This paper presents a comprehensive review of cardiorespiratory auscultation
sensing devices which is useful for understanding the theoretical aspects of
sensing devices, as well as practical notes to design novel sensing devices.
One of the methods to design a stethoscope is using electret condenser
microphones (ECM). In this paper, we first introduce the acoustic properties of
the heart and lungs, as well as a brief history of stethoscope evolution. Then,
we discuss the basic concept of ECM sensors and a recent stethoscope based on
this technology. In response to the limitations of ECM-based systems, we
explore the potential of microelectromechanical systems (MEMS), particularly
focusing on piezoelectric transducer (PZT) sensors. This paper comprehensively
reviews sensing technologies, emphasizing innovative MEMS-based designs for
wearable cardiopulmonary auscultation in the past decade. To our knowledge,
this is the first paper to summarize ECM and MEMS applications for heart and
lung sound analysis. Keywords: Micro-electro-mechanical Systems (MEMS);
Electret Condenser Microphone (ECM); Wearable Sensing Devices;
Cardiorespiratory Auscultation; Phonocardiography (PCG); Heart Sound; Lung
Sound

本文综述了心肺听诊感应设备方面的研究，重点介绍了电容电浆麦克风和微电子机械系统在可穿戴式心肺听诊方面的创新应用，同时对心肺听诊声音分析的领域进行了归纳总结。

探索心肺声音监测的传感器设备

Exploring Sensing Devices for Heart and Lung Sound Monitoring

Many industrial and real life problems exhibit highly nonlinear periodic
behaviors and the conventional methods may fall short of finding their
analytical or closed form solutions. Such problems demand some cutting edge
computational tools with increased functionality and reduced cost. Recently,
deep neural networks have gained massive research interest due to their ability
to handle large data and universality to learn complex functions. In this work,
we put forward a methodology based on deep neural networks with responsive
layers structure to deal nonlinear oscillations in microelectromechanical
systems. We incorporated some oscillatory and non oscillatory activation
functions such as growing cosine unit known as GCU, Sine, Mish and Tanh in our
designed network to have a comprehensive analysis on their performance for
highly nonlinear and vibrational problems. Integrating oscillatory activation
functions with deep neural networks definitely outperform in predicting the
periodic patterns of underlying systems. To support oscillatory actuation for
nonlinear systems, we have proposed a novel oscillatory activation function
called Amplifying Sine Unit denoted as ASU which is more efficient than GCU for
complex vibratory systems such as microelectromechanical systems. Experimental
results show that the designed network with our proposed activation function
ASU is more reliable and robust to handle the challenges posed by nonlinearity
and oscillations. To validate the proposed methodology, outputs of our networks
are being compared with the results from Livermore solver for ordinary
differential equation called LSODA. Further, graphical illustrations of
incurred errors are also being presented in the work.

通过引入具有振荡激活功能的深度神经网络结构，本文提出了一种应对微电机系统中非线性振动问题的方法，其中还使用了一些振荡和非振荡激活功能以进行综合分析；实验结果表明，此设计网络是更可靠和稳健的，可以有效应对非线性和振动的挑战。