Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition.

Human activity recognition (HAR) in wearable and ubiquitous computing typically involves translating sensor readings into feature representations, either derived through dedicated pre-processing procedures or integrated into end-to-end learning approaches. Independent of their origin, for the vast majority of contemporary HAR methods and applications, those feature representations are typically continuous in nature. That has not always been the case.

Family Helps Transform the STEM Pathways of Community College Women of Color STEM Majors.

A "critical access point in the STEM pipeline for Latinx students and other students of color" (Herrera , 2018), community colleges provide a seminal breeding ground for academic pursuits (Bahr , 2017). However, how personal networks influence STEM pathways of two-year college students remains largely unexplored. This mixed methods case study explores influence of personal networks on pursuing STEM fields via social network analysis and qualitative narratives.

Promoting Self-Efficacy of Individuals With Autism in Practicing Social Skills in the Workplace Using Virtual Reality and Physiological Sensors: Mixed Methods Study.

Background: Individuals with autism often experience heightened anxiety in workplace environments because of challenges in communication and sensory overload. As these experiences can result in negative self-image, promoting their self-efficacy in the workplace is crucial. Virtual reality (VR) systems have emerged as promising tools for enhancing the self-efficacy of individuals with autism in navigating social scenarios, aiding in the identification of anxiety-inducing situations, and preparing for real-world interactions.

Internet use among persons with common mental disorders: A qualitative study.

Aims And Objectives: To assess the current patterns and experience of internet use among persons with depressive and anxiety disorders, particularly for health-related issues.

Methodology: In-depth interviews were conducted with persons diagnosed with depressive and anxiety disorders (DSM-5), aged ≥18 years who could read and write in Hindi/English, using an interview guide. The interview guide contained questions related to internet use in daily life, internet use for health-related issues, and internet use for stress, and internet use for anxiety/depressive disorder.

Executive function predictors of science achievement in middle-school students.

Cognitive flexibility as measured by the Wisconsin Card Sort Task (WCST) has long been associated with frontal lobe function. More recently, this construct has been associated with executive function (EF), which shares overlapping neural correlates. Here, we investigate the relationship between EF, cognitive flexibility, and science achievement in adolescents.

The role of executive function abilities in interleaved vs. blocked learning of science concepts.

This study investigated the relative efficacy of interleaved versus blocked instruction and the role of executive function in governing learning from these instructional sequences. Eighth grade students learned about three rock concepts (igneous, sedimentary, metamorphic) and their attributes (origin, texture, composition). Consistent with prior studies and as predicted by current theoretical accounts, students who received interleaved instruction showed better memory (i.

Mortality risk from United States coal electricity generation.

Policy-makers seeking to limit the impact of coal electricity-generating units (EGUs, also known as power plants) on air quality and climate justify regulations by quantifying the health burden attributable to exposure from these sources. We defined "coal PM" as fine particulate matter associated with coal EGU sulfur dioxide emissions and estimated annual exposure to coal PM from 480 EGUs in the US. We estimated the number of deaths attributable to coal PM from 1999 to 2020 using individual-level Medicare death records representing 650 million person-years.

Intelligent Care Management for Diabetic Foot Ulcers: A Scoping Review of Computer Vision and Machine Learning Techniques and Applications.

Ten percent of adults in the United States have a diagnosis of diabetes and up to a third of these individuals will develop a diabetic foot ulcer (DFU) in their lifetime. Of those who develop a DFU, a fifth will ultimately require amputation with a mortality rate of up to 70% within five years. The human suffering, economic burden, and disproportionate impact of diabetes on communities of color has led to increasing interest in the use of computer vision (CV) and machine learning (ML) techniques to aid the detection, characterization, monitoring, and even prediction of DFUs.

"I care about sex, I care about my health": A mixed-methods pre-test of a HIV prevention mobile health app for Black women in the southern United States.

Background: Black women experience higher rates of adverse sexual and reproductive health and HIV outcomes, however the use of mHealth to address these health disparities in this population has been inadequate. This study involved a one-month pre-test with Black women living in metro-Atlanta to evaluate the usability, acceptability, and engagement of an HIV prevention app SavvyHER.

Methods: An explanatory mixed-methods design was employed in which quantitative data was collected through weekly cross-sectional surveys, and qualitative data was collected through semi-structured in-depth interviews.

Competing numerical magnitude codes in decimal comparison: Whole number and rational number distance both impact performance.

A critical difference between decimal and whole numbers is that among whole numbers the number of digits provides reliable information about the size of the number, e.g., double-digit numbers are larger than single-digit numbers.

Communicating COVID-19 exposure risk with an interactive website counteracts risk misestimation.

During the COVID-19 pandemic, individuals depended on risk information to make decisions about everyday behaviors and public policy. Here, we assessed whether an interactive website influenced individuals' risk tolerance to support public health goals. We collected data from 11,169 unique users who engaged with the online COVID-19 Event Risk Tool (https://covid19risk.

The Lifespan of Human Activity Recognition Systems for Smart Homes.

With the growing interest in smart home environments and in providing seamless interactions with various smart devices, robust and reliable human activity recognition (HAR) systems are becoming essential. Such systems provide automated assistance to residents or to longitudinally monitor their daily activities for health and well-being assessments, as well as for tracking (long-term) behavior changes. These systems thus contribute towards an understanding of the health and continued well-being of residents.

Digital Health App to Address Disparate HIV Outcomes Among Black Women Living in Metro-Atlanta: Protocol for a Multiphase, Mixed Methods Pilot Feasibility Study.

Background: Cisgender Black women in the southern United States are at heightened risk for HIV and adverse sexual and reproductive health outcomes. Mobile health interventions that target HIV risk while being adapted to the needs and lived experiences of Black women are remarkably limited.

Objective: The study aims to refine SavvyHER, a mobile app for HIV prevention, with Black women residing in high HIV incidence areas of Georgia and evaluate the feasibility, acceptability, and usability of SavvyHER.

A TTFS-based energy and utilization efficient neuromorphic CNN accelerator.

Spiking neural networks (SNNs), which are a form of neuromorphic, brain-inspired AI, have the potential to be a power-efficient alternative to artificial neural networks (ANNs). Spikes that occur in SNN systems, also known as activations, tend to be extremely sparse, and low in number. This minimizes the number of data accesses typically needed for processing.

Fully robotic social environment for teaching and practicing affective interaction: Case of teaching emotion recognition skills to children with autism spectrum disorder, a pilot study.

21st century brought along a considerable decrease in social interactions, due to the newly emerged lifestyle around the world, which became more noticeable recently of the COVID-19 pandemic. On the other hand, children with autism spectrum disorder have further complications regarding their social interactions with other humans. In this paper, a fully Robotic Social Environment (RSE), designed to simulate the needed social environment for children, especially those with autism is described.

Community-Centered Assessment to Inform Pandemic Response in Georgia (US).

The Georgia Community Engagement Alliance (CEAL) Against COVID-19 Disparities Project conducts community-engaged research and outreach to address misinformation and mistrust, to promote inclusion of diverse racial and ethnic populations in clinical trials and increase testing and vaccination uptake. Guided by its Community Coalition Board, The GEORGIA CEAL Survey was administered among Black and Latinx Georgia 18 years and older to learn about community knowledge, perceptions, understandings, and behaviors regarding COVID-19 testing and vaccines. Survey dissemination occurred using survey links generated through Qualtrics and disseminated among board members and other statewide networks.

Navigating the complexities of mobile medical app development from idea to launch, a guide for clinicians and biomedical researchers.

With today's pace of rapid technological advancement, many patient issues in modern medicine are increasingly solvable by mobile app solutions, which also have the potential to transform how clinical research is conducted. However, many critical challenges in the app development process impede bringing these translational technologies to patients, caused in large part by the lack of knowledge among clinicians and biomedical researchers of "what it takes" to design, develop, and maintain a successful medical app. Indeed, problems requiring mobile app solutions are often nuanced, requiring more than just clinical expertise, and issues such as the cost and effort required to develop and maintain a well-designed, sustainable, and scalable mobile app are frequently underestimated.

Mobile Phone Apps for HIV Prevention Among College-Aged Black Women in Atlanta: Mixed Methods Study and User-Centered Prototype.

Background: Black women in college are disproportionately affected by HIV diagnoses. Mobile apps can facilitate the innovative delivery of accurate HIV and sexual and reproductive health information. However, mobile health interventions are severely underused in this population.

Perceptions of stroke survivors regarding factors affecting adoption of technology and exergames for rehabilitation.

Background: Task-specific motor training and repetitive practice are essential components of clinical rehabilitation. Emerging evidence suggests that incorporating gaming interfaces (also referred to as "exergames"), including virtual reality and augmented reality (VR/AR)-based interfaces for motor training, can enhance the engagement and efficacy of poststroke rehabilitation.

Objective: To investigate perceptions of individuals with stroke regarding technology and exergames for rehabilitation.

Leveraging Symptom Search Data to Understand Disparities in US Mental Health Care: Demographic Analysis of Search Engine Trace Data.

Background: In the United States, 1 out of every 3 people lives in a mental health professional shortage area. Shortage areas tend to be rural, have higher levels of poverty, and have poor mental health outcomes. Previous work has demonstrated that these poor outcomes may arise from interactions between a lack of resources and lack of recognition of mental illness by medical professionals.

Cross-Platform Detection of Psychiatric Hospitalization via Social Media Data: Comparison Study.

Background: Previous research has shown the feasibility of using machine learning models trained on social media data from a single platform (eg, Facebook or Twitter) to distinguish individuals either with a diagnosis of mental illness or experiencing an adverse outcome from healthy controls. However, the performance of such models on data from novel social media platforms unseen in the training data (eg, Instagram and TikTok) has not been investigated in previous literature.

Objective: Our study examined the feasibility of building machine learning classifiers that can effectively predict an upcoming psychiatric hospitalization given social media data from platforms unseen in the classifiers' training data despite the preliminary evidence on identity fragmentation on the investigated social media platforms.

Assessing and Promoting Cardiovascular Health for Adolescent Women: User-Centered Design Approach.

Background: Cardiovascular disease (CVD) is the leading cause of death among women in the United States. A considerable number of young women already have risk factors for CVD. Awareness of CVD and its risk factors is critical to preventing CVD, yet younger women are less aware of CVD prevalence, its risk factors, and preventative behaviors compared to older women.

Clinical, technical, and implementation characteristics of real-world health applications using FHIR.

Objective: Understanding the current state of real-world Fast Healthcare Interoperability Resources (FHIR) applications (apps) will benefit biomedical research and clinical care and facilitate advancement of the standard. This study aimed to provide a preliminary assessment of these apps' clinical, technical, and implementation characteristics.

Materials And Methods: We searched public repositories for potentially eligible FHIR apps and surveyed app implementers and other stakeholders.

From promise to practice: towards the realisation of AI-informed mental health care.

In this Series paper, we explore the promises and challenges of artificial intelligence (AI)-based precision medicine tools in mental health care from clinical, ethical, and regulatory perspectives. The real-world implementation of these tools is increasingly considered the prime solution for key issues in mental health, such as delayed, inaccurate, and inefficient care delivery. Similarly, machine-learning-based empirical strategies are becoming commonplace in psychiatric research because of their potential to adequately deconstruct the biopsychosocial complexity of mental health disorders, and hence to improve nosology of prognostic and preventive paradigms.