IoT-based monitoring system for diagnosing adverse drug reactions and enhancing drug compliance in tb patients

• Autorzy: Sharma Anshu , Sharma Anurag , Malhotra Rahul

Identyfikatory

DOI

10.24423/cames.451

• Języki publikacji: EN

Abstrakty

EN

Health plays a prime role for day-to-day working. IoT provides physicians and patients with advanced medical resources, solutions, services, advantages etc. The goal behind the internet of things (IoT) is to have IoT devices that self-report in real-time. This project aims to develop a system that provides live-body temperature, cough detection, pulse rate and other health criteria such as weight loss, chest pain detection, blood sugar level, HB-WBC-RBC (hemoglobin, white blood cell, red blood cell) level, etc. NodeMcu, an open-source firmware, is used for wireless data transmission on an IoT platform. The data is stored on a web server so it can be accessed by both the physician and patient to provide the information about the patient’s condition and help the physician to diagnose TB for the patient. The data of 50 patients have been collected for analysis and reviewed in detail in the result and analysis section.

Słowa kluczowe

EN

NodeMcu; MQ telemetry transport; tuberculosis; IoT; health care

PL

NodeMcu; MQ Telemetry Transport; gruźlica; IoT; opieka zdrowotna

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Computer Assisted Methods in Engineering and Science
• Rocznik: 2023
• Tom: Vol. 30, no. 2
• Strony: 187--201
• Opis fizyczny: Bibliogr. 26 poz., rys., tab., wykr.

Twórcy

autor

Sharma Anshu

• School of Computer Science & Engineering, Lovely Professional University, Phagwawa, India

autor

Sharma Anurag

• Faculty of Engineering Design and Automation, GNA University, Phagwara, India

autor

Malhotra Rahul

• Department of Electronics & Communication Engineering, CTIEMT, Jalandhar, India

Bibliografia

• 1. A.R. Biswas, R. Giaffreda, IoT and cloud convergence: Opportunities and challenges, [in:] 2014 IEEE World Forum on Internet of Things (WF-IoT) , IEEE, pp. 375–376, 2014, doi: 10.1109/wf-iot.2014.6803194.
• 2. Z. Yang, Q. Zhou, L. Lei, K. Zheng, W. Xiang, An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare, Journal of Medical Systems , 40 : 286, 2016, doi: 10.1007/s10916-016-0644-9.
• 3. S. Haykin, Neural networks , Pearson Education Asia, New Delhi, 2002.
• 4. F. Behmann, K. Wu, C-IoT Cloud-Based Services and C-IoT User Device Diversity, [in:] Collaborative Internet of Things (C-IOT) , John Wiley & Sons, pp. 225–238, 2015, doi: 10.1002/9781118913734.ch5.
• 5. T. Alam, A reliable communication framework and its use in internet of things (IOT), International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), 3 (5): 450–456, 2018, doi: 10.32628/CSEIT1835111.
• 6. J. Li, Q. Ma, A.H.S. Chan, S.S. Man, Health monitoring through wearable technologies for older adults: Smart wearables acceptance model, Applied Ergonomics , 75 : 162–169, 2019, doi: 10.1016/j.apergo.2018.10.006.
• 7. F. Patlar Akbulut, A. Akan, A smart wearable system for short-term cardiovascular risk assessment with emotional dynamics, Measurement , 128 : 237–246, 2018, doi: 10.1016/j.measurement.2018.06.050.
• 8. Akhila L. et al. , IoT-enabled geriatric health monitoring system, [in:] 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC) , IEEE, pp. 803–810, 2021, doi: 10.1109/icesc51422.2021.9532781.
• 9. A. Kumar, K. Abhishek, C. Chakraborty, N. Kryvinska, Deep learning and Internet of Things Based lung ailment recognition through coughing spectrograms, IEEE Access , 9 : 95938–95948, 2021, doi: 10.1109/access.2021.3094132.
• 10. O. Hataji et al. , Smart watchbased coaching with tiotropium and olodaterol ameliorates physical activity in patients with chronic obstructive pulmonary disease, Experimental and Therapeutic Medicine , 14 (5): 4061–4064, 2017, doi: 10.3892/etm.2017.5088.
• 11. N. Petrović, D. Kocić, IoT for COVID-19 indoor spread prevention: cough detection, air quality control and contact tracing, [in:] 2021 IEEE 32nd International Conference on Microelectronics (MIEL) , IEEE, pp. 297–300, 2021, doi: 10.1109/miel52794.2021.9569099.
• 12. K. Grym et al ., Feasibility of smart wristbands for continuous monitoring during pregnancy and one month after birth, BMC Pregnancy and Childbirth , 19 : 34, 2019, doi: 10.1186/s12884-019-2187-9.
• 13. H.K. Tripathy, S. Mishra, S. Suman, A. Nayyar, K.S. Sahoo, Smart COVID-shield: An IoT driven reliable and automated prototype model for COVID-19 symptoms tracking, Computing , 104 (6): 1233–1254, 2022, doi: 10.1007/s00607-021-01039-0.
• 14. G. Roland, S. Kumaraperumal, S. Kumar, A. das Gupta, S. Afzal, M. Suryakumar, PCA (principal component analysis) approach towards identifying the factors determining the medication behavior of Indian patients: An empirical study, Tobacco Regulatory Science , 7 (6–1): 7391–7401, 2021, doi: 10.18001/TRS.7.6.1.61.
• 15. F. Ajaz, M. Naseem, S. Sharma, M. Shabaz, G. Dhiman, COVID-19: Challenges and its Technological Solutions using IoT, Current Medical Imaging Formerly Current Medical Imaging Reviews , 18 (2): 113–123, 2022, doi: 10.2174/1573405617666210215143503.
• 16. M. Ghozali, S. Satibi, Z. Ikawati, L. Lazuardi, Asthma self-management app for Indonesian asthmatics: A patient-centered design, Computer Methods and Programs in Biomedicine , 211 : p. 106392, 2021, doi: 10.1016/j.cmpb.2021.106392.
• 17. A. Tiwari, V. Dhiman, M.A.M. Iesa, H. Alsarhan, A. Mehbodniya, M. Shabaz, Patient behavioral analysis with smart healthcare and IoT, Behavioural Neurology , 2021 : 1–9, 2021, doi: 10.1155/2021/4028761.
• 18. R.K. Kodali, V.S.K. Gorantla, Weather tracking system using MQTT and SQLite, [in:] 2017 3rd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) , IEEE, pp. 205–208, 2017, doi: 10.1109/ICATCCT. 2017.8389134.
• 19. P. Macheso, T.D. Manda, S. Chisale, N. Dzupire, J. Mlatho, D. Mukanyiligira, Design of ESP8266 smart home using MQTT and Node-RED, [in:] 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS) , IEEE, pp. 502–505, 2021, doi: 10.1109/ICAIS50930.2021.9396027.
• 20. A. Kakti, S. Kumar, N.K. John, V.V. Ratna, S. Afzal, A. das Gupta, Impact of patients approach towards healthcare costs on their perception towards health: An empirical study, Tobacco Regulatory Science , 7 (6–1): 7380–7390, 2021, doi: 10.18001/TRS.7.6.1.60.
• 21. P. Leamy, T. Burke, D. Dorran, Analysis of cough sound observed by different cough etiquettes, [in:] 2021 32nd Irish Signals and Systems Conference (ISSC) , IEEE, pp. 1–6, 2021, doi: 10.1109/issc52156.2021.9467880.
• 22. C.C. Bojarczuk, H.S. Lopes, A.A. Freitas, Genetic programming for knowledge discovery in chest-pain diagnosis, IEEE Engineering in Medicine and Biology Magazine , 19 (4): 38–44, 2000, doi: 10.1109/51.853480.
• 23. P. Ratta, A. Kaur, S. Sharma, M. Shabaz, G. Dhiman, Application of blockchain and internet of things in healthcare and medical sector: Applications, challenges, and future perspectives, Journal of Food Quality , 2021 : 1–20, 2021, doi: 10.1155/2021/7608296.
• 24. T. Chand, B. Sharma, HRCCTP: A hybrid reliable and congestion control transport protocol for wireless sensor networks, [in:] 2015 IEEE Sensors , pp. 1–4, 2015, doi: 10.1109/ic-sens.2015.7370446.
• 25. T. Zheng, W. Li, Y. Liu, B.W.-K. Ling, A noninvasive blood glucose measurement system by arduino and near-infrared, [in:] 2016 IEEE International Conference on Consumer Electronics-China (ICCE-China) , IEEE, pp. 1–3, 2016, doi: 10.1109/icce-china.2016.7849752.
• 26. A. Kishor, C. Chakraborty, W. Jeberson, Reinforcement learning for medical information processing over heterogeneous networks, Multimedia Tools and Applications , 80 : 2398324004, 2021, doi: 10.1007/s11042-021-10840-0.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).