Dheergayu: Clinical Depression Monitoring Assistant

Depression is identified as one of the most common mental health disorders in the world. Depression not only impacts the patient but also their families and relatives. If not properly treated, due to these reasons it leads people to hazardous situations. Nonetheless existing clinical diagnosis tools for monitoring illness trajectory are inadequate. Traditionally, psychiatrists use one to one interaction assessments to diagnose depression levels. However, these cliniccentered services can pose several operational challenges. In order to monitor clinical depressive disorders, patients are required to travel regularly to a clinical center within its limited operating hours. These procedures are highly resource intensive because they require skilled clinician and laboratories. To address these issues, we propose a personal and ubiquitous sensing technologies, such as fitness trackers and smartphones, which can monitor human vitals in an unobtrusive manner

Micro-scale pedestrian streetscapes and physical activity in Hispanic / Latino adults : Results from HCHS / SOL

We examined associations of micro-scale environment attributes (e.g., sidewalks, street crossings) with three physical activity (PA) measures among Hispanic/Latino adults (n = 1776) living in San Diego County, CA. Systematic observation was used to quantify micro-scale environment attributes near each participant's home. Total PA was assessed with accelerometers, and PA for transportation and recreation were assessed by validated self-report. Although several statistically significant interactions between individual and neighborhood characteristics were identified, there was little evidence micro-scale attributes were related to PA. An important limitation was restricted environmental variability for this sample which lived in a small area of a single county

Interband Retrieval and Classification Using the Multilabeled Sentinel-2 BigEarthNet Archive

Conventional remote sensing data analysis techniques have a significant bottleneck of operating on a selectively chosen small-scale dataset. Availability of an enormous volume of data demands handling large-scale, diverse data, which have been made possible with neural network-based architectures. This article exploits the contextual information capturing ability of deep neural networks, particularly investigating multispectral band properties from Sentinel-2 image patches. Besides, an increase in the spatial resolution often leads to nonlinear mixing of land-cover types within a target resolution cell. We recognize this fact and group the bands according to their spatial resolutions, and propose a classification and retrieval framework. We design a representation learning framework for classifying the multispectral data by first utilizing all the bands and then using the grouped bands according to their spatial resolutions. We also propose a novel triplet-loss function for multilabeled images and use it to design an interband group retrieval framework. We demonstrate its effectiveness over the conventional triplet-loss function. Finally, we present a comprehensive discussion of the obtained results. We thoroughly analyze the performance of the band groups on various land-cover and land-use areas from agro-forestry regions, water bodies, and human-made structures. Experimental results for the classification and retrieval framework on the benchmarked BigEarthNet dataset exhibit marked improvements over existing studies

An Automated Strabismus Classification Using Case-Based Reasoning Algorithm For Binocular Vision Management System

Binocular vision is a type of vision that allows an individual to perceive depth and distance using both eyes to create a single image of their environment. However, there is an illness called strabismus, where it is difficult for some people to focus on seeing things clearly at a time. There are a lot of diagnosis need to be done for doctors to diagnose whether patients suffer from strabismus or not. One of them is to perform accommodate amplitude test, which is time-consuming. Thus, with the Agile methodology, the Binocular Vision Management system is proposed which comprised of two components, a web-based component for patient, treatment, and appointment management, and a machine learning component for automating the strabismus classification by using case-based reasoning algorithm. Therefore, this will significantly hasten the process of classifying strabismus and help keep all clinical records in one place