ACOUSTIC FEATURE ANALYSIS FOR COUGH SOUND SIGNAL DETECTION USING WSN AND IOT
Keywords:
Sound Sensof, Sensor Network, AI Algorithm, Node MCU, OLED Display, 3 Axis Accelerometer, Pressure SensorAbstract
Cough is acoustic wave. Acoustics of cough contain large amount of vital information about the respiratory system. By analyzing cough features and cough event detection will play an important role in stimulating the health care practices. The application of internet of things and wireless sensor networking for respiratory disease prediction has created tremendous and future possibilities in medical domain. The cough signature detecting sensor network and diagnostic algorithms are proposed in this paper. This paper investigates the reasons for cough and the features of cough for respiratory modalities. This paper analyzes the different acoustic properties of cough and cough related sound like sneezing, throat clearing, clapping and knocking sound. To detect cough sound sensors such as sound sensor, pressure sensor and 3 axis accelerometer sensor are connected with node MCU and features of cough are detected using algorithm and displayed. Data of different audio sound of cough is collected and compared with various ground noises in practical environments. The collected details could be transmitted to the Cloud platform, if necessary for analysis.
References
Yuhan Chen, PankajAttri, Jeffrey Barahona, Michelle L. Hernandez, Delesha Carpenter, AlperBozkurt, Edgar Lobaton, "Robust Cough Detection With Out-of-Distribution Detection", IEEE Journal of Biomedical and Health Informatics, vol.27, no.7, pp.3210-3221, 2023.
Madhurananda Pahar, Igor Miranda, Andreas Diacon, Thomas Niesler, "Accelerometer-Based Bed Occupancy Detection for Automatic, Non-Invasive Long-Term Cough Monitoring", IEEE Access, vol.11, pp.30739-30752, 2023.
Qurat-ul-Ain Arshad, Wazir Zada Khan, Faisal Azam, Muhammad Khurram Khan, "Deep-Learning-Based COVID-19 Detection: Challenges and Future Directions", IEEE Transactions on Artificial Intelligence, vol.4, no.2, pp.210-228, 2023.
Maggie Ezzat Gaber Gendy, Mehmet Rasit Yuce, "Emerging Technologies Used in Health Management and Efficiency Improvement During Different Contact Tracing Phases Against COVID-19 Pandemic", IEEE Reviews in Biomedical Engineering, vol.16, pp.38-52, 2023.
Ma, W., Zhao, Y., Guo, L., Chen, Y.Q., “Qualitative and quantitative analysis of the COVID-19 pandemic by a two-side fractional-order compartmental model”, ISA Trans. 2022, vol.124, pp.144–156.
Baleanu, D., Mohammadi, H., Rezapour, S.A, “Fractional differential equation model for the COVID-19 transmission by using the Caputo–Fabrizio derivative”, Advanced Differential Equations, 2020, vol. 299, pp.1–27.
Luz, E. Silva, P., Silva, R., Silva, L., Guimarães, J., Miozzo, G., Moreira, G., Menotti, D. ”Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images”, Res. Biomed. Eng. 2022, vol.38, pp.149–162.
Martinek J, Tatar M, Javorka M. ”Distinction between voluntary cough sound and speech in volunteers by spectral and complexity analysis”, J Physiol Pharmacol. 2008; vol.59(6), pp.433–440.
Tracey BH, Comina G, Larson S, Bravard M, Lopez JW, Gilman RH, “Cough detection algorithm for monitoring patient recovery from pulmonary tuberculosis”, In: IEEE EMBC, 2011, pp. 6017–6020.
Swarnkar V, Abeyratne UR, Amrulloh Y, Hukins C, Triasih R, Setyati A. ,” Neural network based algorithm for automatic identification of cough sounds”, In: IEEE EMBC, 2013, pp.1764–1767.
Amrulloh YA, Abeyratne UR, Swarnkar V, Triasih R, Setyati A., ”Automatic cough segmentation from non-contact sound recordings in pediatric wards”, Biomed Signal Process Control. 2015, vol.21, pp.126–136.
Matos S, Birring SS, Pavord ID, Evans H. “Detection of cough signals in continuous audio recordings using Hidden Markov Models”, IEEE Trans Biomed Eng. 2006, vol.53(6), pp.1078–1083.
Swarnkar V, Abeyratne U, Chang A, Amrulloh Y, Setyati A, Triasih R, “Automatic Identification of Wet and Dry Cough in Pediatric Patients with Respiratory Diseases”, Ann Biomed Eng. 2013, vol.41(5), pp.1016–1028.
Kosasih K, Abeyratne UR, Swarnkar V, Triasih R. “Wavelet Augmented Cough Analysis for Rapid Childhood Pneumonia Diagnosis”, IEEE Trans Biomed Engineering, 2014, vol. 62(4), pp.1185–1194.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 S.R.Vijayalakshmi , A.P.Rajesh (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
