## 1 Introduction  
Clinically, respiratory rate (RR), defined as the times of breath per minute (BPM), is a vital sign whose abnormality could indicate various pathological conditions in cardiorespiratory system [1, 2]. Compared with other vital signs such as heart rate and blood pressure, RR is more accurate in screening unstable patients [1, 3, 4]. Therefore, RR monitoring has been used in detecting sleep apnea, cardiac arrest, and sudden infant death syndrome [3, 5]. Especially, compared with discrete RR measurement, continuous RR monitoring could detect RR abnormalities with more than 10 times of accuracy [2]. However, despite the clinical significance of RR, continuous, convenient, and accurate RR monitoring is still difficult. It was reported in [2] that the RR was the least documented vital sign even when the patients suffered from respiratory issues. Manual RR counting is widely used in clinical examination [3] but is not accurate [2]. Other traditional techniques of clinical RR monitoring include spirometry, pneumotachograph, and capnography [1; 4; 6]. These techniques could accurately measure RR, but are cumbersome, difficult to manipulate, or mask-based [4]. To achieve continuous and convenient RR monitoring, some new techniques have been developed [7; 8; 9; 10]. Some researchers extract RR from other physiological signals such as photoplethysmogram and electrocardiogram [11]. Other studies derived respiration signals using non-contact techniques such as C-band sensing [12] and dual smartphone cameras [4]. Some researchers tried distant (less than 10 m) RR detecting based on Wi-Fi, radar, or thermal imaging [11]. Compared with these techniques, wearable and stretchable sensors could independently measure RR without limiting the daily activities of the users. Additionally, the stretchable and wearable sensors could achieve highly accurate RR measurement at a low cost [13]. For a wearable RR sensor, the high accuracy at different body postures could enable its application in various clinical and daily situations. Several studies [3; 12; 14] have investigated the accuracy of RR monitoring devices at different body postures (sitting, standing and supine and side). For instance, [PERSON] et al. [3] embedded arrays of load sensors in an e-textile bed sheet to measure RR from the full body pressure distribution on the sheet. The sensor was tested on 14 human subjects in prone, side and supine postures during sleep. The results showed that RR measurement was more accurate in prone and supine postures compared with side posture. The authors deduced that this difference was due to the amplitude of respiratory movement which is more prominent in thoracic and abdominal areas than on both sides. [PERSON] et al. [14] developed a fiber bragg grating (FBG) sensor mat with three FBG arrays under the pillow, below upper chest, and below lower chest. The sensor was tested on 12 human subjects while lying in prone and side postures. For each of the three sensors, the mean error of RR measurement was lower than one BPM with standard deviation (STD) within \(\pm\)0.25 BPM, compared with the reference RR measured by a polysomnography belt sensor. [PERSON] et al. [12] developed a wireless system to measure RR based on C-band sensing. In prone, sitting, and standing postures, the error of RR detection was within 7%, 6.2% and 5.5% compared with a reference RR sensor. The author suggested that standing posture have the largest influence on the quality of C-band RR detection. [PERSON] et al. [4] used dual cameras on a smartphone to measured RR by reflectance imaging on the chest and the abdomen, with average median error of 1.43% and 1.62%. The majority of existing studies on the effect of body posture on RR detection accuracy were based on non-stretchable [15; 16; 17] or non-wearable RR sensors [3; 12; 14], with a lack of related study based on stretchable RR sensors. Compared with traditional RR sensors, stretchable RR sensors are in direct contact with body surface, and are therefore, more likely to be influenced by body posture.