Smart Machine Learning-based IoT Health and Cough Monitoring System

Wai Leong Pang, Gwo Chin Chung, Kah Yoong Chan, Lee It Ee, Mardeni Roslee, Edzham Fitrey, Yee Wai Sim, Murman Dwi Prasetio


Coronavirus 2019, more commonly known as COVID-19, was declared a global pandemic by the World Health Organization (WHO) on March 11, 2020. The β coronavirus culpable for the disease, SARS CoV-2, is known to be highly contagious with a relatively long incubation period of up to 14 days and is transmittable through small droplets, especially among people who are in close face-to-face contact. The Ministry of Health of Malaysia has recommended five days of quarantine for people who are positive for COVID-19 to avoid further disease transmission. Many resources are used to monitor patients throughout the quarantine period. Therefore, this project would like to present an IoT-enabled wearable device capable of monitoring COVID-19 quarantine patients by utilizing sensors to monitor the necessary health parameters and facilitate home quarantine. The low-cost ESP32 and Arduino Nano 33 BLE Sense microcontrollers are used in this device. They are connected to various IoT sensors to collect temperature, humidity, and sound data. The data obtained will then be uploaded to an IoT platform for doctors to analyze and monitor remotely via the health log throughout the 5-day quarantine period. An alert system is also devised to inform the medical staff if the patient is experiencing abnormal symptoms. The medical staff can then bring their attention to the patient and take the necessary actions to combat COVID-19.


COVID-19; wearable health monitoring system; cough detection system

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V. Chamola, V. Hassija, V. Gupta, and M. Guizani, "A comprehensive review of the COVID-19 pandemic and the role of IoT, drones, AI, blockchain, and 5G in managing its impact," IEEE Access, vol. 8, no. April, pp. 90225–90265, 2020, doi: 10.1109/ACCESS.2020.2992341.

B. Farahani, F. Firouzi, and M. Luecking, "The convergence of IoT and distributed ledger technologies (DLT): Opportunities, challenges, and solutions," J. Netw. Comput. Appl., vol. 177, no. September 2020, p. 102936, 2021, doi: 10.1016/j.jnca.2020.102936.

Z. Chang, S. Liu, X. Xiong, Z. Cai, and G. Tu, "A survey of recent advances in edge-computing-powered artificial intelligence of things," IEEE Internet Things J., vol. 8, no. 18, pp. 13849–13875, 2021, doi: 10.1109/JIOT.2021.3088875.

A. Barnawi, P. Chhikara, R. Tekchandani, N. Kumar, and B. Alzahrani, "Artificial intelligence-enabled Internet of things-based system for COVID-19 screening using aerial thermal imaging," Futur. Gener. Comput. Syst., vol. 124, pp. 119–132, 2021, doi: 10.1016/j.future.2021.05.019.

S. Shriram, B. Nagaraj, J. Jaya, S. Shankar, and P. Ajay, "Deep learning-based real-time AI virtual mouse system using computer vision to avoid COVID-19 spread," J. Healthc. Eng., vol. 2021, 2021, doi: 10.1155/2021/8133076.

Defni, - Johan, A. E. Putra, F. Nova, and W. Andriani, "Utilizing requirement testing methods on web-based swab data information system," Int. J. Adv. Sci. Comput. Eng., vol. 4, no. 1, pp. 22–31, Mar. 2022, doi: 10.30630/ijasce.4.1.75.

S. Jain et al., "Internet of medical things (IoMT)-integrated biosensors for point-of-care testing of infectious diseases," Biosens. Bioelectron., vol. 179, no. September 2020, p. 113074, 2021, doi: 10.1016/j.bios.2021.113074.

N. Y. Philip, J. J. P. C. Rodrigues, H. Wang, S. J. Fong, and J. Chen, "Internet of things for in-home health monitoring systems: Current Advances, Challenges and Future Directions," IEEE J. Sel. Areas Commun., vol. 39, no. 2, pp. 300–310, 2021, doi: 10.1109/JSAC.2020.3042421.

X. Li, Y. Lu, X. Fu, and Y. Qi, "Building the Internet of things platform for smart maternal healthcare services with wearable devices and cloud computing," Futur. Gener. Comput. Syst., vol. 118, pp. 282–296, 2021, doi: 10.1016/j.future.2021.01.016.

B. Pradhan, S. Bhattacharyya, and K. Pal, "IoT-based applications in healthcare devices," J. Healthc. Eng., vol. 2021, 2021, doi: 10.1155/2021/6632599.

I. D. M. B. Filho, G. Aquino, R. S. Malaquias, G. Girao, and S. R. M. Melo, "An IoT-based healthcare platform for patients in ICU beds during the COVID-19 outbreak," IEEE Access, vol. 9, pp. 27262–27277, 2021, doi: 10.1109/ACCESS.2021.3058448.

W. R. Malatji, R. VanEck, and T. Zuva, "A systematic review of the adoption of eHealth cloud-based technology applications during COVID-19," Int. J. Adv. Sci. Comput. Eng., vol. 3, no. 2, pp. 53–64, Oct. 2021, doi: 10.30630/ijasce.3.2.44.

M. Alkhodari et al., "Screening cardiovascular autonomic neuropathy in diabetic patients with microvascular complications using machine learning: a 24-hour heart rate variability study," IEEE Access, vol. 9, pp. 119171–119187, 2021, doi: 10.1109/ACCESS.2021.3107687.

S. K. Mohamed, N. A. Sakr, and N. A. Hikal, "A review of breast cancer classification and detection techniques," Int. J. Adv. Sci. Comput. Eng., vol. 3, no. 3, pp. 128–139, Oct. 2021, doi: 10.30630/ijasce.3.3.55.

L. Brunese, F. Mercaldo, A. Reginelli, and A. Santone, "Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays," Comput. Methods Programs Biomed., vol. 196, p. 105608, Nov. 2020, doi: 10.1016/j.cmpb.2020.105608.

G. Guglielmi, "Fast coronavirus tests: what they can and can't do," Nature, vol. 585, no. 7826, pp. 496–498, Sep. 2020, doi: 10.1038/d41586-020-02661-2.

J. Dunn et al., "Wearable sensors enable personalized predictions of clinical laboratory measurements," Nat. Med., vol. 27, no. 6, pp. 1105–1112, 2021, doi: 10.1038/s41591-021-01339-0.

S. K., P. A. S., P. U., P. S., and S. P. V., "Patient health monitoring system using IoT," Mater. Today Proc., vol. 80, pp. 2228–2231, 2023, doi: 10.1016/j.matpr.2021.06.188.

N. Chinnamadha, R. Z. Ahmed, and K. Kalegowda, "Development of health monitoring system using smart intelligent device," Indones. J. Electr. Eng. Comput. Sci., vol. 28, no. 3, pp. 1381–1387, 2022, doi: 10.11591/ijeecs.v28.i3.pp1381-1387.

N. A. Ahmad Yani and M. F. Zolkipli, "Computerized senior citizen health monitoring using mobile application," Int. J. Adv. Sci. Comput. Eng., vol. 3, no. 3, pp. 140–152, Oct. 2021, doi: 10.30630/ijasce.3.3.56.

S. Karamehic, "Analysis of Covid-19 daily results and information about patients using SQL and PowerBi," Int. J. Data Sci., vol. 4, no. 1, pp. 1–9, May 2023, doi: 10.18517/ijods.4.1.1-9.2023.

M. R. Islam, M. M. Kabir, M. F. Mridha, S. Alfarhood, M. Safran, and D. Che, "Deep learning-based IoT system for remote monitoring and early detection of health issues in real-time," Sensors, vol. 23, no. 11, pp. 1–18, 2023, doi: 10.3390/s23115204.

A. N. Ejin, H. T. Yew, M. Mamat, F. Wong, A. Chekima, and S. K. Chung, “Internet of things based real-time coronavirus 2019 disease patient health monitoring system,†Int. J. Electr. Comput. Eng., vol. 12, no. 6, pp. 6806–6819, 2022, doi: 10.11591/ijece.v12i6.pp6806-6819.

C. Sen Chan, W. L. Pang, K. Y. Chan, and A. S. T. Ng, "Design of an IoT safety distance monitoring device for COVID-19," in Lecture Notes in Electrical Engineering, vol. 835, 2022, pp. 261–272. doi: 10.1007/978-981-16-8515-6_21.

C. E. You, W. L. Pang, and K. Y. Chan, "AI-based low-cost real-time face mask detection and health status monitoring system for COVID-19 prevention," WSEAS Trans. Inf. Sci. Appl., vol. 19, pp. 256–263, Nov. 2022, doi: 10.37394/23209.2022.19.26.

R. Sanchez-Iborra and A. F. Skarmeta, "TinyML-enabled frugal smart objects: challenges and opportunities," IEEE Circuits Syst. Mag., vol. 20, no. 3, pp. 4–18, 2020, doi: 10.1109/MCAS.2020.3005467.

C. Bales et al., "Can machine learning be used to recognize and diagnose coughs?," in 2020 International Conference on e-Health and Bioengineering (EHB), Oct. 2020, pp. 1–4. doi: 10.1109/EHB50910.2020.9280115.

M. You, W. Wang, Y. Li, J. Liu, X. Xu, and Z. Qiu, "Automatic cough detection from realistic audio recordings using C-BiLSTM with boundary regression," Biomed. Signal Process. Control, vol. 72, no. PA, p. 103304, 2022, doi: 10.1016/j.bspc.2021.103304.

V. Bansal, G. Pahwa, and N. Kannan, "Cough classification for COVID-19 based on audio mfcc features using Convolutional Neural Networks," in 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON), Oct. 2020, pp. 604–608. doi: 10.1109/GUCON48875.2020.9231094.

A. Dasari, A. Revanur, L. A. Jeni, and C. S. Tucker, "Video-based elevated skin temperature detection," IEEE Trans. Biomed. Eng., vol. 70, no. 8, pp. 2430–2444, Aug. 2023, doi: 10.1109/TBME.2023.3247910.

P. E. Aylwin, S. Racinais, S. Bermon, A. Lloyd, S. Hodder, and G. Havenith, "The use of infrared thermography for the dynamic measurement of skin temperature of moving athletes during competition; methodological issues," Physiol. Meas., vol. 42, no. 8, p. 084004, Aug. 2021, doi: 10.1088/1361-6579/ac1872.



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