Portable oxygen breathing apparatus integrated with biosensors: Enabling intelligent monitoring and optimal oxygen provision for biomechanical homeostasis
Abstract
A lightweight, compact, inconspicuous gadget that provides extra oxygen when traveling is called a portable oxygen breathing apparatus. Cells rely on oxygen to drive the oxidative phosphorylation process within mitochondria, where adenosine triphosphate (ATP) is synthesized. Adequate ATP is essential for maintaining the muscle contraction and cell motility. With these portable devices, patients can maintain their oxygen therapy while going about their daily lives, enhancing their quality of life (QoL) and encouraging more mobility. An incorrect assessment could result in a low oxygen supply during exercise. Hypoxia-induced changes can also trigger intracellular signaling pathways that may lead to cell damage and, in the long term, contribute to the progression of various pathologies. Promising resolution to the difficulties can be discovered by incorporating machine learning (ML) algorithms and sophisticated monitoring systems into portable oxygen delivery devices. In this study, we propose a novel intelligent portable oxygen breathing apparatus integrated with biosensors (IPOBAB) that has revolutionized the treatment of long-term respiratory disorders, particularly severe hypoxemia and chronic obstructive pulmonary disease (COPD). IPOBAB system deployed with the Dynamic Gradient Boosting Machine (DGBM) classifier to classify the physical activities into low, moderate, and high exertion levels to ensure oxygen delivery is repeatedly adjusted based on the patient’s current requirements. Inertial Measurement Unit (IMU) sensor data, blood oxygen saturation (SpO2), and cardiovascular rate are just a few of the vital physiological features that biological sensors continuously monitor. This data lets doctors perform real-time assessments of a patient’s health status. To eliminate noise, the information was processed using a median filter. The Fast Fourier Transform (FFT), which displays dominating frequency components, divides the electrical signal into individual frequencies to extract features. The results demonstrated that the IPOBAB model exhibits a high weighted accuracy of 98.4% in mechanically adjusting oxygen flow according to medical criteria compared to existing algorithms. This indicates that the system is effective in optimizing oxygen delivery, which is essential for maintaining the proper cell and molecular biomechanical functions in patients with long-term respiratory disorders. In conclusion, the IPOBAB represents a significant advancement in portable oxygen therapy as it combines adaptive oxygen delivery and comprehensive monitoring, thereby optimizing the care for patients with long-term respiratory conditions and safeguarding the integrity and functionality of cells and tissues at the molecular level.
References
1. Sanchez-Morillo, D., Lara-Doña, A., Priego-Torres, B., Morales-Gonzalez, M., Montoro-Ballesteros, F. and Leon-Jimenez, A., 2020. Portable Oxygen Therapy: Is the 6-Minute Walking Test Overestimating the Actual Oxygen Needs? Journal of Clinical Medicine, 9(12), p.4007. DOI: https://doi.org/10.3390/jcm9124007
2. Lellouche, F. and L’Her, E., 2021. Conventional Oxygen Therapy: Technical and Physiological Issues. In High Flow Nasal Cannula: Physiological Effects and Clinical Applications (pp. 1-36). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-42454-1_1
3. Pillai, S., Upadhyay, A., Sayson, D., Nguyen, B.H. and Tran, S.D., 2021. Advances in medical wearable biosensors: Design, fabrication and materials strategies in healthcare monitoring. Molecules, 27(1), p.165. https://doi.org/10.3390/molecules27010165
4. Strapazzon, G., Gatterer, H., Falla, M., Dal Cappello, T., Malacrida, S., Turner, R., Schenk, K., Paal, P., Falk, M., Schweizer, J. and Brugger, H., 2021. Hypoxia and hypercapnia effects on cerebral oxygen saturation in avalanche burial: A pilot human experimental study. Resuscitation, 158, pp.175-182. DOI: https://doi.org/10.1016/j.resuscitation.2020.11.023
5. Verma, D., Singh, K.R., Yadav, A.K., Nayak, V., Singh, J., Solanki, P.R. and Singh, R.P., 2022. Internet of Things (IoT) in nano-integrated wearable biosensor devices for healthcare applications. Biosensors and Bioelectronics: X, 11, p.100153. DOI: https://doi.org/10.1016/j.biosx.2022.100153
6. Wang, L., Lou, Z., Jiang, K. and Shen, G., 2019. Bio‐multifunctional smart wearable sensors for medical devices. Advanced Intelligent Systems, 1(5), p.1900040. DOI: https://doi.org/10.1002/aisy.201900040
7. Swapna, M., Viswanadhula, U.M., Aluvalu, R., Vardharajan, V. and Kotecha, K., 2022. Bio-signals in medical applications and challenges using artificial intelligence. Journal of Sensor and Actuator Networks, 11(1), p.17. DOI: https://doi.org/10.3390/jsan11010017
8. Vakhter, V., Kahraman, B., Bu, G., Foroozan, F. and Guler, U., 2023. A prototype wearable device for noninvasive monitoring of transcutaneous oxygen. IEEE Transactions on Biomedical Circuits and Systems, 17(2), pp.323-335. DOI: https://doi.org/10.1109/TBCAS.2023.3251321
9. Kanna, R.K., Banappagoudar, S.B., Menezes, F.R. and Sona, P.S., 2022, December. Patient Monitoring System for COVID Care Using Biosensor Application. In International Conference on Communication, Networks and Computing (pp. 310-321). Cham: Springer Nature Switzerland. DOI: https://doi.org/10.1007/978-3-031-43140-1_27
10. Kumar, S.A., Gopinath, B., Kavinraj, A. and Sasikala, S., 2021, October. Towards improving patient health monitoring systems using machine learning and the Internet of Things. In 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/ICAECA52838.2021.9675673
11. Mia, M.M.H., Mahfuz, N., Habib, M.R. and Hossain, R., 2021, December. An Internet of Things application on continuous remote patient monitoring and diagnosis. In 2021 4th international conference on bio-engineering for smart technologies (BioSMART) (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/BioSMART54244.2021.9677715
12. Lavric, A., Petrariu, A.I., Mutescu, P.M., Coca, E. and Popa, V., 2022. Internet of Things concept in the context of the COVID-19 pandemic: a multi-sensor application design. Sensors, 22(2), p.503. DOI: https://doi.org/10.3390/s22020503
13. Stella, K., Menaka, M., Jeevitha, R., Jenila, S.J., Devi, A. and Vethapackiam, K., 2023, June. Patient Pulse Rate and Oxygen Level Monitoring System Using IoT. In International Conference on IoT Based Control Networks and Intelligent Systems (pp. 343-355). Singapore: Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-99-6586-1_23
14. Bhattacharya, S. and Pandey, M., 2022. System for Remote Health Monitoring using Biosensors with IoT Cloud Convergence. NeuroQuantology, 20(16), p.3877. DOI: https://doi.org/10.48047/NQ.2022.20.16.NQ880392
15. Contardi, U.A., Morikawa, M., Brunelli, B. and Thomaz, D.V., 2021. Max30102 photometric biosensor coupled to esp32-webserver capabilities for continuous point-of-care oxygen saturation and heart rate monitoring. Engineering Proceedings, 16(1), p.9. DOI: https://doi.org/10.3390/IECB2022-11114
16. Wong, D.L.T., Yu, J., Li, Y., Deepu, C.J., Ngo, D.H., Zhou, C., Singh, S.R., Koh, A., Hong, R., Veeravalli, B. and Motani, M., 2019. An integrated wearable wireless vital signs biosensor for continuous inpatient monitoring. IEEE Sensors Journal, 20(1), pp.448-462. DOI: https://doi.org/10.1109/JSEN.2019.2942099
17. Jin, X., Liu, C., Xu, T., Su, L., and Zhang, X., 2020. Artificial intelligence biosensors: Challenges and prospects. Biosensors and Bioelectronics, 165, p.112412. DOI: https://doi.org/10.1016/j.bios.2020.112412
18. Nwibor, C., Haxha, S., Ali, M.M., Sakel, M., Haxha, A.R., Saunders, K. and Nabakooza, S., 2023. Remote health monitoring system for the estimation of blood pressure, heart rate, and blood oxygen saturation level. IEEE Sensors Journal, 23(5), pp.5401-5411. DOI: https://doi.org/10.1109/JSEN.2023.3235977
19. Madevska Bogdanova, A., Koteska, B., Vićentić, T., D. Ilić, S., Tomić, M. and Spasenović, M., 2024. Blood Oxygen Saturation Estimation with Laser‐Induced Graphene Respiration Sensor. Journal of Sensors, 2024(1), p.4696031. DOI: https://doi.org/10.1155/2024/4696031
20. Mondal, A., Dutta, D., Chanda, N., Mandal, N. and Mandal, S., 2024. RESPIPulse: Machine learning-assisted sensory device for pulsed mode delivery of oxygen bolus using surface electromyography (sEMG) signals. Sensors and Actuators A: Physical, 369, p.115121. DOI: https://doi.org/10.1016/j.sna.2024.115121
21. Nathan, V., Vatanparvar, K. and Kuang, J., 2020. Assessing Severity of Pulmonary Obstruction from Respiration Phase-Based Wheeze Sensing Using Mobile Sensors. DOI: http://dx.doi.org/10.1145/3313831.3376444
Copyright (c) 2024 Honghao Zhang
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
