tinyML EMEA - W. Hong Yeo: Smart and Connected Soft Biomedical Stethoscope and Machine Learning...
Smart and Connected Soft Biomedical Stethoscope and Machine Learning for Continuous Real-Time Auscultation and Automated Disease Detection
W. Hong YEO
Associate Professor and Woodruff Faculty Fellow
Georgia Tech
In this work, the computational mechanics study offers a key design guide for
developing a soft wearable system, maintaining mechanical reliability in multiple uses
with bending and stretching. Optimizing a system packaging using biocompatible
elastomers and soft adhesives allows for skin-friendly, robust adhesion to the body while minimizing motion artifacts due to the stress distribution and conformable lamination. The soft device demonstrates a precise detection of high-quality cardiopulmonary sounds even with the subject’s different actions. Compared to commercial digital stethoscopes, the SWS using a wavelet denoising algorithm shows superior performance as validated by the enhanced signal-to-noise ratio. Deep-learning integration with the SWS demonstrates a successful application for a clinical study where the stethoscope is used for continuous, wireless auscultation with multiple patients. The results show automatic detection and diagnosis of four different types of lung diseases, such as crackle, wheeze, stridor, and rhonchi, with about 95% accuracy for five classes. Collectively, this work represents a major shift in how clinicians collect cardiopulmonary sounds for disease diagnosis and health monitoring.
In addition, Dr. Yeo will share how different printing processes are used to
manufacture nano-microscale sensors and circuit interconnects, while discussing the details of hard-soft materials integration and soft packaging strategies. He will also share other application examples of soft electronic platforms such as portable health monitoring devices, disease diagnostic devices, therapeutic systems, and human-machine interface systems. Finally, more details of sensor design, circuits, manufacturing, system optimization, signal processing, machine learning, and data classification will be shared at high levels.
