# A Novel Magnetic Respiratory Sensor for Human Healthcare
## 3 Results and Discussion
### Real-Time Monitoring of Human Respiratory Motion in Wake-Up and Sleep Modes  
Nowadays, many people experience insomnia, sleep difficulties, dream sleep, or chronic trouble falling asleep. According to the American Sleep Association (ASA), insomnia is the most common sleep disorder [30]. Currently, about 30 percent of American adults are faced with insomnia [30]. Insomnia can arise for various reasons such as irritability, depression or anxiety, stress from multiple work tasks, medical conditions, or mental health disorders, and it can reduce our health, work performance, and quality of life [26; 30; 31]. Research has shown that breathing training techniques can help us to relax, sleep, and breathe more naturally and effectively [26; 31]. Before using such techniques, it is essential to understand what affects our breathing before, during, and after our sleep [31]. In this context, there is an urgent need for the development of a highly sensitive, contactless respiratory monitoring device that can precisely monitor human respiratory motion in real time in these states. Owing to its ultrahigh sensitivity, the MLCR sensor is expected to fulfil this requirement. In this study, we examined the capacity of the MLCR sensor in monitoring a patient's respiratory motion during sleep, as well as tracking a transition of their respiratory motion from wake-up mode to sleep mode. First, we showed how respiratory motion of a patient can be tracked in real time during sleep by the MLCR sensor. In this case, the breathing pattern of Subject 2 (36 YO) was tracked while the subject was sleeping. As one can see clearly in Figure 6, the sleeping patterns were clearly recorded. It is worth noting that abnormal breathing or a shortness of breath occurred around \(t\) = 80 s. This may suggest that this subject experiences some respiratory problems or associated diseases. Figure 5: Breathing patterns of subjects aged (**a**) 36 and (**b**) 42 years old at three different states; State 1: breath holding, State 2: breath releasing, and State 3: normal breathing. Next, we tracked how the respiratory motion of a patient (Subjects 1 and 3) changed during their wake-up or sleep period, as well as from the transition from wake to sleep mode. The breathing patterns of Subject 1 (30 YO) were continuously tracked from wake to sleep mode, the result of which is shown in Figure 7. This figure shows a clear breathing transition of the subject from the wake-up to the sleep mode. It is also worth noting that in sleep mode, the subject breathed more deeply (higher amplitude) and slowly (11 times per minute), as compared to the wake mode (12 times per minute). In a similar fashion, the breathing patterns of Subject 3 (42 YO) were continuously tracked from wake to sleep mode (see Figure 8). The sensor precisely tracked how his breath transitioned from wake to sleep mode. In sleep mode, the subject breathed more deeply (higher amplitude) and slowly (14 times per minute), as compared to the wake mode (20 times per minute). The results obtained from both Subject 1 and Subject 2 consistently pinpoint the fact that, in a normal sleep mode, one tends to relax and sleep better than during the wake mode. Relative to Subject 1, there is more of a difference in the respiratory rate between the wake and sleep mode found for Subject 3. This suggests that older people tend to relax more during sleep [31]. Figure 6: Breathing pattern of Subject 2 (36 YO) during 180 s of sleep and periodic sleeping patterns were observed. Figure 7: (**a**) Breathing patterns of Subject 1 in both wake and sleep modes. An obvious transition from the wake to sleep mode was observed. (**b**,**c**) show the wake and sleep breathing patterns over 1 min. In addition, the ultrahigh sensitivity of the MLCR sensor enables the detection of abnormal breathing around \(t=40\) s in both the wake-up and sleep breathing patterns for Subject 3.