# AI Asthma Guard: Predictive Wearable Technology for Asthma Management in Vulnerable Populations
## 2 System-Level Design  
The Asthma Guard system features a wearable armband, securely positioned on the patient's arm, as detailed in the block diagram illustrated in Figure 2. The system consists of two ChatGPT-enabled LLMs, which facilitate personalized asthma care. Upon boot-up, the first LLM engages the user or guardian in an interactive sequence, posing questions related to various symptoms and demographic data. This information is then captured and converted into a feature list. The list is processed by a random forest classifier, determining the patient's asthma severity level--mild, moderate, or none.  
This severity metric, alongside the patient's age and gender, is forwarded to a second random forest classifier. This classifier also receives data from sensors embedded in the Asthma Guard armband, which includes a microcontroller, early warning LED, and Wi-Fi communication module. The sensors integrated are a pulse oximeter [10], an I2C O\({}_{2}\) sensor [11], DHT22 for humidity and temperature sensing [12], MQ2 for smoke detection [13], and MQ135 for air quality monitoring [14].  
The details of the various sensors integrated into the armband are listed in Table 1. Data from the aforementioned sensors, except for the MQ2 sensor data (which are first processed through a polynomial regression forecasting model to predict potential smoke hazards), are fed into the decision device before reaching the second classifier. The classifier analyzes these combined data to produce an asthma attack prediction. Upon prediction, the system's microcontroller activates a blinking red LED and establishes communication with a second ChatGPT-enabled LLM. This LLM is responsible for managing the asthma protocol, informing and guiding the guardian through the necessary steps following an asthma alert.  
Figure 2: System block diagram of the Asthma Guard management system. The diagram illustrates the flow from user interaction to severity assessment and active management.  
To ensure continuous model validation and updates, the AI Asthma Guard system includes several mechanisms designed to maintain and enhance its accuracy and reliability. A crucial component of this system is an ongoing feedback loop that continually refines and updates the machine learning models with real-world data. This feedback loop involves collecting data from users during regular usage, which are then analyzed to identify patterns and improve the model performance. Additionally, rigorous safety protocols are implemented to mitigate risks associated with AI \"hallucinations\" or erroneous advice. Human oversight plays a critical role in this process, with healthcare professionals regularly reviewing and validating the system's outputs. This combination of continuous data integration and strict safety measures ensures that the AI Asthma Guard system remains effective and trustworthy for asthma management.  
The LLM-enabled app communicates with the AI Asthma Guard device via Bluetooth and cloud services, enabling real-time data collection and analysis. It securely collects, processes, and stores user data, utilizing anonymization and aggregation techniques to safeguard their privacy. The app empowers users with data management capabilities, allowing them to view, edit, and delete their records. Featuring a user-friendly interface, the app facilitates seamless interaction with the AI Asthma Guard device, delivering personalized advice, educational content, and real-time notifications based on health data. Examples include daily health summaries and asthma management tips. By integrating ChatGPT, the app offers enhanced functionality with personalized, context-aware responses, predicting asthma attacks, providing tailored treatment recommendations, and improving user education. Moreover, it boosts user engagement through medication reminders, health tips, virtual consultations with healthcare providers, and proactive asthma management strategies.