Work

Deep Learning Integration for Normal Breathing Classification Using a Flexible Fiber Sensor

Year: 2024

Type: article

Abstract: Measuring respiratory parameters is crucial for clinical decision making and detecting abnormal patterns for disease prevention. While deep learning methods are commonly used in respiratory analysis, the image-based classification of abnormal breathing remains limited. This study developed a stitched sensor using silver-coated thread, optimized for the knit fabric’s course direction in a belt configuration. By applying a Continuous Wavelet Transform (CWT) and a two-dimension Convolutional Neural Network (2D-CNN), the model achieved 96% accuracy, with potential for further improvement through data expansion.

Source: Processes

DOI: 10.3390/pr12122644

Authors: Jiseon Kim, Jiseon Kim, Jooyong Kim, Jooyong Kim

Institution: Soongsil University

Language: English

Topic: Non-Invasive Vital Sign Monitoring

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,728