Real-time deep learning-assisted mechano-acoustic system for respiratory diagnosis and multifunctional classification
Hee Kyu Lee, Sang Uk Park, Sunga Kong, Heyin Ryu, Hyun Bin Kim, Sang Hoon Lee, Danbee Kang, Sun Hye Shin, Ki Jun Yu, Juhee Cho, Joohoon Kang, Il Yong Chun, Hye Yun Park, Sang Min Won
Abstract
Epidermally mounted sensors using triaxial accelerometers have been previously used to monitor physiological processes with the implementation of machine learning (ML) algorithm interfaces. The findings from these previous studies have established a strong foundation for the analysis of high-resolution, intricate signals, typically through frequency domain conversion. In this study we integrate a wireless mechano-acoustic sensor with a multi-modal deep learning system for the real-time analysis of signals emitted by the laryngeal prominence area of the thyroid cartilage at frequency ranges up to 1 kHz. This interface provides real-time data visualization and communication with the ML server, creating a system that assesses severity of chronic obstructive pulmonary disease and analyzes the user’s speech patterns.