Fabrication and Characterization of Yarn-Based Temperature Sensor for Respiratory Monitoring

BAI Yunfeng(白云峰), XIE Erxiang(谢二想), LI Qiao(李 乔), WANG Xi(王 玺), DING Hao(丁 浩), ZHU Shigen(朱世根)*

1 College of Mechanical Engineering, Donghua University, Shanghai 201620, China2 Engineering Research Center of Advanced Textile Machinery, Ministry of Education, Donghua University, Shanghai 201620, China3 College of Textiles, Donghua University, Shanghai 201620, China4 College of Information Science and Technology, Donghua University, Shanghai 201620, China

Abstract: The development of wearable technologies promotes the research of flexible sensors. It is hoped that a flexible sensor can collect different physiological data, such as temperature and respiratory rate(RR). The temperature of the exhaled gas is generally higher than that in the air, and the periodic change of temperature is related to the respiratory rate. In this work, we use platinum fiber and spandex fiber to prepare yarn-based temperature sensor with high tensile performance through hollow spindle wrapping spinning technology. After the measurement, the sensitivity of the sensor can reach at least 3.18×10-3℃-1. We use the sensor and ordinary fabric mask to prepare a sensor mask that can monitor human respiratory signals to explore the performance of the sensor in RR measurement. The experimental results show that when measuring human RR, the yarn-based temperature sensor can accurately distinguish different respiratory states such as normal breathing, deep breathing, and rapid breathing while speaking. It is suggested that yarn-based temperature sensors can be used in medical fields such as real-time respiratory detection and temperature measurement.

Key words: flexible sensor; yarn-based temperature sensor；sensing mask； respiratory measurement

Introduction

The four vital signs of respiratory rate(RR), temperature, pulse rate and blood pressure are the most important parameters in clinical medicine[1-2]. RR is one of the most important vital signs of human physiological signals. Doctors can judge the severity of patients with respiratory diseases based on the respiratory rate[3-4]. For patients with high RR, doctors need to consider whether to use assisted oxygen inhalation or ventilator treatment. At present, under the epidemic situation of COVID-19 and influenza, how to monitor the condition of respiratory patients in real time and judge the development of the condition has become more important[5]. Especially for areas with scarce medical resources, decentralized care can be realized, which can reduce the risk of infection of medical staff, and increase the protection of medical staff, which is of great significance[6].

At present, there are mainly piezoresistive and capacitive sensors used for portable respiratory monitoring[7-8]. However, these sensors need to be prepared into tight straps or underwear for accurate measurement, which seriously affects the wearing comfort of users. Meanwhile, there are some humidity sensors for respiration monitoring[9-10]. This method is to place the sensor in the mouth or nose to monitor the respiration rate using the humidity of the breathing gas, but the general measurement accuracy is difficult to guarantee.

The development of flexible sensors advances smart textiles. Functional yarn-based temperature sensor is an important part of smart textiles with a wide range of applications[11]. The yarn-based temperature sensor is formed by wrapping temperature-sensitive platinum fiber around spandex fiber, which has good stretchability, flexibility, as well as temperature-sensitive performance, and has higher sensitivity than multi-wall carbon nanotubes (MWCNTs) temperature sensors reported by Liuetal.[12]During human respiration, the temperature of the exhaled gas is higher than that of inhaled gas. The temperature difference between inhaled gas and exhaled gas can reach about 15℃[13]. Therefore, we can estimate the RR by using the high sensitivity of the temperature sensor to the temperature difference between inhalation and exhalation of the human body. The sensor is combined with an ordinary mask to form an intelligent mask, which is easy to collect human respiratory physiological signals and plays an important role in promoting wearable devices in medical treatment and other fields[14-15].

1 Experiment

1.1 Experimental materials

Platinum has a good thermal effect. When the ambient temperature of platinum metal increases, its resistance will increase rapidly, so it can be used to make some temperature sensors. However, platinum fibers are prone to crack under external force, making it difficult to be integrated into textile products. In order to endow stretchability to platinum fibers, we wrapped platinum fibers (Shanghai Senjak Co., Ltd., Shanghai, China) with a diameter of 20 μm on stretchable spandex (INVISTA Fiber Co., Ltd., Shanghai, China) through a wrapping machine as a temperature sensor component. The Teflon Polytetrafluoroethylene (PTFE) ultra-fine wire with a diameter of not exceeding 1.8 mm (Shanghai Xiannai Wire and Cable Co., Ltd., Shanghai, China) is chosen. The wire and the sensor are connected by using soft silicone (Dongguan Lelai Adhesive Products Co., Ltd., Dongguan, China). The sensor is bonded to the medical disposable practical mask (Hunan Xiaobei Medical Products Co., Ltd., Huangshi, China) through double-sided tape, which is convenient to dispose or reuse later.

1.2 Fabrication process

1.2.1Preparationprocessofyarn-basedtemperaturesensor

The yarn-based temperature sensor adopts spiral structure, as shown in Fig. 1(a). The coverage or twist refers to the number of turns of the outer yarn wrapped around the core yarn per 1 m. The coverage is selected as 4 000 turn/m. The sensor uses spandex as the core and platinum fiber as the outer yarn by wrapping the hollow spindle spinning machine. The preparation process consists of three parts: traction, wrapping, and winding[16]. The spandex passes through the hollow spindle from bottom to top and is fixed in the motor 4, and its function is to wind the wrapped sensor. Motor 1 and motor 2 are respectively used to pull the spandex and drive the platinum fiber on the hollow spindle to rotate around the spandex to complete the winding. The schematic diagram of the wrapping machine is shown in Fig. 1(b).

1—motor 1; 2—motor 2; 3—hollow spindle; 4—motor 4; 5—yarn-based temperature sensor; 6—spandex; Pt—platinum wireFig. 1 Fabrication of yarn-based temperature sensor: (a) the structure of yarn-based sensor; (b) schematic diagram of wrapping machine

The core yarn is made of elastic spandex, which needs to be pre-stretched during wrapping. The coverage can be estimated by

(1)

whereTcis the coverage (turn/mm);n1is the speed of motor 1 (r/min);n2is the speed of motor 2 (r/min);Cis the circumference of the connecting shaft of motors 1 and 2 (44 mm);Pis the pre-stretching ratio.n2andPare selected according to the reference range obtained from our previous research with appropriate adjustments[11]. Taking the static friction between spandex and rotating shaft into consideration, the specific process parameters are set as shown in Table 1.

Table 1 Parameter setting of wrapping machine

The fabricated temperature sensor is shown in Fig. 2. The surface morphology of the sensor was observed under an electron microscope (HDM I200C-B, Shenzhen Zongyuan Weiye Technology Co., Ltd., Shenzhen, China). It was found that the platinum fiber did not break and cross, and was evenly wrapped on the spandex wire with a specific pitch and helix angle. The slight bulge structure of spandex in the middle of adjacent platinum fiber spacing indicates that the sensor structure is compact.

Fig. 2 Surface topography of yarn-based temperature sensor

1.2.2Preparationprocessofsensingmask

The widely used disposable medical masks on the market are used as the basis for the preparation of sensor masks. The preparation method is as follows. (1) Cut a line segment with a length of 2 cm from the prepared yarn-based sensor. (2) Use silica gel to connect the sensor to the wire shown in Fig. 3(a). Due to the slow solidification of silica gel, it needs to be kept at room temperature for 24 h to completely solidify. (3) Bond the sensor with the connected wires to the first crease of the mask through double-sided tape or silicone shown in Fig. 3(c). The production process is shown in Fig. 3.

Fig. 3 Preparation process of the sensor mask: (a) connection between sensor and wire; (b) fixing the sensor into the mask; (c) overall view of the sensor mask

2 Sensitivity Test of Yarn-Based Temperature Sensor

2.1 Sensitivity measurement

The constant temperature heating platform P20 (Shenzhen Antaixing Electronic Technology Co., Ltd., Shenzhen, China) is used as an instrument to control the ambient temperature of the yarn-based temperature sensor. The temperature of the heating platform is increased at intervals of 0.1 ℃ in the range of 25.0-25.8 ℃, and the temperature of the platform is measured three times with a non-contact resistance thermometer (Guangdong Xujun Medical Instrument Co., Ltd., Guangzhou, China) with a resolution of 0.1 ℃. The resistance was recorded by a self-made measurement system composed of bridge circuit, operational amplifier circuit and analog-to-digital (A/D) converter, and recorded 5 times per second.

2.2 Sensitivity measurement results

The temperature sensor sensitivityρindicates temperature-sensitive property and it was calculated by[17]

ρ=(R-R0)/(R0×ΔT),

(2)

and the relative resistance changeδwas calculated by

δ=(R-R0)/R0×100%,

(3)

whereRis the measured resistance,R0is the resistance at 0 ℃, and ΔTis the temperature change. The results obtained by averaging the recorded resistance data are shown in Fig. 4. When the temperatureTvaries 0.1 ℃, there will be an obvious resistance change of about 0.02 Ω and the linearity can reach 0.998. The results show that the sensor not only has a good linear relationship between resistance and temperature but also the sensitivity can reach 3.18×10-3℃-1.

Fig. 4 Test results of sensitivity

3 Detection of RR

3.1 Detection process

The RR was detected by the yarn-based temperature sensor. It was carried out in a constant temperature and humidity chamber. The room temperature is 19.8 ℃, the humidity is 60%, and the subject is a healthy man without respiratory diseases. In order not to affect the experimental results, the subject did not do strenuous exercise before the detection, and resumed the normal breathing rate after entering the laboratory for 10 min. After wearing the prepared sensor mask correctly and connecting the measurement system to the computer, the resistance was recorded 5 times per second. The subject is in an upright position keeping the mask and nose from moving relatively for 3 min in the experimental timet. The subject performed normal breathing, deep breathing, rapid breathing, and breathing while speaking. At the same time, we count and record the times of exhalation and inhalation.

Fig. 5 Measurement of breathing rate: (a) wearing of the sensor mask; (b) data acquisition process

3.2 Results and discussion of RR detection

The results to detect the RR are shown in Fig. 6. The human body’s breathing is carried out in a continuous cycle. When the subject exhales, the gas in the lungs passes through the exhalation pipe with body temperature, so the temperature of the exhaled gas at the nostrils is higher than the air temperature. When inhaling, the temperature decreases due to the effect of the gas flow rate. Therefore, when the subject is exhaling, the sensor resistance increases as the temperature rises, and the curve rises. When the subject inhales, the external gas is sucked through the nasal cavity, resulting in a low temperature around the sensor and a decrement in the resistance and the curve. The human body forms a waveform similar to a sine wave in the state of normal exhalation. Breathing waveform can be obviously observed in normal breathing, deep breathing, rapid breathing while speaking. Respiration includes two parts: exhalation and inhalation. The total number of breathing cycles per minute is called RR. Generally, the normal RR of the people is 12 to 22 times/min[18-19]. In the normal breathing state, the peaks and troughs appear 8 times within 25 s. It can be concluded that the breathing frequency is about 20 times/min, and the calculation results are in line with the normal breathing frequency of the human body. Similarly, it can be calculated that the breathing frequency of deep breathing and rapid breathing is from 14 times/min to 15 times/min and from 35 times/min to 36 times/min， respectively. This is completely consistent with the RR recorded in our experiment. At the same time, we also found that the breathing intensity of deep breathing was greater than that of normal breathing and that the intensity of fast breathing is the lowest. This is also in line with the normal law of the human respiratory system. During the inhalation process, the lung absorbs oxygen from the air and then expels carbon dioxide from the body[7].

Fig. 6 Measuring results at different breathing states: (a) normal breathing; (b) deep breathing; (c) rapid breathing; (d) normal breathing process cycle; (e) normal breathing & breathing while speaking; (f) deep breathing & rapid breathing

The difference between the peak and the trough represents the breathing intensity, as shown in Fig. 6(d). In different breathing states, the breathing intensity and breathing frequency are different, and the respiration waveform can be clearly distinguished even in the state of rapid breathing while speaking. When measuring the RR, it is monitored by changing the breathing state of the subject, such as deep breathing, rapid breathing, and switching from deep breathing to rapid breathing. As shown in Figs. 6(e) and 6(f), the breathing state of the subject can still be clearly distinguished in the speaking state. The analysis shows that the average temperature of the breathing while speaking is higher than that of the normal state, which is due to the fact that, when the subject is speaking at this time, part of the gas is exhaled from the mouth and the temperature of the human mouth is generally around 32.5 ℃, inevitably leading to a rise of the temperature in the mask. When the subject stopped speaking, the temperature inside the mask dropped rapidly, and so did the breath test curve. The experimental results show that the yarn-based temperature sensor can accurately measure breathing in various states.

4 Conclusions

The yarn-based temperature sensor is composed of a spiral structure formed by wrapping elastic spandex and platinum with temperature-sensing function. It has high elasticity and softness, and can be easily implanted into fabrics or masks. The use of temperature sensors to prepare a sensor mask that can monitor the RR can accurately reflect the breathing state of the subject in terms of RR measurement, including normal breathing, deep breathing, rapid breathing, and breathing while speaking. The above results show that the yarn-based temperature sensor as a smart textile has good performance in respiration detection.