Potential Pneumoconiosis Patients Monitoring and Warning System with Acoustic Signal
Monitoring for early symptoms is a critical step in preventing pneumoconiosis. The early signs of pneumoconiosis can be characterized by dyspnea, tachypnea, and cough. While traditional sensor-based methods are promising, they necessitate the wearing of devices and confine human physical movements....
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MDPI AG
2025-03-01
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| author | Zhongxu Bao Baoxuan Xu Xuehan Zhang Yuqing Yin Xu Yang Qiang Niu |
| author_facet | Zhongxu Bao Baoxuan Xu Xuehan Zhang Yuqing Yin Xu Yang Qiang Niu |
| author_sort | Zhongxu Bao |
| collection | DOAJ |
| description | Monitoring for early symptoms is a critical step in preventing pneumoconiosis. The early signs of pneumoconiosis can be characterized by dyspnea, tachypnea, and cough. While traditional sensor-based methods are promising, they necessitate the wearing of devices and confine human physical movements. On the other hand, camera-based methods have issues related to illumination, obstruction, and privacy. Recently, wireless sensing has attracted a significant amount of research attention. Among wireless signals, acoustic signals possess unique advantages for fine-grained sensing due to their low propagation speed in the air and low hardware requirement. In this paper, we propose a system called <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>P</mi><mn>3</mn></msup><mi>W</mi><mi>a</mi><mi>r</mi><mi>n</mi><mi>i</mi><mi>n</mi><mi>g</mi></mrow></semantics></math></inline-formula> to realize low-cost warnings for potential pneumoconiosis patients in a contactless manner. For the first time, the designed system utilizes the inaudible acoustic signal to monitor early symptoms of pneumoconiosis (i.e., abnormal respiration and cough), leveraging a pair of commercial speaker and microphone. We introduce and address unique technical challenges, such as formulating a delay elimination method to synchronize transceiver signals and providing a search-based strategy to amplify signal variation for accurate and long-distance vital sign sensing. Ultimately, we apply an innovative signal decomposition technique to reconstruct the respiration waveform and extract features for cough detection. Comprehensive experiments were conducted to evaluate <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>P</mi><mn>3</mn></msup><mi>W</mi><mi>a</mi><mi>r</mi><mi>n</mi><mi>i</mi><mi>n</mi><mi>g</mi></mrow></semantics></math></inline-formula>. Experiment results show that it can achieve a robust performance with a median error of 0.39 bpm for abnormal respiration pattern monitoring and an accuracy of 95% for cough detection in total, and support the furthest sensing range of up to 4 m. |
| format | Article |
| id | doaj-art-b29b97aecbc24256bfd88f25b1d5c9d2 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
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| spelling | doaj-art-b29b97aecbc24256bfd88f25b1d5c9d22025-08-20T02:43:03ZengMDPI AGSensors1424-82202025-03-01256187410.3390/s25061874Potential Pneumoconiosis Patients Monitoring and Warning System with Acoustic SignalZhongxu Bao0Baoxuan Xu1Xuehan Zhang2Yuqing Yin3Xu Yang4Qiang Niu5State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan 232000, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, ChinaMonitoring for early symptoms is a critical step in preventing pneumoconiosis. The early signs of pneumoconiosis can be characterized by dyspnea, tachypnea, and cough. While traditional sensor-based methods are promising, they necessitate the wearing of devices and confine human physical movements. On the other hand, camera-based methods have issues related to illumination, obstruction, and privacy. Recently, wireless sensing has attracted a significant amount of research attention. Among wireless signals, acoustic signals possess unique advantages for fine-grained sensing due to their low propagation speed in the air and low hardware requirement. In this paper, we propose a system called <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>P</mi><mn>3</mn></msup><mi>W</mi><mi>a</mi><mi>r</mi><mi>n</mi><mi>i</mi><mi>n</mi><mi>g</mi></mrow></semantics></math></inline-formula> to realize low-cost warnings for potential pneumoconiosis patients in a contactless manner. For the first time, the designed system utilizes the inaudible acoustic signal to monitor early symptoms of pneumoconiosis (i.e., abnormal respiration and cough), leveraging a pair of commercial speaker and microphone. We introduce and address unique technical challenges, such as formulating a delay elimination method to synchronize transceiver signals and providing a search-based strategy to amplify signal variation for accurate and long-distance vital sign sensing. Ultimately, we apply an innovative signal decomposition technique to reconstruct the respiration waveform and extract features for cough detection. Comprehensive experiments were conducted to evaluate <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>P</mi><mn>3</mn></msup><mi>W</mi><mi>a</mi><mi>r</mi><mi>n</mi><mi>i</mi><mi>n</mi><mi>g</mi></mrow></semantics></math></inline-formula>. Experiment results show that it can achieve a robust performance with a median error of 0.39 bpm for abnormal respiration pattern monitoring and an accuracy of 95% for cough detection in total, and support the furthest sensing range of up to 4 m.https://www.mdpi.com/1424-8220/25/6/1874inaudible acoustic sensingpneumoconiosiscontactless sensingcommercial device |
| spellingShingle | Zhongxu Bao Baoxuan Xu Xuehan Zhang Yuqing Yin Xu Yang Qiang Niu Potential Pneumoconiosis Patients Monitoring and Warning System with Acoustic Signal Sensors inaudible acoustic sensing pneumoconiosis contactless sensing commercial device |
| title | Potential Pneumoconiosis Patients Monitoring and Warning System with Acoustic Signal |
| title_full | Potential Pneumoconiosis Patients Monitoring and Warning System with Acoustic Signal |
| title_fullStr | Potential Pneumoconiosis Patients Monitoring and Warning System with Acoustic Signal |
| title_full_unstemmed | Potential Pneumoconiosis Patients Monitoring and Warning System with Acoustic Signal |
| title_short | Potential Pneumoconiosis Patients Monitoring and Warning System with Acoustic Signal |
| title_sort | potential pneumoconiosis patients monitoring and warning system with acoustic signal |
| topic | inaudible acoustic sensing pneumoconiosis contactless sensing commercial device |
| url | https://www.mdpi.com/1424-8220/25/6/1874 |
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