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@article{195303,
        author = {G. Anushruth sai Sharma and Sai Vignesh and Sai Sri Ram and Sai Kiran and T.Lavanya and Dr. S Shiva Prasad},
        title = {BREATHING RATE CLASSIFICATION USING PIEZORESISTIVE SENSOR UTILIZING CONTINUOUS WAVELET TRANSFORM AND LIGHTWEIGHT CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {7886-7892},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195303},
        abstract = {Breathing rate monitoring has become increasingly feasible for remote healthcare applications due to recent advancements in digital stethoscope sensor technology, signal processing, and machine learning. Automatic breathing rate classification further enhances medical diagnostics by enabling accurate, continuous, and non-invasive respiratory assessment. In this paper, a lightweight Convolutional Neural Network (CNN) is proposed for automatic breathing rate classification using piezoresistive sensor data. The raw signals acquired from the piezoresistive sensor are pre-processed using Continuous Wavelet Transform (CWT) to generate time–frequency representation images. These images are then fed into a lightweight CNN model, which efficiently classifies breathing rates into six distinct classes based on breaths per minute (BPM). Extensive experimental results demonstrate that the proposed model achieves a classification accuracy of 96.40%, outperforming all benchmark models considered in this study. Additionally, the performance of the proposed system is evaluated on edge computing platforms such as Raspberry Pi, NVIDIA Jetson Nano, and NVIDIA AGX Xavier, confirming its suitability for real-time and resource-constrained healthcare applications.},
        keywords = {Breathing rate classification, breathing sensor, continuous wavelet transforms, deep convolutional neural network, machine learning, piezoresistive sensor.},
        month = {March},
        }