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.

使用三轴加速度计的表皮安装传感器以前用于监测生理过程，并实施了机器学习 （ML） 算法接口。这些先前研究的结果为分析高分辨率、复杂的信号奠定了坚实的基础，通常通过频域转换。在这项研究中，我们将无线机械声学传感器与多模态深度学习系统集成在一起，用于实时分析甲状软骨喉部突出区域在高达 1 kHz 的频率范围内发出的信号。该接口提供实时数据可视化和与 ML 服务器的通信，从而创建一个系统来评估慢性阻塞性肺病的严重程度并分析用户的语音模式。