# Wearable Sensors for Vital Signs Measurement: A Survey
## 2 Classification of Wearable Medical Sensors
### Classification and Application of Wearable Medical Sensors
#### 2.2.5 Wearable Medical Sensors for Monitoring Sleep  
Monitoring sleep aims to judge the quality of sleep and facilitate the detection of sleep diseases, such as common sleep apnea syndrome. In people's work and rest, sleep interruption may affect neural function, and may even be a symptom of physical and mental disorders. The miniaturization of wearable medical sensors and the progress of computing power have become favorable technologies for monitoring human physiological conditions in real-world scenes. Many scholars in related fields have studied their application in monitoring sleep (Table 4). [PERSON] et al. (2019) proposed wearable in-ear electroencephalography (ear EEG) for night sleep monitoring as a 24/7 continuous and unobtrusive technology for community sleep-quality assessment. The results showed that the in-ear sensor was feasible for monitoring night sleep outside the sleep laboratory, but also reduced the technical difficulties related to PSG, and this technology was the key to affordable medical treatment and future electronic health [43]. [PERSON] et al. (2020) proposed a system based on a bed vibration sensor to monitor important parameters during sleep, including heart rate (HR) and respiratory rate (RR), body movement, and sleep posture. Finally, the short-term and long-term experiments on different participants and different beds indicated that the proposed system achieved satisfactory accuracy [44]. [PERSON] et al. (2020) introduced a wearable multi-biological signal wireless interface for sleep analysis. It realized comfortable sleep monitoring through a direct sleep-stage classification function. Finally, they verified the system and found that its low-power headband analysis device was used for wearable sleep monitoring, in which the direct sleep-stage classification was performed based on the decision tree algorithm, with the correlation of the four sleep stages being 74% [45]. [PERSON] et al. (2021) introduced an unsynchronized sensor data analytics (USDA) model to effectively process wearable device data without considering time factors. They found that a responsive healthcare solution using asynchronous WS-data helped to achieve better efficiency and reduce complexity in evaluating healthcare system performance [46].  
The review of the above scholars' research reveals that wearable devices are mostly used to monitor sleep parameters in different ways, such as brain map, HR, RR, etc., and to judge sleep quality by understanding the relevant parameters. Thus, wearable devices monitor physiological parameters such as respiration; the overall framework is shown in Figure 3. However, in the process of monitoring sleep, the security of data transmission and information was not particularly addressed.  
Figure 3: Framework of respiratory monitoring system of wearable medical sensors.  
As shown in Figure 3, the respiratory monitoring system is mainly composed of N wearable sensor detection devices and data computing center. N wearable sensors are responsible for measuring the physiological parameters related to each sleep, and regularly sending the collected real-time parameters to the data computing center by using the wireless communication module. The data computing center is mainly responsible for processing the data received by the wireless module and using the LCD to display the processed physiological parameter information, thereby obtaining the sleep quality.