This study has been designed to be performed in two phases: the first phase, ongoing in 2024, aimed to study the usability factors that help or hinder the adoption of wearable devices among elderlies. The second phase in 2025 will focus on designing a device customized with age-friendly features to improve home health monitoring and management using wearables in the aging population.

This research aims to develop a compact optoelectronic biosensor capable of swiftly detecting Salmonella Typhi. This innovation promises to revolutionize pathogen detection, simplifying research efforts and enhancing response times in infection control. By streamlining laboratory procedures, our device holds potential applications in healthcare, food safety, and environmental monitoring, ultimately advancing scientific innovation for societal benefit.

We're developing a streamlined AI model to predict the progression of heat-related illnesses efficiently. Our model utilizes advanced algorithms to offer timely insights into the development of these conditions, facilitating proactive measures for prevention and intervention. With a focus on accuracy and speed, our approach aims to enhance the effectiveness of healthcare interventions in combating heat-related illnesses.

Our research focuses on revolutionizing disease diagnosis in dentistry through deep learning. By integrating oral and radiographic images, our model offers a comprehensive assessment, enhancing accuracy and reliability. Through Explainable AI techniques, predictions are transparent and interpretable, crucial for clinical adoption. Our outcomes serve as a valuable tool for dentists, providing timely diagnoses, aiding treatment planning, and ultimately improving patient care outcomes.

We're creating a cutting-edge Machine Learning model for diagnosing skin diseases at point-of-care, directly from cell phone images. With our solution, users can snap photos of skin conditions on their phones, and our model rapidly analyzes them, delivering precise diagnoses. This breakthrough empowers individuals with instant feedback and guidance, revolutionizing access to timely healthcare resources and elevating the management of skin health.

We're developing an ML platform to analyze social media footprints for predicting mental health crises in young individuals. By leveraging advanced algorithms, our platform scans social media activities to identify patterns indicative of potential mental health challenges. This innovative approach aims to provide early intervention and support, ultimately enhancing the mental well-being of the younger population.

We're pioneering a system for home detection and alerting of falls in older individuals. Our innovative mechanism utilizes advanced sensors to detect falls in real-time, triggering immediate alerts for timely assistance. This technology aims to enhance the safety and well-being of older people by ensuring prompt responses to fall incidents, reducing the risk of injuries and improving overall quality of life.

We're crafting an integrated wearable kit designed to predict heart attacks in vulnerable individuals. Our innovative solution combines wearable sensors and advanced algorithms to continuously monitor vital signs and detect early warning signs of heart attacks. By providing real-time insights, our kit empowers users and healthcare professionals to take proactive measures, potentially saving lives and improving heart health outcomes.

We're exploring the role of ChatGPT in transforming Electrical Engineering Education. By leveraging this AI technology, students can access instant assistance, explanations, and problem-solving guidance tailored to their needs. ChatGPT serves as a virtual tutor, enhancing learning experiences, fostering deeper understanding, and preparing students for real-world challenges in the field of Electrical Engineering.