices on life and health insurance companies, with a description of the potential benefits for the industry and the wearables market, was analyzed as an example of the potential emerging market drivers for such technology in the future

A Review of Activity Trackers for Senior Citizens: Research Perspectives, Commercial Landscape and the Role of the Insurance Industry.

https://ro.uow.edu.au/cgi/viewcontent.cgi?article=2345&context=smhpapers1

Evaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification, visual interpretation and machine learning approaches

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. The synthesis and analysis have pointed out a number of shortcomings in prior research. Major shortcomings are demonstrated by the majority of the studies adopting an observational research design, too small sample sizes, poorly presented, and/or non-representative participant demographics (i.e., age, gender, patient/healthy). These aspects need to be considered in future research work.

https://www.mdpi.com/1424-8220/20/5/1502/pdf

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

Vegetation is a key component of nearly all global ecosystems, having a role of regulating various biogeochemical cycles in nature, as well as maintaining the energy balance at the earth's surface and atmospheric boundary layer. Therefore, it is of wide importance to constantly monitor the changes in vegetation cover and structure, especially those that are due to human influence. Backed up by many global initiatives for vegetation preservation and monitoring, it is necessary to have a system to collect and analyse different vegetation indices and products. One of the most important parameters of calculating many of these indices, is the amount of the vegetation cover in a certain area. This paper proposes a compact system for estimating vegetation in a certain area. The proposed system consists of a small UAV platform with the ability of performing autonomous flights and recording the videos of the ground cover using a camera with a modified infrared filter lens for obtaining the composite NDVI videos. The video is analysed and processed to extract the most significant frames that form a mosaic representing the area recorded by the camera. Finally, a contour is calculated forming the boundary around the area on the mosaic image that contains pixels that represent vegetation or photosynthetic activity. The contour and the area inside the boundary can be used for segmentation and estimation of other vegetation indices.

UAV-based remote sensing for vegetation cover estimation using NDVI imagery and level sets method

The purpose of this paper is to present a smart interactive mirror interface. In this paper, we describe the design and development of a futuristic mirror that offers simplified and customisable services to the home environment. On a par with the recent advances in the Internet of Things standards and applications, the mirror is designed to enable residents to control the household smart appliances and access personalised services; ensuring convenience in accessing these services with the slightest possible user intervention. The multipurpose user-friendly functionalities of the proposed mirror interface provide users with the versatility needed for better management and integration of daily tasks. A service oriented approach is adopted in the architecture of the proposed system. It consists mainly of two mobile applications devoted to the customisation of user profiles, which are displayed on the smart mirror interface once successfully paired. Moreover, in the proposed system, emphasis is particularly given to the user profile personalisation, as well as planned system interactivity and adaptability. Hence, the proposed system is set apart from others for its ease of use as well as its provision of various customised information services for user profile generation.

/pdf/a-mobile-programmable-smart-mirror-for-ambient-iot-4qu9owsteu.pdf

A Mobile-Programmable Smart Mirror for Ambient IoT Environments

In this paper, we propose an integrated caregiver-focused framework that aims to provide a health care and a fall detection service for elderly users. The proposed system looks at the responsibility of the elder-care from three different perspectives: maintenance of an accurate and updated health history, prevention of inappropriate dietary options, and detection of major fall accidents. We ensure a timely intervention by capitalizing on smart watches and their ability to notify the caregiver any time and anywhere. The integrated system provides the users with an organized medical journal that gives an insight of their medical status while being able to share it with their doctor Moreover, the system provides a food and nutrition guide that allows the users to evaluate their food intake both quantity and quality wise. Lastly, users can benefit from a fall detection service that uses the sensors available on the commercial smart watches and the cascade feed-forward neural network for classification. The experiments performed result in an accuracy of 93.33% of the proposed system in the classification of fall events.

An integrated caregiver-focused mHealth framework for elderly care

Mobile government is an innovative research area where efforts are made to advance governmental and public services. As such, in this paper, we propose a crisis management system for real-time emergency notification of users using mobile applications and smart watches. We develop an intuitive web portal using a client server architecture for governmental agencies to easily and efficiently notify users within the range of danger in the occurrence of a disaster through SMS or push notifications to the mobile application or the smart watch, if the latter is available. Moreover, real-time mapping for indoor localization systems are utilized for user navigation to the nearest exit with the added feature of floor plans for public locations that may be accessed offline for the convenience of the users. The system is also designed for instant emergency aid assistance in case of a medical personal difficulty through the use of the developed dedicated bilingual multi-platform mobile application.

Smart Mobile-Based Emergency Management and Notification System

Diabetes is currently identified as one of the fastest-growing chronic diseases globally, causing a number of diseases affecting other body physiological systems. Thus, it is crucial to develop methods for early detection of diabetes for its prevention, as well as for more efficient treatment. One of the most prevalent complications caused by diabetes is diabetic retinopathy, which is reported to be the leading cause of blindness in the majority of diabetic patients. Early detection of diabetic retinopathy cannot only provide better treatment and prevent the disease from advancing, but it can also serve as an early sign of diabetes in general. Automated noninvasive CAD systems for detecting retina changes, and thus the early occurrence of diabetic retinopathy have become the main focus of the research in the area, as they show a strong potential to decrease the use of standard methods that mostly rely on visual observation and human bias. This chapter discusses current noninvasive imaging techniques utilized for studying retinal changes. Moreover, we discuss a new approach based on the accurate segmentation of the retina in 12 distinct layers and evaluating informative pixel-wise measures on each of the layers separately. These measures include retinal thickness, reflectivity, and curvature. These are consequently used for training a random forest classifier to discriminate between healthy and diabetic retinas. The results obtained show good classification performance and thus, a strong potential to be utilized as a standard diagnostic tool, despite the lack of training and testing data.

Yasmina Al Khalil

Papers

UAV-based remote sensing for vegetation cover estimation using NDVI imagery and level sets method

A Mobile-Programmable Smart Mirror for Ambient IoT Environments

An integrated caregiver-focused mHealth framework for elderly care

Smart Mobile-Based Emergency Management and Notification System

Early detection of diabetics using retinal OCT images