AI-enabled wireless wearable breathing sensor for breathing pattern recognition

This paper presents an AI-driven multisensor wearable system for real-time breathing pattern recognition by integrating an inertial measurement unit (IMU) and a flex sensor with wireless data connectivity. Three artificial intelligence models-transformer, convolutional neural network-long short-term memory (CNN-LSTM), and histogram gradient boosting (HGB)-were evaluated for breathing pattern recognition across different model complexities (complex, simple, pure) and sensor configurations (IMU , Flex , and combined). The multisensor system combined with AI model was tested with multiple participants. The complex transformer model, trained with focal loss on the combined IMU and flex sensor data, achieved the highest performance, with 93.41% accuracy and a mean area under the curve (AUC) of 0.9919, outperforming all the other models. Multimodal input significantly improved classification accuracy-up to 20% higher than flex sensor models in six-class tasks-while focal loss enhanced robustness, particularly in addressing class imbalance. These results demonstrate the potential of combining wearable sensor fusion with deep learning to enable accurate, noninvasive, and wireless real-time respiratory monitoring, with potential applications in clinical diagnostics, telemedicine, and personalized health tracking.