Combination of Artificial Intelligence and Continuous Wave Radar Sensor in Diagnosing Breathing Disorder

N T P Van; L Tang; S F Hasan; S Mukhopadhyay; N D Minh

doi:10.1007/978-981-15-2780-7_91

Combination of Artificial Intelligence and Continuous Wave Radar Sensor in Diagnosing Breathing Disorder

N T P Van
, L Tang
, S F Hasan
, S Mukhopadhyay
, N D Minh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

The combination of artificial intelligence (AI) and sensor systems enables many applications. This work shows an innovative solution to test the performance of smart Continuous Wave (CW) Doppler radar sensor system by investigating the mathematical models for receiving signals from different breathing disorder patients. CW radar sensor has many good features such as simple hardware structure, narrow bandwidth frequency, and high sensitivity in detecting the chest movement of humans remotely. Based on the developed berating theoretical models, data sets at different values of signal-to-noise ratio (SNR) are generated. The next step is to apply machine learning technique on this data set to categorize breathing disorder patterns. The results show the high potential application of smart radar sensor in diagnosing disorder sleeping problems.

Original language	English
Title of host publication	Intelligent Computing in Engineering
Editors	Vijender Kumar Solanki, Manh Kha Hoang, Zhonghyu (Joan) Lu, Prasant Kumar Pattnaik
Place of Publication	Singapore
Pages	853-863
Volume	1125
Edition	1
DOIs	https://doi.org/10.1007/978-981-15-2780-7_91
Publication status	Published - 10 Apr 2020

Publication series

Name	Advances in Intelligent Systems and Computing (AISC)
Number	1125

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10.1007/978-981-15-2780-7_91

https://hdl.handle.net/1959.11/72200

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Van, N. T. P., Tang, L., Hasan, S. F., Mukhopadhyay, S., & Minh, N. D. (2020). Combination of Artificial Intelligence and Continuous Wave Radar Sensor in Diagnosing Breathing Disorder. In V. K. Solanki, M. K. Hoang, Z. Lu, & P. K. Pattnaik (Eds.), Intelligent Computing in Engineering (1 ed., Vol. 1125, pp. 853-863). (Advances in Intelligent Systems and Computing (AISC); No. 1125).. https://doi.org/10.1007/978-981-15-2780-7_91

Van, N T P ; Tang, L ; Hasan, S F et al. / Combination of Artificial Intelligence and Continuous Wave Radar Sensor in Diagnosing Breathing Disorder. Intelligent Computing in Engineering. editor / Vijender Kumar Solanki ; Manh Kha Hoang ; Zhonghyu (Joan) Lu ; Prasant Kumar Pattnaik. Vol. 1125 1. ed. Singapore, 2020. pp. 853-863 (Advances in Intelligent Systems and Computing (AISC); 1125).

@inproceedings{cb6f2a5697564440b568665bf8a35ec0,

title = "Combination of Artificial Intelligence and Continuous Wave Radar Sensor in Diagnosing Breathing Disorder",

abstract = "The combination of artificial intelligence (AI) and sensor systems enables many applications. This work shows an innovative solution to test the performance of smart Continuous Wave (CW) Doppler radar sensor system by investigating the mathematical models for receiving signals from different breathing disorder patients. CW radar sensor has many good features such as simple hardware structure, narrow bandwidth frequency, and high sensitivity in detecting the chest movement of humans remotely. Based on the developed berating theoretical models, data sets at different values of signal-to-noise ratio (SNR) are generated. The next step is to apply machine learning technique on this data set to categorize breathing disorder patterns. The results show the high potential application of smart radar sensor in diagnosing disorder sleeping problems.",

author = "Van, \{N T P\} and L Tang and Hasan, \{S F\} and S Mukhopadhyay and Minh, \{N D\}",

year = "2020",

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language = "English",

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Van, NTP, Tang, L, Hasan, SF, Mukhopadhyay, S & Minh, ND 2020, Combination of Artificial Intelligence and Continuous Wave Radar Sensor in Diagnosing Breathing Disorder. in VK Solanki, MK Hoang, Z Lu & PK Pattnaik (eds), Intelligent Computing in Engineering. 1 edn, vol. 1125, Advances in Intelligent Systems and Computing (AISC), no. 1125, Singapore, pp. 853-863. https://doi.org/10.1007/978-981-15-2780-7_91

Combination of Artificial Intelligence and Continuous Wave Radar Sensor in Diagnosing Breathing Disorder. / Van, N T P; Tang, L; Hasan, S F et al.
Intelligent Computing in Engineering. ed. / Vijender Kumar Solanki; Manh Kha Hoang; Zhonghyu (Joan) Lu; Prasant Kumar Pattnaik. Vol. 1125 1. ed. Singapore, 2020. p. 853-863 (Advances in Intelligent Systems and Computing (AISC); No. 1125).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Combination of Artificial Intelligence and Continuous Wave Radar Sensor in Diagnosing Breathing Disorder

AU - Van, N T P

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AU - Hasan, S F

AU - Mukhopadhyay, S

AU - Minh, N D

PY - 2020/4/10

Y1 - 2020/4/10

N2 - The combination of artificial intelligence (AI) and sensor systems enables many applications. This work shows an innovative solution to test the performance of smart Continuous Wave (CW) Doppler radar sensor system by investigating the mathematical models for receiving signals from different breathing disorder patients. CW radar sensor has many good features such as simple hardware structure, narrow bandwidth frequency, and high sensitivity in detecting the chest movement of humans remotely. Based on the developed berating theoretical models, data sets at different values of signal-to-noise ratio (SNR) are generated. The next step is to apply machine learning technique on this data set to categorize breathing disorder patterns. The results show the high potential application of smart radar sensor in diagnosing disorder sleeping problems.

AB - The combination of artificial intelligence (AI) and sensor systems enables many applications. This work shows an innovative solution to test the performance of smart Continuous Wave (CW) Doppler radar sensor system by investigating the mathematical models for receiving signals from different breathing disorder patients. CW radar sensor has many good features such as simple hardware structure, narrow bandwidth frequency, and high sensitivity in detecting the chest movement of humans remotely. Based on the developed berating theoretical models, data sets at different values of signal-to-noise ratio (SNR) are generated. The next step is to apply machine learning technique on this data set to categorize breathing disorder patterns. The results show the high potential application of smart radar sensor in diagnosing disorder sleeping problems.

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Van NTP, Tang L, Hasan SF, Mukhopadhyay S, Minh ND. Combination of Artificial Intelligence and Continuous Wave Radar Sensor in Diagnosing Breathing Disorder. In Solanki VK, Hoang MK, Lu Z, Pattnaik PK, editors, Intelligent Computing in Engineering. 1 ed. Vol. 1125. Singapore. 2020. p. 853-863. (Advances in Intelligent Systems and Computing (AISC); 1125). doi: 10.1007/978-981-15-2780-7_91

Combination of Artificial Intelligence and Continuous Wave Radar Sensor in Diagnosing Breathing Disorder

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