In contemporary education, ensuring classroom accessibility is fundamental to fostering equitable learning opportunities for all students. Despite advancements in inclusive education, students with disabilities continue to encounter substantial barriers that hinder their academic progress. According to the Centers for Disease Control and Prevention (CDC), 24.8% of U.S. adults have a disability, with 12% of graduate students reporting similar challenges [1]. In 2022, approximately 15% of college students disclosed having ADD or ADHD; however, a significant proportion (15-43%) of students who disclose disabilities do not receive necessary accommodations [1][2][3][4]. These statistics underscore the urgent need for a systematic approach to assessing and improving classroom accessibility. To address this challenge, researchers developed a mobile application to audit classroom accessibility. The application incorporates structured questionnaires that evaluate physical classroom accessibility, generating a quantitative accessibility score based on user responses. This score provides data-driven insights into the inclusivity of classroom environments, enabling educational institutions to identify and address accessibility barriers. The application, compatible with both iOS and Android platforms, facilitates real-time feedback collection from students, educators, and staff. This study involves the collection and analysis of real-world physical classroom accessibility data at a Midwestern university using our developed mobile application. Analysis of the accessibility scores reveals significant trends and identifies key areas requiring improvement. The findings indicate that accessible and user-friendly evaluation tools can effectively identify and address physical barriers in classroom environments. Future developments will be directed at incorporating Explainable Artificial Intelligence (XAI) to generate comprehensive reports, providing deeper insights into accessibility challenges for a diverse array of individuals with impairments. This research highlights the potential of technology-driven approaches in capturing firsthand experiences and facilitating substantial improvements in classroom accessibility, ultimately fostering a more inclusive learning environment.

Women's health remains a critical area of focus in the realm of healthcare, with various challenges that impact their overall well-being. This survey explores the multifaceted ways in which technology can contribute to addressing women's health issues, providing a comprehensive overview of existing research and emerging trends. The survey begins by identifying key challenges in women's health, encompassing reproductive health, gynecological conditions, mental health, cardiovascular health and preventative measures. Subsequently, it delves into a thorough analysis of how various technologies, both established and emerging, play a pivotal role in mitigating these challenges. Mental health, a critical aspect often overlooked in women, is also discussed in the context of mobile apps, virtual support groups, and AI-powered mental health assessments tailored to women's needs. By presenting an extensive analysis of the current landscape, this survey not only highlights the strides made in leveraging technology for women's health but also identifies gaps and challenges that warrant further research. The synthesis of existing knowledge aims to inform policymakers, healthcare professionals, and technology developers, fostering collaborative efforts to harness the full potential of technology in enhancing women's health outcomes. As technology continues to evolve, this survey provides a foundation for future endeavors that prioritize and advance women's health on a global scale.

Rehabilitation and disability researchers are increasingly considering utlizing machine learning (ML) algorithms to enhance accessibility for people with disabilities (PwD) as they interact with their environments. PwD often experience environmental barriers in the community and private buildings due to a lack of accessible infrastructure design and prior accessibility information. Such environmental barriers may inhibit PwD from full participation and impede in one’s overall independence and quality of life. The availability of healthcare services and information are essential for increased participation in occupations and optimal independence. In the current era of connected health, information has the ability to be accessed anywhere and providing accessibility content for PwD to use can enhance occupational performance factors. The purpose of this study was to 1) identify existing accessibility measurement challenges and barriers and 2) propose an intelligent accessibility evaluation and assessment for buildings, and 3) leverage mobile applications to address major challenges of accessibility measurement. This research aims to improve the accuracy and reliability of the accessibility measurement using a smarter system.

The elderly population experiences great variability in health, disability, age-related changes, polymorbidity, and associated polypharmacy. Polypharmacy refers to the simultaneous use of multiple drugs to treat a single condition. Polypharmacy often leads to high healthcare costs, unpropitious events, confusion, and errors in the management of the individual’s health. The remote monitoring approach to medicine, especially for elderly populations in rural regions, with limited access to care, has significantly increased. Continuous monitoring of appropriate medication prescription, consistent review of medication lists, and re-evaluation of patient needs are crucial for ensuring that polypharmacy is minimized, and therefore the patient wellbeing is maximized. This study aims to present a comprehensive catalog of information in uniform terminology, define the general definition and features of polypharmacy in elderly people from rural areas, and enable the reader to use that information optimally for their specific application. Our study reveals the current understanding of polypharmacy in elderly individuals from rural regions. We proposed a novel system design for the remote monitoring of elderly patients to minimize polypharmacy. We aim to develop a unique mobile-based application for patient uses and a web-based application for doctors. Our applications will communicate with the medical IoT devices connected with the patients to obtain data. By using our application, providers can monitor their elderly patients’ health data and will be able to make better informed decisions for prescribing medications. Our approach will help elderly people in rural areas and their providers minimize adverse effects due to polypharmacy.

Detecting heart diseases has been a research interest for centuries. Many of these approaches are based on heartbeat analysis using a stethoscope and some of these are digitally analyzed. In an ordinary system, doctors use an acoustic stethoscope to detect any aberration in the heartbeat and predict atypical conditions of the human heart. One major problem is that the frequency range and intensity of the heart sounds are flat as well as the sound may contain noise. Hence, even a cardiac specialist doctor may encounter difficulties to analyze the heart sound perfectly. This paper describes a new methodology to detect heart diseases by examining heart sounds in real-time. We consider the guts sound as our input data. Our methodology uses a deep learning approach to determine whether a patient has any disease or is healthy. To achieve that, we integrated an electronic stethoscope and a software solution known as a heartbeat audio classifier. Our proposed system solution should be able to differentiate normal heartbeats and heart murmurs with a prediction of probable heart problem type in real-time. We believe our approach assists in reducing the cardiac arrest rate.