# A Novel Magnetic Respiratory Sensor for Human Healthcare
## 4 Conclusions and Future Outlooks  
We have developed an innovative, contactless magnetic respiratory monitoring platform based on ultrasensitive magneto-LC resonance (MLCR) technology and a permanent magnet. This technology has been successfully applied to test human respiratory motion in real time and in different states. We demonstrated that the MLCR sensor can track a person's breathing pattern very precisely in real time over the course of multiple states (normal breathing, holding breath, and sleeping). Owing to its ultrahigh sensitivity, the sensor yielded a distinct breathing pattern for each person tested and revealed abnormal breathing, which is superior to existing respiratory monitoring devices. We have shown that when getting older, one usually manifests weaker breathing, which is marked by an increased breathing rate. They tend to hold their breath for a shorter period of time and often experience more respiratory issues than younger people. People become more relaxed and thus breathe more regularly and slowly while sleeping as compared to the wake-up mode. Our highly sensitive magnetic respiratory monitor provides not only valuable information on a patient's current health status, but also a novel breathing control tool for improving our health and physical performance.  
Through these studies, we can also identify several exciting and important applications of our novel magnetic respiratory monitoring technology for advanced human healthcare systems. In particular, research on the practical implementation of this technique for the fast screening and real-time tracking of COVID-19, or associated viruses, is very exciting and worth pursuing. Monitoring COVID-19 patients remotely with the MLCR detector and collecting data from numerous physiological signals, with the help of machine learning, would improve clinical decision making for providers [31, 32, 33, 34]. The Fast Fourier Transform algorithm, which converts a signal from the time domain to the frequency domain and vice versa, can be used to extract spectral features from breathing patterns. This can also be used by machine learning and other algorithms for signal processing and to provide comprehensive information on a patient's health status and physical performance.  
Figure 8: (**a**) Breathing patterns of Subject 3 in both wake and sleep modes. An obvious transition from the wake to the sleep mode was observed. (**b,c**) show the wake-up and sleep breathing patterns per 1 min.  
**Author Contributions:** Conceptualization, M.-H.P., K.Y.H., B.M., V.D.L. and A.-T.L.; methodology, K.Y.H. and V.O.J.; software, V.O.J. and T.E.; validation, K.Y.H., B.M. and M.-H.P.; formal analysis, K.Y.H. and M.-H.P.; investigation, K.Y.H.; resources, M.-H.P.; writing--original draft preparation, K.Y.H. and M.-H.P.; writing--review and editing, K.Y.H., V.O.J., B.M., V.D.L., A.-T.L., T.E. and M.-H.P.; supervision, M.-H.P.; funding acquisition, M.-H.P. and V.D.L. All authors have read and agreed to the published version of the manuscript.  
Research at USF was supported by the Covid19 Research Project under Grant number 125300. The work was also supported by Vietnam Ministry of Science and Technology through the national-level project DTDLCN.17/19.  
Ethical review and approval were waived for this study, because the research presents no more than minimal risk of harm to subjects and involves no procedures for which written consent is normally required outside the research context.  
Not required.  
The authors declare no conflict of interest.