# AI Asthma Guard: Predictive Wearable Technology for Asthma Management in Vulnerable Populations
###### Abstract  
This paper presents AI Asthma Guard, a novel wearable device designed to predict and alert users of impending asthma attacks using artificial intelligence. The system integrates physiological and environmental sensors to monitor health metrics such as the heart rate, oxygen saturation, and exposure to specific air pollutants, which are crucial in managing asthma in children and individuals with mental disabilities. Utilizing machine learning models, including support vector machines and random forest, AI Asthma Guard classifies the risk levels of asthma attacks and provides timely notifications. This study details the device's design, implementation, and preliminary testing results, underscoring its potential to improve health outcomes by enabling proactive asthma management. The implications of this technology reflect its alignment with the Sustainable Development Goals by enhancing individual health and well-being. The integration of a companion app leveraging large language models like ChatGPT facilitates user interaction, providing personalized advice and educational content about asthma management. wearable technology; artificial intelligence; asthma management; predictive healthcare; environmental sensing; machine learning; sustainable health solutions +
Footnote †: journal: MedicalInitiatives like those described in [4] have demonstrated the feasibility of using sensor-based wearable devices connected to IoT systems for the real-time monitoring of environmental triggers such as NO\({}_{2}\) and aldehyde levels, significantly aiding in early detection and management [4]. Artificial intelligence (AI) has emerged as a transformative tool in the healthcare sector, offering significant advancements in the diagnosis, treatment, and management of asthma. AI technologies enable the development of systems that can predict asthma exacerbations, optimize treatment plans, and monitor disease progression in real time. AI-driven diagnostic tools leverage machine learning algorithms to analyze complex datasets, including patient symptoms, environmental conditions, and physiological parameters. These systems can identify patterns and correlations that may not be evident to human clinicians, thus improving the accuracy of asthma diagnoses. For instance, convolutional neural networks (CNNs) have been applied to interpret spirometry data and chest X-rays, aiding in the early detection of respiratory anomalies that could indicate asthma [5]. In treatment settings, AI models assist in personalizing therapy regimens based on individual patient data, thus enhancing the efficacy of asthma management. Predictive analytics can forecast patient responses to various medications, allowing healthcare providers to tailor treatments that minimize side effects and maximize benefits. Furthermore, AI-driven decision support systems provide clinicians with real-time recommendations during patient consultations, ensuring that the most current guidelines and treatment protocols are applied [6]. For ongoing management, AI systems are integrated with wearable devices and home monitoring equipment to track patient health metrics continuously. These systems analyze data trends to predict potential asthma attacks, enabling preemptive medical interventions. Mobile health applications powered by AI enhance patient engagement and compliance by sending reminders for medication, providing health tips, and facilitating virtual consultations with healthcare providers. This level of ongoing, personalized monitoring significantly improves the quality of life of asthma patients and reduces the frequency and severity of asthma episodes [7]. The trends in scholarly publications on asthma and machine learning were determined through a comparative analysis of Scopus-indexed conference and journal publications, as shown in Figure 1. The data were generated by entering the keywords \"asthma\" and \"machine learning\" in the Scopus search engine, which provided the number of publications per year.