# AI Asthma Guard: Predictive Wearable Technology for Asthma Management in Vulnerable Populations
###### Abstract
The base layer of the graph represents the total number of publications on the topic of asthma. This visualization highlights the growth and research interest in both fields, with a particular emphasis on the intersection of machine learning technologies and asthma studies. Figure 1: Comparative analysis of scholarly publications on asthma and machine learning. The graph shows the total number of publications on asthma and machine learning in research, illustrating the trends and research interest over time (Source: Science Direct). In reviewing recent advancements in asthma monitoring, two key studies stand out for their innovative use of machine learning to enhance the predictive capabilities and patient management. The first study, \"Home monitoring with connected mobile devices for asthma attack prediction with machine learning\" (AAMOS-00) [8], conducted between April 2021 and June 2022, involved 22 UK participants who used an interconnected system of smart devices, including a peak flow meter, inhaler, and smartwatch. These devices collected data on the expiratory flow, inhaler usage, activity, and heart rate, alongside environmental factors like the weather and air quality. Utilizing random forest classifiers, the study achieved high metrics in predicting unscheduled doctor visits, demonstrating the potential of machine learning in forecasting asthma attacks. However, its reliance on active user engagement and its narrow participant criteria during the COVID-19 pandemic may limit its generalizability [8]. In contrast, the study \"A Predictive Machine Learning Tool for Asthma Exacerbations\", by [PERSON] et al. (2022) [9], employed a gradient-boosting trees model to predict asthma exacerbations from data captured by an electronic multi-dose dry powder inhaler with integrated sensors, involving 360 adults over 12 weeks. The model, which incorporated inhalation patterns and patient demographics, showed significant predictive power with a high ROC AUC score, emphasizing the role of integrated sensor technology in managing chronic asthma [9]. Building upon these studies, our project, Asthma Guard, seeks to expand the inclusion criteria and integrate more passive monitoring technologies to improve user adherence and data accuracy. By also conducting our research outside the unusual conditions of a global pandemic, we hope to achieve broader applicability. Asthma Guard incorporates advanced machine learning techniques, including LLMs and regression analysis, to offer a more nuanced and effective approach to asthma management. In this work, a novel solution is proposed and described: a smartwatch equipped with artificial intelligence capabilities designed to predict and alert users of impending asthma attacks by analyzing both physiological and environmental data. The necessity for the AI-driven Asthma Guard smartwatch arises from the identified gaps in the current asthma management strategies. The device employs advanced technology to monitor vital physiological indicators such as the heart rate and oxygen levels, providing critical data that can preemptively alert both the patient and healthcare providers of potential asthma exacerbations. This capability addresses the immediate needs in asthma crisis prevention and contributes to a broader understanding of asthma management through continuous data analysis. For children, the Asthma Guard watch serves as a critical layer of security, acting as a vigilant guardian by monitoring environmental and physiological parameters to anticipate and alert users of potential exacerbations. This proactive approach is crucial in minimizing the impact of asthma on children's daily activities and enabling them to lead more active and fulfilling lives. The solution utilizes a blend of machine learning techniques, including classification with support vector machines and prediction using recurrent neural networks, to enhance the predictive accuracy of the smartwatch.