Clapping and Vibrating Caring to Address Ineffective Airway Clearance Based on: Neural Network (Preprint)

<sec> <title>BACKGROUND</title> The accumulation of mucus in the airways is a serious health problem as it can obstruct airflow and impair lung function. This condition is typically managed by suctioning the mucus and performing manual chest clapping, which involves repeatedly patting the back. However, manual clapping is often ineffective and inefficient due to factors like operator fatigue and inconsistent application. This research introduces a portable clapping system called Clapping and Heater Integrated Caring device (CHIC) </sec> <sec> <title>OBJECTIVE</title> this research is to develop an automatic detection system that can reliably recognize human clapping and hand induced vibrations using wearable sensors. The study aims to design and implement pattern recognition algorithms, ranging from threshold based methods to lightweight machine learning models, that differentiate intentional clapping or vibrating gestures from environmental noise, and to evaluate the system’s real time performance in terms of accuracy, latency, and energy consumption across diverse usage scenarios. </sec> <sec> <title>METHODS</title> This research introduces an innovative medical device that integrates automatic clapping capabilities with a vibrating warm pillow. The goal of this innovation, named CHIC, is to overcome the limitations of manual methods and enhance the effectiveness of chest physiotherapy. A key feature that makes CHIC so relevant is the use of Neural Network (NN) technology to determine the number of claps based on the patient's condition </sec> <sec> <title>RESULTS</title> A Neural Network (NN) allows the system to adaptively generate the optimal clapping frequency based on the patient's physiological parameters, ensuring a more precise and personalized therapy compared to conventional approaches. Three different NN architectures were tested, and the one with three hidden layers and a neuron configuration of [20 30 40] proved to be the most effective. This configuration yielded a Mean Squared Error (MSE) of 0.00029279 for the training data and a Root Mean Squared Error (RMSE) of 0.02812 for the validation data. </sec> <sec> <title>CONCLUSIONS</title> The CHIC device demonstrated reliable performance for airway clearance therapy. Its ESP32 based control system, temperature monitoring, vibrating pillow, and rotary DC motor provided consistent and coordinated clapping actions. The integrated neural network algorithm dynamically adjusted the clapping frequency according to the patient’s physiological parameters, delivering a more precise and personalized treatment compared to manual chest percussion. The system operated without operator fatigue and maintained patient comfort, indicating that CHIC is an effective and practical solution for clinical and community based physiotherapy applications </sec>