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REVIEW PAPER
Machine learning and IoT system for real-time cough detection and classification
1
Medical University of Lublin
2
WSEI University
3
Netrix S.A.
Submission date: 2024-06-28
Acceptance date: 2024-07-20
Publication date: 2024-08-20
JoMS 2024;57(Numer specjalny 3):772-782
KEYWORDS
TOPICS
ABSTRACT
Our research investigates the application of machine learning and Internet of Things (IoT) technologies in healthcare, focusing on detecting and classifying coughing episodes. Leveraging deep learning architectures and a comprehensive IoT infrastructure, we developed an automated system capable of monitoring audio signals from a microphone array module to detect coughs and classify their types accurately. The study utilized the COUGHVID dataset for model training and evaluation, employing rigorous preprocessing techniques to ensure data integrity. Through comparative analysis, we identified MobileNet as the optimal model for cough detection, achieving promising results in accuracy, area under the ROC curve (AUC), and F1 score. Furthermore, our emphasis on privacy safeguards and remote medical examination facilitation underscores the practical implications of our research in enhancing healthcare delivery. Our study contributes to advancing technology-enabled healthcare solutions, offering valuable insights and solutions for improving patient care and outcomes.
REFERENCES (20)
1.
Abaza, A.A., Day, J.B., Reynolds, J.S., Mahmoud, A.M., Goldsmith, W.T., McKinney, W.G., Petsonk, E.L., Frazer, D.G., (2009). Classification of Voluntary Cough Sound and Airflow Patterns for Detecting Abnormal Pulmonary Function. Cough 5(1):8. doi: 10.1186/1745-9974-5-8.
2.
Anon. n.d. (2024)Performance Evaluation of the Nvidia Jetson Nano Through a Real-Time Machine Learning Application | SpringerLink. Retrieved April 27, (https://link.springer.com/chap...).
3.
Atmoko, R. A., R. Riantini, and M. K. Hasin. (2017). IoT Real-Time Data Acquisition Using MQTT Protocol. Journal of Physics: Conference Series 853(1):012003. doi: 10.1088/1742-6596/853/1/012003.
4.
Balamurali, B. T., Hwan Ing Hee, Saumitra Kapoor, Oon Hoe Teoh, Sung Shin Teng, Khai Pin Lee, Dorien Herremans, and Jer Ming Chen. (2021). Deep Neural Network-Based Respiratory Pathology Classification Using Cough Sounds. Sensors 21(16):5555. doi: 10.3390/s21165555.
5.
Bansal, Vipin, Gaurav Pahwa, and Nirmal Kannan. (2020). Cough Classification for COVID-19 Based on Audio Mfcc Features Using Convolutional Neural Networks. Pp. 604–8 in 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON).
6.
Bhatia, Nikhil. (2020). Using Transfer Learning, Spectrogram Audio Classification, and MIT App Inventor to Facilitate Machine Learning Understanding. Thesis, Massachusetts Institute of Technology.
7.
Gunawan, Kevin, Alam Ahmad Hidayat, Tjeng Wawan Cenggoro, and Bens Pardamean. (2021). A Transfer Learning Strategy for Owl Sound Classification by Using Image Classification Model with Audio Spectrogram. International Journal on Electrical Engineering and Informatics 17:546. doi: 10.15676/ijeei.2021.13.3.3.
8.
Maj, M., Rymarczyk, T., Cieplak, T., Pliszczuk, D. (2022). Deep Learning Model Optimization for Faster Inference Using Multi-Task Learning for Embedded Systems. Pp. 892–93 in Proceedings of the 28th Annual International Conference on Mobile Computing And Networking, MobiCom ’22. New York, NY, USA: Association for Computing Machinery.
9.
Maj, M., Rymarczyk, T., Cieplak, T., Pliszczuk, D. (2023). Cross-Modal Perception for Customer Service. Pp. 1–3 in Proceedings of the 29th Annual International Conference on Mobile Computing and Networking. New York, NY, USA: Association for Computing Machinery.
10.
Mittapalli, Pardha Saradhi, M. R. N. Tagore, Pulagam Ammi Reddy, Giri Babu Kande, and Y. Mallikarjuna Reddy. (2023). Deep Learning Based Real-Time Object Detection on Jetson Nano Embedded GPU. Pp. 511–21 in Microelectronics, Circuits and Systems, edited by A. Biswas, A. Islam, R. Chaujar, and O. Jaksic. Singapore: Springer Nature.
11.
Pahar, Madhurananda, Marisa Klopper, Robin Warren, and Thomas Niesler. (2021). COVID-19 Cough Classification Using Machine Learning and Global Smartphone Recordings. Computers in Biology and Medicine 135:104572. doi: 10.1016/j.compbiomed.2021.104572.
12.
Rymarczyk, T., Cieplak, T., Kłosowski, G., Kozłowski. E. (2020). The Use of LSTM Networks in the Detection of Outliers in IoT – Based Air Quality Monitoring Systems. Przeglad Elektrotechniczny 5:91–94.
13.
Serrurier, Antoine, Christiane Neuschaefer-Rube, and Rainer Röhrig. (2022). Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review. Sensors 22(8):2896. doi: 10.3390/s22082896.
14.
Shin, Woochang. (2020). Implementation of Cough Detection System Using IoT Sensor in Respirator. International Journal of Advanced Smart Convergence 9(4):132–38. doi: 10.7236/IJASC.2020.9.4.132.
15.
Singh, Meena, M. A. Rajan, V. L. Shivraj, and P. Balamuralidhar. (2015). Secure MQTT for Internet of Things (IoT). Pp. 746–51 in 2015 Fifth International Conference on Communication Systems and Network Technologies.
16.
Prashanthi, S.K., Kesanapalli, S.A., Simmhan, Y. (2022). Characterizing the Performance of Accelerated Jetson Edge Devices for Training Deep Learning Models. Proceedings of the ACM on Measurement and Analysis of Computing Systems 6(3):44:1-44:26. doi: 10.1145/3570604.
17.
Styła, M., Adamkiewicz, P., Niderla, K., Rymarczyk. T. (2021). Indoor Navigation System Using Radio Tomography. Journal of Physics: Conference Series 1782(1):012035. doi: 10.1088/1742-6596/1782/1/012035.
18.
Takahashi, Shinya, Tsuyoshi Morimoto, Sakashi Maeda, Naoyuki Tsuruta. (2004). Cough Detection in Spoken Dialogue System for Home Health Care.
19.
Wang, Hui-Hui, Jia-Ming Liu, Mingyu You, Guo-Zheng Li. (2015). Audio Signals Encoding for Cough Classification Using Convolutional Neural Networks: A Comparative Study. Pp. 442–45 in 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
20.
Yassein, Muneer Bani, Mohammed Q. Shatnawi, Shadi Aljwarneh, and Razan Al-Hatmi. (2017). Internet of Things: Survey and Open Issues of MQTT Protocol. Pp. 1–6 in 2017 International Conference on Engineering & MIS (ICEMIS).
| eISSN: | 2391-789X |
| ISSN: | 1734-2031 |
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