Firefighters are faced with myriad stressors from hazardous work conditions and exposure to extreme heat that place their health at significant risks. The intense heat, smoke, shift work, long working hours, and stressful work put firefighters at substantial risk for heat-related injuries, long-term chronic complications, and mental health challenges.
Metabolic health refers to the balance and optimal functioning of the body's metabolic processes, including glucose regulation, lipid metabolism, and energy utilization. With the rise of chronic diseases like diabetes, AI-driven technologies have become crucial tools for diabetes management. These technologies leverage data science and artificial intelligence to analyze large volumes of patient data, such as continuous glucose monitoring (CGM) data, to provide personalized insights and interventions. CGM data, collected through wearable devices (like Dexcom, Abbott's FreeStyle Libre, Medtronic etc.), offers real-time monitoring of a patient's blood glucose levels. AI algorithms process this data to generate actionable insights, helping individuals make informed decisions about their diet, medication, and physical activity. By analyzing patterns and trends in CGM data, AI-driven systems can provide predictive alerts for potential hyperglycemic or hypoglycemic events, enabling users to take proactive measures. The integration of AI into diabetes management not only enhances individual control over glucose levels but also facilitates healthcare professionals' ability to provide targeted interventions. As AI technologies continue to evolve, they hold the promise of revolutionizing the way we approach metabolic health, leading to more effective and personalized strategies for diabetes management.
Undergrad research projects of Shovito
Academic projects formulated, developed, and demonstrated by Shovito during his undergrad and graduate studies.
Stress and challenges associated with stress management are prevalent problems of modern life. Many physical and mental health problems are driven by or escalate with the degree of stress. Stress has harmful effects on those who suffer from mental and physical health problems. Therefore a comprehensive study of stress and its effect is an important research topic in the mobile health domain. Our research lies at the intersection of sensor systems and machine learning, in which we research methods of detecting stress in real-life settings. We use wearable sensor systems to capture bio-markers of stress and design and develop machine learning algorithms for stress detection and classification. Our research aims to develop tools, methodologies, and algorithms for comprehensive approaches to stress detection and to invent smart interventions strategies to promote the well-being of individuals.
Designing active learning strategies that collect labeled sensor data in uncontrolled environments is challenging because (i) without taking into account user's cognitive factors, active learning places much burden on the user and lowers adoption of the technology; and (ii) the labels expressed by end-users exhibit significant amounts of temporal and spacial variations leading to poor performance of the learned models. In this project, we design, develop, and validate human-in-the-loop technologies that collect sensor data in-the-wild and develop algorithms and tools (i) to maximize the active learning performance taking informativeness of sensor data, burden of data labeling on user, and reliability of prospective labels into account; and (ii) for understanding and inferring complex health events using wearable and mobile sensors.
We develop novel approaches for reliable gait monitoring and investigate applications of wearable-base monitoring in various populations. The utility of wearable sensors for continuous gait monitoring has grown substantially, enabling novel applications on mobility assessment in healthcare. Existing approaches for gait monitoring rely on predefined or experimentally tuned platform parameters and are often platform-specific, parameter-sensitive, and unreliable in noisy environments. To address these challenges, we investigate platform-agnostic and reconfigurable computational approaches to gait monitoring, step counting, mobility assessment, and related problems.