# Empowering Healthcare: TinyML for Precise Lung Disease Classification
## 1 Introduction
TinyML is a fast-growing field of ML including hardware, algorithms, and software that aims to facilitate running ML models on ultra-low-power devices having very limited power (under 1 mW), less memory, and limited processor capabilities [14]. TinyML offers tiny IoT devices the ability to analyze data collected by various sensors and act based on the decisions made by the embedded ML model without the need for the cloud. TinyML finds applications in diverse fields [11], including agriculture [11; 15], healthcare [10; 11; 16], and environmental monitoring [11; 17]. The hardware limitations of tiny IoT devices require the minimizing of the ML model in order to deploy it in extremely resource-limited devices. The minimization of the model can be performed by the following techniques: pruning and quantization [14]. These techniques aim to reduce the size of the ML model while trying not to impact its accuracy. The pruning technique is the process of removing unused weights in the model to increase speed inference and minimize its size, while quantization reduces the precision of the model parameters from floating-point (e.g., 32-bit) to lower (e.g., 8-bit) precision [11]; this decreases the model's memory footprint as well as the amount of processing required. TinyML holds immense potential in the healthcare sector [10; 11; 16]. TinyML's ability to run directly on devices at the edge offers numerous advantages. One of the most significant benefits is the ability to perform real-time data analysis without the need for continuous data transmission to centralized cloud systems [11; 16; 18]. This reduces latency, enhances data privacy by minimizing the transfer of sensitive patient data, and lowers dependency on reliable internet connections [10; 16], which is particularly beneficial for remote health monitoring in remote and undeserved areas [12; 16]. Many studies have demonstrated the practical applications of TinyML in healthcare, showcasing its potential to optimize real-time health monitoring and diagnostic tools. The authors of [19] optimized a Convolutional Neural Network (CNN) model through pruning and quantization, making it deployable on low-cost microcontrollers like the Raspberry Pi Pico and ESP32 for real-time blood pressure estimation using photoplethysmogram (PPG) signals. This approach enables efficient, low-power solutions for continuous blood pressure monitoring. The authors of [20] proposed a TinyML-based solution for predicting and detecting falls among elderly individuals, utilizing a wearable device placed on the leg to capture movement data. The system employs a nonlinear support vector machine classifier for real-time fall detection and prediction. Similarly, the authors of [21] used CNN models combined with audio data to detect falls. In [22], researchers developed a system that predicts blood glucose levels in individuals with type 1 diabetes by deploying DL models on edge devices. This system, which relies on recurrent neural networks (RNNs), processes continuous glucose monitoring (CGM) data on low-power, resource-constrained devices, enabling real-time monitoring without the need for cloud infrastructure. Additionally, the authors of [23] introduced a lightweight solution based on Temporal Convolutional Networks (TCNs) for heart rate estimation in wearable devices. By leveraging optimized TCN models, the system achieved accurate heart rate monitoring while maintaining low latency and energy consumption, making it suitable for use in resource-constrained environments like wearable health devices. In this paper, we present a new approach focused on creating TinyML models to distinguish between asthma and non-asthma conditions by using lung sound recordings. We developed and compared various ML models based on different metrics. To ensure these models are suitable for cost-effective platforms such as the Arduino Nano 33 BLE, we employed quantization techniques. The remainder of the paper is organized as follows: Section 2 reviews existing studies that have addressed similar challenges.