Emerging Development of Auto-Charging Sensors for Respiration Monitoring

Owida, Hamza Abu; Al-Ayyad, Muhammad; Al-Nabulsi, Jamal I.

doi:https://doi.org/10.1155/2022/7098989

International Journal of Biomaterials

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Abstract Introduction Conclusion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Review Article | Open Access

Volume 2022 | Article ID 7098989 | https://doi.org/10.1155/2022/7098989

Emerging Development of Auto-Charging Sensors for Respiration Monitoring

Hamza Abu Owida,¹Muhammad Al-Ayyad,¹and Jamal I. Al-Nabulsi¹

Academic Editor: Sameh Ali

Received11 Apr 2022

Revised30 Jun 2022

Accepted08 Jul 2022

Published29 Aug 2022

Abstract

In recent years, the development of biomedical monitoring systems, including respiration monitoring systems, has been accelerated. Wearable and implantable medical devices are becoming increasingly important in the diagnosis and management of disease and illness. Respiration can be monitored using a variety of biosensors and systems. Auto-charged sensors have a number of advantages, including low cost, ease of preparation, design flexibility, and a wide range of applications. It is possible to use the auto-charged sensors to directly convert mechanical energy from the airflow into electricity. The ability to monitor and diagnose one’s own health is a major goal of auto-charged sensors and systems. Respiratory disease model output signals have not been thoroughly investigated and clearly understood. As a result, figuring out their exact interrelationship is a difficult and important research question. This review summarized recent developments in auto-charged respiratory sensors and systems in terms of their device principle, output property, detecting index, and so on. Researchers with an interest in auto-charged sensors can use the information presented here to better understand the difficulties and opportunities that lie ahead.

1. Introduction

Respiration is an important crucial indicator for humans because it can reflect trends in health conditions as well as the preponderance of respiratory diseases [1]. Abnormal respiration rate is also one of the indications of the severity of a COVID-19 patient’s condition, and it is often used as an index to support clinical decisions for timely interventions at the inchoate stage of the infection. The SARS-CoV-2 viral infection could cause the severe acute respiratory syndrome, disturbing regular breathing and leading to continuous coughing [2].

Respiration involves a complex biological interaction between the central nervous system, respiratory-related motor neurons, and respiration muscles [3]. Based on these interactions, various techniques have been developed for respiratory rate detection. However, accurate, continuous, and pervasive respiratory rate measurement is challenging and still under investigation, especially for direct measurement [4].

Respiratory rate is commonly defined clinically as the times of respiration measured during a minute. Respiratory rate is a crucial indicator whose abnormality indicates major clinical occurrences. Measuring respiratory rate could reveal any disturbance in the bodily system that produces hypoxaemia [5]. Significant data suggests that an irregular respiratory rate is a predictor of potentially catastrophic clinical outcomes such as intensive care unit readmission, cardiopulmonary arrest, chronic heart failure, pneumonia, and pulmonary embolism [4].

Clinically, the respiratory rate could be measured using a variety of techniques, including spirometry, capnometry, and pneumography [4], as shown in Table 1. These procedures frequently necessitate complex and expensive gear that may interfere with natural breathing and can be unmanageable in specific applications such as ambulatory monitoring, stress testing, and sleep research. Automatic, dependable, and convenient sensors and equipment might significantly enhance the status of respiratory rate monitoring [5].

Despite this, respiratory rate is one of the most overlooked, under-utilized, and under-recorded vital signs. There are several causes for this; due to intense workloads and other issues, nurses rarely have time to complete a full 60-second measurement by manual counts. Inaccuracies occur when 2 or 4 multiply a 30-second or 15-second assessment [1, 5].

Interruptions, changing patients, difficulties counting or remembering a count, and poor visualization of the start and finish of a breath can all lead to further errors. Such problems connected with human counting can be alleviated by technologies that automate respiratory rate measurement. Medical device development has gained power in recent years as a result of technological advancements and increased lifespan. Electronic monitors might automatically measure Respiratory rate, which would have numerous advantages in clinical and physiological applications. Automatic respiratory rate monitoring has the potential to predict potentially significant clinical outcomes such as cardiac arrest or intensive care unit admission [4, 5].

Commercial and respiratory sensing systems have various drawbacks, such as being huge, costly, as well as complicated, thus limiting their use on a regular basis. The rational and practical research material is how to assess breathing limits in a continuous, straightforward, and comfortable manner. Researchers have devised a number of strategies to increase the comfort and feasibility of breath monitoring, including researching touch monitoring techniques, employing soft cloths, and more [6–8]. Experts in the field of biomedical sensing have taken a keen interest in autonomous respiratory nerves in particular [9–11]. Respiration is one of the most essential human characteristics; in order to detect respiratory disorders such as asthma, apnea, pneumonia, and tuberculosis, sputum samples must be collected and tested [12–16]. A significant role has been played by the most recent wearable and artificial sensors in the measurement of respiratory parameters, which include breathing rate and energy, temperature, and specific inert gases in exhaust air [17–22]. Both active and inactive nerves demonstrate unique gains in respiratory awareness among them [23, 24].

Triboelectric nanogenerators, piezoelectric nanogenerators (PENG), pyroelectric nanogenerators, hydroelectric nanogenerators, and hygroelectric nanogenerator devices are examples of artificial devices with biomechanical or thermal energy that can be converted directly into electricity [25–28].

The electrical impulses that they emit have a strong link to the respiratory tract. The airflow is driven by the TENG and can generate pulses that are correlated to the dynamics of flow of air [29, 30]. Since the demonstration of self-contained polyvinylidene fluoride respiratory sensors in 1990 [31], many advancements in material science and micro/nanofabrication technologies have been made [32–34].

Nanogenerators based on piezoelectric and triboelectric effects, in particular, encourage smaller, simpler, more sensitive, and precise autonomic respiratory and sensory systems that are more suitable for wearable and wearable circumstances [35–38]. Scientists have progressed to a detailed examination of nanogenerators utilized in bioengineering, with a special focus on TENG’s future application and predilection for biological sensors such as respiratory monitoring [39–42]. Tai et al. provide an update on the emergence of respiratory analysis, which began with the possibility of moisture and gas sensitivity [43].

Furthermore, Su et al. recently published a study on TENG-enabled autonomous respiratory monitoring, which clearly demonstrates the sensitivity and model of your theater in both respiratory signal collecting and cellular respiratory analysis [44]. Due to its features including diminutive, low toxicity, light construction, simple make-up, and extensive applications, the nanogenerator-based respirator active biosensing is a critical component of the research and development of these systems. These self-regulating respiratory systems may also be used to connect smart remote monitoring sensors that become more common in the near future. From device-type features to output performance to respiratory sensor index, we have looked at the evolution of self-regulating respirator monitoring systems and systems. The air that is breathed through the mouth can stimulate self-regulating nerves, indicating direct breathing circumstances. Researchers have also created self-propelled heat sensors that use piezoelectric ceramic materials and nanogenerators can be used to trace gases in the lungs. In the meantime, we give problems and suggestions for breathing self-monitoring. Despite many notable improvements, several challenges remain, such as the difficulty of giving appropriate diagnostic information based on the effects of the autonomic nerves to date. However, artificial intelligence and other emerging, novel approaches to treating breathing impairments are being considered; we may be able to gather additional information on the impacts of the equipment they operate.

2. Application of Auto-Charged Sensors in Respiration Tracking

The concept of auto-charged sensors exhausting polyvinylidene fluoride or polyvinylidene difluoride strip in cantilever activation as a hearing aid was initially proposed by Roopa et al. in 2011 [6]. For a long time, the respiratory monitoring system’s conscious stage was largely dependent on sensing applications with limited functionality and huge dimensions. Soon later, the usage of an autonomic respiratory monitoring system was created. They are separated into three functions: the collection of respiratory signals, the monitoring of respiratory temperature and humidity, and the detection of exhaust gas cells [45, 46]. They are divided into categories based on how they are used, with implanted and wearable ventilation systems being the most common as shown in Figure 1.

3. Respiratory Temperature/Humidity Monitoring

Human respiration is hot and humid without mechanical energy [47–49]. Some self-regulating breathing systems detect respiratory temperature/humidity by gathering these symptoms (Figure 2). Xue and colleagues create a portable respirator pyroelectric sensing element with a component of polyvinylidene fluoride or polyvinylidene difluoride that could be activated thru breathing and has a high sensitivity to various respiratory conditions [50]. Due to its positive linearity and room temperature, the pyroelectric respirator sensor can also be used to detect ambient temperature. Roy et al. built the most flexible pyroelectric nanogenerator based on graphene in 2019 [51]. 4.3 V/kPa is possible. It also has a pyroelectric output power more than 1.2 nW/m2 with the degree of heat induced by continuous human breathing, indicating the potential for a pyroelectric respiratory sensor. Most of the self-activating respiratory sensors are dependent on the hydroelectric effect of graphene oxide, decreased graphene oxide, and so on [52]. In 2015, Zhao et al. first propose an auto-charged moisture sensing element to monitor breathing, which detects moisture signals based on single graphene oxide [53]. The graphene oxide mechanism that produces electricity from moisture is called the hydroelectric effect later [54, 55]. Mechanical ventilation is used to prepare a single graphene oxide, which can form a gradient of a group containing oxygen. When graphene oxide contacts with moisture, a concentrated H⁺ level can attract the movement of strong and free electrons in the outer cycle. Because of extreme sensitivity to changes in humidity, which have developed a self-regulating respiratory system to yield respiratory power, outgoing and current voltage can reach 18 mV, through the dampening effect of a normal male study.

After a range of exercises, graphene oxide can monitor the respiratory frequency and heart rate in a healthy patient. Following that, their team presented a stable dynamic transition based on the encounters of graphene oxide nanoribbon networks [46]. In the long run, there is a low probability of failure; the flexible effect membrane achieved high breathability for writing and reading. They also developed a breathable investigator battery using graphene oxide and lithium foil [56]. The graphene oxide film collects and transfers moisture in this battery, whereas the lithium foil reacts to redox moisture exposure. This work demonstrates promising power in the use of future respiratory biomedical sensor systems. Other researchers created self-contained artificial respiratory sensors based on the hydroelectric effect of graphene oxide [57–61]. Bo-skovic et al. introduced a humidity sensor based on an aluminum breathing monitor; no external power supply is required because of the low volume [62]. Using 1% silicone thin foam film as a composite capacitor, the air humidity sensor has the same functionality as an aluminum air battery. A structure with holes and nanochannels promotes the advertising and molecules of water distribution. To monitor changes in human skin moisture and respiratory rate, this sensor can be used.

4. Expiratory Breathing Gas Molecular Detection

Efficient ventilators have detected some of the respiratory tract’s gas emissions, providing a possible way to improve the structure of respiratory observing schemes and reinforce the connections between respiratory symptoms and disease identification (Figure 3). Using carbon dioxide concentration, odors in exhaust systems can be removed [7]. Kim and coauthors proposed a three-dimensional triboelectric respirator sensing element that is cast off to detect real-time respiratory signals, which are made up of polyethylenimine-coated ethylene propylene membrane with a willow coating [45]. This is one of the most significant mechanisms in the body for exchanging materials, such as humidity, temperature, and gas molecules, in between the body system and the environmental elements. The presence of nitrogen oxides, for example, is indicative of respiratory inflammatory diseases [63]; acetone is linked to diabetes [64]; and ammonia is linked to hepatitis [65–68]. Furthermore, alcohol-filled gas has been recognized as a sign of fatty liver and plays a key role in diagnosing a drunk driver [69, 70]. Investigating the voltage at the output, four breathing patterns can be detected by them, ranging from very strong to very weak to long and short. The power output of the solid mode is almost three times that of the weak one, while the short and long breathing case has the same discharge pattern of 0.5 seconds with a maximum value variance. By inventing new inventions, they use polyethylenimine as a CO₂ scanner to differentiate between expiration and respiration due to differences in CO₂ concentration. The triboelectric respiration sensor emissions decreased by 11.73% after CO₂ was injected at a speed of 6.5 m/s for an extended time period with a relative humidity of 37%.

The amount of alcohol in the exhaust gas is a good predictor of safety and can be used to test for drunk driving. Derived on a triboelectric nanogenerator that is driven by a blow, Wen et al. developed a self-regulating alcohol respirator [71]. Triboelectric respiration sensor identified CO₂ sensitivity in human respiratory monitoring and fostered the evolution of self-regulating respiratory neurons. It features sensors that range from 10 to 200 ppm and a response/recovery time of 11 to 20 seconds. Xue et al. establish that alcohol-filled breathing is beneficial to human respiration [72]. Triboelectric effect, gas sensitivity sandwich nanostructures, and elastin are merged in this e-skin. When an adult hits it, this self-driving system can detect an intoxicated driver. Their team also established respiratory sensors using piezo-gas-sensing arrays made of polyvinylidene fluoride. As a result, five different senses elements have identified responses that correspond to specific gas signals [73].

Therefore, this device has been proven to be useful in detecting ethanol saturation in the exhaust system. All of these activities encouraged the systematization of self-regulation and greatly improved their performance. An increase in ammonia in the concentration of exhaust gas indicates a certain disease. Nanocomposite films for respiratory strength and trace concentrations of NH₃ have been developed by Wang et al. in 2019 [74]. Subsequently, they developed a triboelectric self-powered respiratory sensor and adopted a theoretical model for human respiratory analysis and the function of the NH₃ sensor [75]. However, NH₃ sensor is important to the triboelectric respiration sensor. If connected to the chest through an external gas collection system, the triboelectric respiration sensor can collect energy and rhythm of breathing from normal expansion and contraction of the chest, recognizing the monitoring of gas emissions. Triboelectric respiration sensor output function refers to triboelectric respiration NH3 biomarker traces in outdoor electricity that can be monitored with this technology, and the different types of breathing in the zip hatha tradition (standard, deep, shallow, and rapid) are distinguished as is the standard procedure. Nitrogen dioxide, a harmful gas, is found all around us. TENG was used by Su et al. to develop a portable alveolus-inspired membrane sensor for monitoring human respiration and detecting NO₂ [76].

Alveolus-inspired membrane sensor emissions can show differences in time and respiratory power. This function provides a new way to sense toxic gases by merging breathing observing and NO₂ recognition. Internally and externally targeted gas may cause a potential electrical gap between the latex membrane and the sensitive film by expanding and contracting. Expending a composite film of tungsten trioxide to detect gas sensation, the Alveolus-inspired membrane sensor revealed admirable sensitivity of under a concentration of 80 ppm of NO₂, up to 340.24%. Compared to other gases, the Alveolus-inspired membrane sensor also exhibits a higher NO₂ sensor choice.

5. Sensing of Mechanical Breathing Activity

A number of mechanical indicators, such as the compression of the sternum and the flow of air throughout inspiration and expiration (Figure 4), are reversed by our respiratory system [77, 78]. Many autonomous respiratory systems, on the other hand, rely on pressure impulses to monitor changes in the physical state of an object mechanically [79–83]. In recent years, size and output features in diverse sensory perceptions have been used to more accurately portray self-regulating respiratory system systems for the gathering of respiratory tract data. The position of the respiratory tract has a significant impact on the signalling system, creating a shift in the location of the machines that determine whether they are driven by body movement or airflow. Body motions are the primary source of power for the nerves implanted in the belly, chest, and throat.

They frequently have a larger output than other air-driven airflow due to their high mobility. High temperatures, humidity, and the detection of molecules, on the other hand, are reflected in the gas-collecting respiratory systems in the nose and mouth. Breathing sensors are frequently implanted to collect energy and detect mechanical signals and the mouth. Breathing sensors are frequently implanted to collect output breathing mechanical activity. Predicated on the observation of direct conversions of communication stress, one study recently launched a bilayer triboelectric sensor for sensing pressure, with resolutions of 0.34 Pa and 0.16 Pa related to the variations in the level of air pressure [84]. There may be sporadic interaction between one of the rubber films and the fluorinated ethylene propylene membrane due to movement, breathing, and heart rate, which could signal a shift in pressure. The technology can identify self-sustaining breathing monitoring in real time when coupled to an airbag and connected to the study center. Liu et al. created a human respiratory monitoring system that can feel pressure based on the microsphere-based TENG [85].

A thin triboelectric layer made up of thermal expanding microspheres and polydimethylsiloxane is the most critical component of this TENG; different points of interaction are created by changes in pressure and hence variable charges on the triboelectric surface. Sensitive to changes in breathing position, this 33 mm² pressure gauge can provide consistent output signals when connected to the chest of the head. At 30 breaths per minute and 9 breaths per minute, the recorded signal revealed a substantial difference between shallow and deep breaths, respectively. Furthermore, the microsphere-based sensor may be utilized to detect hand signals, which can help with noninvasive medical diagnostics by presenting key symptoms.

The majority of respiratory signal monitoring devices rely on direct contact between electrical objects and individuals [86–88]. Chen et al., on the other hand, saw a gap in the research of unaffected respiratory nerves and developed a microstructure sensor with an auto-charged gauge sensor that detected data gathering without direct skin contact [89]. The electrostatic events that occur between the highly charged multilayered elements are the primary mechanism of action. Auto-charged microstructure sensor can screen sensitive breathing and heart rate in a noninvasive mode while operating under body weight, consistent with significant differences between the multiple processes of a wholesome subject. The advance of offline auto-charged microstructure sensors has enhanced the sense of well-being of users, which has encouraged the review of monitoring systems for the health of people’s sleep patterns. Furthermore, researchers have introduced a breathing apparatus that works by directly touching the skin. The majority of these are entirely predicated on the sensor that builds up near the chest, abdomen, and throat. As a result of the skin’s proximity to the breathing nerve within those locations, the breathing signals can be directly obtained from the skin. Generally, the acquisition of mechanical breathing activity technology is easier than inhaling and exhaling through the nose and mouth because body movements are greater than the force of air currents. In addition, energy-efficient respiratory nerves can also be driven by airflow produced by respiratory behaviour. Wang et al. introduced a TENG respirator-driven air sensor, in which a thin film of nanostructured polytetrafluoroethylene flexible was usually vibrated with an exposed to airflow; acrylic nozzle becomes transparent [30]. TENG has a corresponding reaction to different respiratory conditions, and the increasing amount of charge passed through the respiratory process is closely related to the total amount of gas converted.

6. Implantable and Wearable Breathing Sensing Elements

Considering the issue of safety and ethical testing, a few studies on the collection of breathing signs were performed in vivo. Animal studies have shown that a few related studies can be carried out. The heart, lungs, and diaphragm can harvest energy from rest and relaxation as integrated biomedical systems (Figure 5), allowing people to breathe naturally for the first time [90]. Using an ultrastretchable micrograting system, Li et al. designed nanogenerator prostheses capable of powering implanted medical devices [20]. After being implanted into the abdominal cavity of Sprague-Dawley adult rats, it can harvest energy from the regular diaphragm movement during respiratory process and output electrical signals correlated with the inhale and exhale part. Nowadays, portable electronic surveillance devices are often self-powered, configured, and operated in a variety of ways [91, 92].

As living standards increase and the level of education increases, autonomous respiratory systems have a much lower tendency for performance, ease, and efficiency [93–95], addressing social issues through the creation of artificial and wearable nerves [96–99]. The aforementioned self-regulating breathing apparatus is an electronic device that is worn extensively, facial or abdominal masks, or any combination of these items. But implanted sensors have many advantages, such as high accuracy and the ability to monitor specific biomedical signals for an extended period of time [92, 100]. However, the development of implanted monitoring devices is less battery life [101]. An energy-efficient respirator system usually combines energy harvesting work with the collection of respiratory symptoms. Ma et al. established an effective triboelectric sensor input for monitoring the human body in real time [102]. It is made up of triboelectric layers, electrodes, and spacers, and all structures are closed with a flexible multilayer shell. Packed in a pericardial sac, the implantable triboelectric active sensor not only can monitor respiratory rhythm and blood pressure. During the breathing process, the implantable triboelectric active sensor output peaks rose from 4.8 V to 6.3 V when breathing and descended to the real state of ventilation. Zheng et al. designed a multimodal triboelectric nanogenerator as a self-regulatory health monitoring system [103].

When powered by a Yorkshire pig heartbeat, it can generate an open-circuit voltage of 14 V and a short-circuit current of 5 μA. In addition, the implantable triboelectric nanogenerator has output peaks that correspond to the respiratory cycle. Moreover, it was introduced the function of implantable triboelectric nanogenerator using the harvesting force of respiratory mechanical action [92]. Following that, inserted under the chest of the left mouse, it can measure the vital volume of the forced mouse. Implantable triboelectric nanogenerator demonstrates the potential to develop health facilities that incorporate artificial intelligence. Compared to artificial energy sensors, wearable sensors are more flexible and easier to replicate [10, 93, 104, 105]. The wearable respiratory sensor can be used in conjunction with a variety of other wearable devices, including face masks [106] and chest/abdomen belts [107, 108]. Using electrospun polyetherimide nonwoven as electret materials, Cheng et al. produce an energy-efficient wearable face mask [109]. A smart face mask can eliminate submicron particulate matter more efficiently and harvest energy from airflow based on the cost that retains polyetherimide potential during severe humidity conditions. The mask can be used as a self-powered biomedical sensor to detect breathing levels during human respiratory movements. A normal person breathes roughly 21 times each minute. The effectiveness of a filter can be measured using an LCD display and this technique. Respiration is monitored via graphene fibers that use moisture-electric energy transformation to collect energy from the environment [110].

This gadget can detect changes between different human breathing conditions when coupled to a mask and has a maximum output voltage of 292 mV. Advanced diatom frustrations of cellulose nanofibril-based TENG respiratory monitoring were introduced by Rajabi-Abhari et al., capable of producing an average of 85.5 mW/cm³ with an effective interaction capacity of 4.9 cm² in isolation mode [111]. They have designed a smart mask that uses discarded energy because of this DF-CNF TENG. When a person breathes, airflow will cause the fluorinated ethylene propylene film to move between the two layers of the diatom frustules enhanced cellulose nanofibre, thus producing the energy to release energy associated with respiratory conditions. The single-TENG mode has a lower voltage than the TENG-based dual-sensing breathing sensor. Because of its small size and high output, this sensor will be useful in future skin-attached wearable health monitoring systems. Additional research is being done on flexible fabrics, e-skin, and chest/abdomen bands [112–114], as well as smart facial masks. Using a chest movement harvester, Shahhaidar et al. in 2013 developed a self-powered respiratory biosensor [115, 116].

They have created piezoelectric and electromagnetic sensor/harvester devices that can be added to the chest band. In 2015, they created a new wearable electromagnetic self-powered sensor that was also attached to the chest band [117]. It is capable of keeping track of the breathing process by gathering the power produced by the movement of the chest wall by implanting this neural system in the xyphoid system and the navel. Vasandani et al. develop a portable respirator with a microdome pattern (wREH) based on contact separation mode [118]. The low-density PDMS’ soft design aims to improve wREH output performance. To track breathing levels and depth, wREH can be attached to a belt that does not follow the rules imposed around the abdomen. The open-circuit voltage reaches 16.8 V under typical respiratory test conditions, revealing the real power of future biomedical sensory networks.

7. Conclusion and Perspectives

The growth of biomedical monitoring programs, particularly respiratory monitoring programs, has increased in recent years as a result of the growing promotion of healthy living and quality. In this comparison, the benefits and drawbacks of the most recent respiration monitoring systems by different types of signal collection are discussed with their perspectives. Flow variations, including the duty cycle, intensity, and density of cross ventilation throughout breathing, have a close association with the output of the respiratory tract driven by airflow. Although the pyroelectric and hydroelectric effects of the respiratory system are mainly dependent on the temperature and humidity of the exhaust gas, respectively, it is easily modified by external forces. In addition, the respiratory sensor for cell detection can provide extra information on the chemical composition, which can help with disease diagnosis. Furthermore, by combining the diverse qualities of the internet of things, an intelligent healthcare system has spurred the development of powerful artificial electronics. However, there are still some challenges that need to be overcome for the development in the future, and they are described as follows.

7.1. Miniaturization

The quick progress of energy-based respiratory monitoring studies is encouraging. Many of the incredible self-regulating breathing systems are based on a combination of respiratory symptoms such as nasal congestion, mouth breathing, and chest motions. Airflow, humidity, temperature, and molecules are all detectable in the exhaled gas, but chest motions provide primarily mechanical information in the form of pressure changes. The majority of respirators are built into masks, belts, or belts. Resources should be kept as few as possible to promote user comfort. Some researchers are now using flexible materials to produce energy-efficient breathing sensors, hence improving user comfort. External power dependencies can be eliminated with independent sensors, which have the benefit of lowering device ratings. Advanced micro-/nanofabrication technologies, on the other hand, may offer a more complex way to produce an integrated and compact system. Personal wearable equipment will be possible in the future because of a flexible breathing mechanism built of microscopic pledges.

7.2. Stability

Respiratory rhythm, respiratory capacity, acetone, respiratory moisture, and other physical and biochemical indicators of respiratory activity may reflect the health or sickness of the human body. If other devices can detect many of the above symptoms in real time and simultaneously, the devices’ stability cannot be overlooked. Developing a system encapsulation method, such as wrapping a device in tiny layers of expandable polymers, could improve its stability. To acquire reliable signals, most respiratory monitoring equipment should be limited to a specific location during this time. The respiratory system’s antijamming capabilities and functional health are critical for future development. Maintaining consistent sensory qualities of the energy system during daily life is a huge problem, especially in real time and for long-term acquisition.

7.3. Improved Output

The power supply limit still exists for the most recent self-operating sensors or devices. Although harmful methods such as piezoelectric, triboelectric, and pyroelectric devices can harvest energy from the environment, the energy efficiency of the entire respiratory monitoring system is insufficient to match the power consumption of the system. A major task will be to increase the performance of the harvesting power system in order to drive the system in real time. The sensor data for the self-regulatory respiratory system should then be delivered to terminals such as computers and mobile phones, which will interpret the data and provide an analysis of the users’ respiratory health. Wireless data transmission is required to ensure the device’s wearability. The devices’ power consumption increases as a result of the wireless transmission of received signals. The average power consumption, for example, ranges from a few millimeters to tens of millimeters, which is higher than the output of several devices harvesting less wearable electricity.

Nanomaterial-based sensors have been developed by researchers to diagnose respiratory disorders. However, a reliable medical diagnosis is still a long way off. Another goal of the respiratory monitoring program is to give clients and physicians the findings of prior tests as well as professional recommendations. Worse, the most recent artificial respiratory nerves are unable to deliver appropriate results in terms of respiratory disease diagnosis. The fundamental obstacle is that each user’s distinct breathing variances will generate signal differences, posing numerous challenges in accurately diagnosing respiratory disorders. One possible option is to employ artificial intelligence to deal with symptoms, which has been used extensively in disease diagnosis. Self-regulating respiratory systems for modern medical diagnostics and healthcare, on the other hand, should be multitasking.

A diagnosis of the respiratory disease typically necessitates the collection of health data on a variety of factors, including the amount of gas moved, respiratory frequency, and organic variables in the respiratory system. The rapid growth and popularity of 5G have recently brought considerable benefits to a smart city’s wireless sensor system, indicating that it is a major artificial intelligence site with high accuracy for respiratory sickness. Hopefully, we will soon be able to benefit from the convenience of digital communication via wireless self-driving sensors. Table 2 summarized, and contrasted the most recent auto-charging breathing monitoring systems in terms of their advantages and challenges.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest to report regarding the present work.

Acknowledgments

This work was supported and sponsored by Al-Ahliyya Amman University.

References

G. Viegi, S. Maio, S. Fasola, and S. Baldacci, “Global burden of chronic respiratory diseases,” Journal of Aerosol Medicine and Pulmonary Drug Delivery, vol. 33, no. 4, pp. 171–177, 2020.
View at: Publisher Site | Google Scholar
C. Massaroni, A. Nicolò, E. Schena, and M. Sacchetti, “Remote respiratory monitoring in the time of COVID-19,” Frontiers in Physiology, vol. 11, p. 635, 2020.
View at: Publisher Site | Google Scholar
K. Ikeda, K. Kawakami, H. Onimaru et al., “The respiratory control mechanisms in the brainstem and spinal cord: integrative views of the neuroanatomy and neurophysiology,” The Journal of Physiological Sciences, vol. 67, no. 1, pp. 45–62, 2017.
View at: Publisher Site | Google Scholar
H. Liu, J. Allen, D. Zheng, and F. Chen, “Recent development of respiratory rate measurement technologies,” Physiological Measurement, vol. 40, no. 7, 2019.
View at: Publisher Site | Google Scholar
G. Singh, A. Tee, T. Trakoolwilaiwan, A. Taha, and M. Olivo, “Method of respiratory rate measurement using a unique wearable platform and an adaptive optical-based approach,” Intensive Care Medicine Experimental, vol. 8, no. 1, pp. 15–20, 2020.
View at: Publisher Site | Google Scholar
G. Roopa, K. Rajanna, and M. M. Nayak, “Non-Invasive human breath sensor,” IEEE Sensor, pp. 1788–1791, 2011.
View at: Publisher Site | Google Scholar
J. Dai, L. Li, B. Shi, and Z. Li, “Recent progress of self-powered respiration monitoring systems,” Biosensors and Bioelectronics, vol. 194, Article ID 113609, 2021.
View at: Publisher Site | Google Scholar
Z. Zhou, S. Padgett, Z. Cai et al., “Single-layered ultra-soft washable smart textiles for all-around ballistocardiograph, respiration, and posture monitoring during sleep,” Biosensors and Bioelectronics, vol. 155, Article ID 112064, 2020.
View at: Publisher Site | Google Scholar
H. Ouyang, D. Jiang, Y. Fan, Z. L. Wang, and Z. Li, “Self-powered technology for next-generation biosensor,” Science Bulletin, vol. 66, no. 17, pp. 1709–1712, 2021.
View at: Publisher Site | Google Scholar
Y. Zou, L. Bo, and Z. Li, “Recent progress in human body energy harvesting for smart bioelectronic system,” Fundamental Research, vol. 1, no. 3, pp. 364–385, 2021.
View at: Publisher Site | Google Scholar
Y. Zou, J. W. Liao, H. Ouyang et al., “A flexible self-arched biosensor based on combination of piezoelectric and triboelectric effects,” Applied Materials Today, vol. 20, Article ID 100699, 2020.
View at: Publisher Site | Google Scholar
N. Alkhouri, F. Cikach, K. Eng et al., “383 analysis of breath volatile organic compounds as a noninvasive tool to diagnose nonalcoholic fatty liver disease in obese children,” Gastroenterology, vol. 144, no. 5, pp. S-947–S-948, 2013.
View at: Publisher Site | Google Scholar
B. Buszewski, M. Kesy, T. Ligor, and A. Amann, “Human exhaled air analytics: biomarkers of diseases,” Biomedical Chromatography, vol. 21, no. 6, pp. 553–566, 2007.
View at: Publisher Site | Google Scholar
H. Haick, Y. Y. Broza, P. Mochalski, V. Ruzsanyi, and A. Amann, “Assessment, origin, and implementation of breath volatile cancer markers,” Chemical Society Reviews, vol. 43, no. 5, pp. 1423–1449, 2014.
View at: Publisher Site | Google Scholar
D. Hashoul and H. Haick, “Sensors for detecting pulmonary diseases from exhaled breath,” European Respiratory Review, vol. 28, no. 152, Article ID 190011, 2019.
View at: Publisher Site | Google Scholar
P. Paredi, S. A. Kharitonov, and P. J. Barnes, “Elevation of exhaled ethane concentration in asthma,” American Journal of Respiratory and Critical Care Medicine, vol. 162, no. 4, pp. 1450–1454, 2000.
View at: Publisher Site | Google Scholar
S. C. Gandevia and D. K. McKenzie, “Respiratory rate: the neglected vital sign,” Medical Journal of Australia, vol. 189, no. 9, p. 531, 2008.
View at: Publisher Site | Google Scholar
T. Hoffmann, B. Eilebrecht, and S. Leonhardt, “Respiratory monitoring system on the basis of capacitive textile force sensors,” IEEE Sensors Journal, vol. 11, no. 5, pp. 1112–1119, 2011.
View at: Publisher Site | Google Scholar
S. Kano, A. Yamamoto, A. Ishikawa, and M. Fujii, “Respiratory rate on exercise measured by nanoparticle-based humidity sensor,” in Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3567–3570, Berlin, Germany, 2019.
View at: Google Scholar
J. Li, L. Kang, Y. Long et al., “Implanted battery free direct-current micro-power supply from in vivo breath energy harvesting,” ACS Applied Materials & Interfaces, vol. 10, no. 49, pp. 42030–42038, 2018.
View at: Publisher Site | Google Scholar
Y. Su, T. Yang, X. Zhao et al., “A wireless energy transmission enabled wearable active acetone biosensor for non-invasive prediabetes diagnosis,” Nano Energy, vol. 74, Article ID 104941, 2020.
View at: Publisher Site | Google Scholar
K. Xu, Y. Fujita, Y. Lu et al., “A wearable body condition sensor system with wireless feedback alarm functions,” Advanced Materials, vol. 33, no. 18, Article ID 2008701, 2021.
View at: Publisher Site | Google Scholar
Y. Li, M. Zhang, X. Hu et al., “Graphdiyne-based flexible respiration sensors for monitoring human health,” Nano Today, vol. 39, Article ID 101214, 2021.
View at: Publisher Site | Google Scholar
Q. Xu, Y. Fang, Q. Jing et al., “A portable triboelectric spirometer for wireless pulmonary function monitoring,” Biosensors and Bioelectronics, vol. 187, Article ID 113329, 2021.
View at: Publisher Site | Google Scholar
H. Feng, C. Zhao, P. Tan, R. Liu, X. Chen, and Z. Li, “Nanogenerator for biomedical applications,” Advanced Healthcare Materials, vol. 7, no. 10, Article ID e1701298, 2018.
View at: Publisher Site | Google Scholar
G. Khandelwal, N. P. Maria Joseph Raj, and S. J. Kim, “Triboelectric nanogenerator for healthcare and biomedical applications,” Nano Today, vol. 33, Article ID 100882, 2020.
View at: Publisher Site | Google Scholar
R. Luo, J. Dai, J. Zhang, and Z. Li, “Accelerated skin wound healing by electrical stimulation,” Advanced Healthcare Materials, vol. 10, no. 16, 2021.
View at: Publisher Site | Google Scholar
Y. L. Zi, L. Lin, J. Wang et al., “Triboelectric- pyroelectric-piezoelectric hybrid cell for high-efficiency energy harvesting and self-powered sensing,” Advanced Materials, vol. 27, no. 14, pp. 2340–2347, 2015.
View at: Publisher Site | Google Scholar
H. Guo, J. Chen, L. Tian, Q. Leng, Y. Xi, and C. Hu, “Airflow-induced triboelectric nanogenerator as a self-powered sensor for detecting humidity and airflow rate,” ACS Applied Materials & Interfaces, vol. 6, no. 19, pp. 17184–17189, 2014.
View at: Publisher Site | Google Scholar
M. Wang, J. H. Zhang, Y. J. Tang et al., “Air-flow driven triboelectric nanogenerators for self-powered real-time respiratory monitoring,” ACS Nano, vol. 12, no. 6, pp. 6156–6162, 2018.
View at: Google Scholar
Y. Q. Chen, L. R. Wang, and W. H. Ko, “A piezo polymer finger pulse and breathing wave sensor,” Sensors and Actuators A: Physical, vol. 23, no. 1–3, pp. 879–882, 1990.
View at: Publisher Site | Google Scholar
Y. Han, Y. Han, X. Zhang et al., “Fish gelatin based triboelectric nanogenerator for harvesting biomechanical energy and self-powered sensing of human physiological signals,” ACS Applied Materials & Interfaces, vol. 12, no. 14, pp. 16442–16450, 2020.
View at: Publisher Site | Google Scholar
S. H. Wang, X. J. Mu, X. Wang, A. Y. Gu, Z. L. Wang, and Y. Yang, “Elasto-aerodynamics-driven triboelectric nanogenerator for scavenging air-flow energy,” ACS Nano, vol. 9, no. 10, pp. 9554–9563, 2015.
View at: Publisher Site | Google Scholar
K. Y. Xie and B. Q. Wei, “Materials and structures for stretchable energy storage and conversion devices,” Advanced Materials, vol. 26, no. 22, pp. 3592–3617, 2014.
View at: Publisher Site | Google Scholar
F. R. Fan, W. Tang, and Z. L. Wang, “Flexible nanogenerators for energy harvesting and self-powered electronics,” Advanced Materials, vol. 28, no. 22, pp. 4283–4305, 2016.
View at: Publisher Site | Google Scholar
A. Tricoli, N. Nasiri, and S. Y. De, “Wearable and miniaturized sensor technologies for personalized and preventive medicine,” Advanced Functional Materials, vol. 27, no. 15, Article ID 1605271, 2017.
View at: Publisher Site | Google Scholar
Z. L. Wang, “Self-powered nanosensors and nanosystems,” Advanced Materials, vol. 24, no. 2, pp. 280–285, 2012.
View at: Publisher Site | Google Scholar
Z. L. Wang, J. Chen, and L. Lin, “Progress in triboelectric nanogenerators as a new energy technology and self-powered sensors,” Energy & Environmental Science, vol. 8, no. 8, pp. 2250–2282, 2015.
View at: Publisher Site | Google Scholar
J. Lama, A. Yau, G. Chen, A. Sivakumar, X. Zhao, and J. Chen, “Textile triboelectric nanogenerators for self- powered biomonitoring,” Journal of Materials Chemistry, vol. 9, no. 35, pp. 19149–19178, 2021.
View at: Publisher Site | Google Scholar
T. Tat, A. Libanori, C. Au, A. Yau, and J. Chen, “Advances in triboelectric nanogenerators for biomedical sensing,” Biosensors and Bioelectronics, vol. 171, Article ID 112714, 2021.
View at: Publisher Site | Google Scholar
S. Zhang, M. Bick, X. Xiao, G. Chen, A. Nashalian, and J. Chen, “Leveraging triboelectric nanogenerators for bioengineering,” Matter, vol. 4, no. 3, pp. 845–887, 2021.
View at: Publisher Site | Google Scholar
X. Zhao, H. Askari, and J. Chen, “Nanogenerators for smart cities in the era of 5G and Internet of Things,” Joule, vol. 5, no. 6, pp. 1391–1431, 2021.
View at: Publisher Site | Google Scholar
H. Tai, S. Wang, Z. Duan, and Y. Jiang, “Evolution of breath analysis based on humidity and gas sensors: potential and challenges,” Sensors and Actuators B: Chemical, vol. 318, Article ID 128104, 2020.
View at: Publisher Site | Google Scholar
Y. Su, G. Chen, C. Chen et al., “Self-powered respiration monitoring enabled by a triboelectric nanogenerator,” Advances in Materials, vol. 33, no. 35, Article ID 2101262, 2021.
View at: Publisher Site | Google Scholar
I. Kim, H. Roh, and D. Kim, “Willow-like portable triboelectric respiration sensor based on polyethylenimine-assisted CO₂ capture,” Nano Energy, vol. 65, Article ID 103990, 2019.
View at: Publisher Site | Google Scholar
F. Zhao, L. Wang, Y. Zhao, L. Qu, and L. Dai, “Graphene oxide nanoribbon assembly toward moisture-powered information storage,” Advanced Materials, vol. 29, no. 3, Article ID 1604972, 2017.
View at: Publisher Site | Google Scholar
G. E. Carpagnano, M. P. Foschino-Barbaro, C. Crocetta et al., “Validation of the exhaled breath temperature measure,” Chest, vol. 151, no. 4, pp. 855–860, 2017.
View at: Publisher Site | Google Scholar
A. Sultana, M. M. Alam, T. R. Middya, and D. Mandal, “A pyroelectric generator as a self-powered temperature sensor for sustainable thermal energy harvesting from waste heat and human body heat,” Applied Energy, vol. 221, pp. 299–307, 2018.
View at: Publisher Site | Google Scholar
H. Zhao, L. Liu, X. Lin et al., “Proton-conductive gas sensor: a new way to realize highly selective ammonia detection for analysis of exhaled human breath,” ACS Sensors, vol. 5, no. 2, pp. 346–352, 2020.
View at: Publisher Site | Google Scholar
H. Xue, Q. Yang, D. Y. Wang et al., “A wearable pyroelectric nanogenerator and self-powered breathing sensor,” Nano Energy, vol. 38, pp. 147–154, 2017.
View at: Publisher Site | Google Scholar
K. Roy, S. K. Ghosh, A. Sultana et al., “A self-powered wearable pressure sensor and pyroelectric breathing sensor based on GO interfaced PVDF nanofibers,” ACS Applied Nano Materials, vol. 2, no. 4, pp. 2013–2025, 2019.
View at: Publisher Site | Google Scholar
J. Feng, M. Xiao, Z. Hui et al., “High-performance magnesium-carbon nanofiber hygroelectric generator based on interface-mediation-enhanced capacitive discharging effect,” ACS Applied Materials & Interfaces, vol. 12, no. 21, pp. 24289–24297, 2020.
View at: Publisher Site | Google Scholar
F. Zhao, H. H. Cheng, Z. P. Zhang, L. Jiang, and L. T. Qu, “Direct power generation from a graphene oxide film under moisture,” Advanced Materials, vol. 27, no. 29, pp. 4351–4357, 2015.
View at: Publisher Site | Google Scholar
Y. X. Huang, H. H. Cheng, C. Yang et al., “Interface-mediated hygroelectric generator with an output voltage approaching 1.5 volts,” Nature Communications, vol. 9, no. 1, p. 4166, 2018.
View at: Publisher Site | Google Scholar
C. Yang, Y. X. Huang, H. H. Cheng, L. Jiang, and L. T. Qu, “Hygroelectric generators: rollable, stretchable, and reconfigurable graphene hygroelectric generators,” Advanced Materials, vol. 31, no. 2, Article ID 1970013, 2019.
View at: Publisher Site | Google Scholar
M. H. Ye, H. H. Cheng, J. Gao, C. X. Li, and L. T. Qu, “A respiration-detective graphene oxide/lithium battery,” Journal of Materials Chemistry, vol. 4, no. 48, pp. 19154–19159, 2019.
View at: Google Scholar
Z. Fang, J. Feng, X. Fu et al., “Humidity and pressure dual responsive metal-water batteries enabled by three-in-one all-polymer cathodes for smart self-powered systems,” ACS Applied Materials & Interfaces, vol. 12, no. 21, pp. 23853–23859, 2020.
View at: Publisher Site | Google Scholar
D. Z. Shen, Y. Xiao, G. S. Zou et al., “Exhaling-driven hydroelectric nanogenerators for stand-alone nonmechanical breath analyzing,” Advanced Materials Technologies, vol. 5, no. 1, Article ID 1900819, 2020.
View at: Publisher Site | Google Scholar
W. Gui-Xin, P. Zhi-Bin, and Y. Chang-Hui, “Inkjet-printing and performance investigation of self-powered flexible graphene oxide humidity sensors,” Journal of Inorganic Materials, vol. 34, no. 1, pp. 114–120, 2019.
View at: Publisher Site | Google Scholar
D. L. Wen, X. Liu, H. T. Deng et al., “Printed silk-fibroin-based triboelectric nanogenerators for multi-functional wearable sensing,” Nano Energy, vol. 66, Article ID 104123, 2019.
View at: Publisher Site | Google Scholar
Y. Xiao, D. Z. Shen, G. S. Zou et al., “Self powered, flexible and remote-controlled breath monitor based on TiO₂ nanowire networks,” Nanotechnology, vol. 30, no. 32, Article ID 325503, 2019.
View at: Publisher Site | Google Scholar
M. V. Boskovic, M. Sarajlic, M. Frantlovic et al., “Aluminum-based self-powered hyper-fast miniaturized sensor for breath humidity detection,” Sensors and Actuators B: Chemical, vol. 321, Article ID 128635, 2020.
View at: Publisher Site | Google Scholar
M. Barker, M. Hengst, J. Schmid et al., “Volatile organic compounds in the exhaled breath of young patients with cystic fibrosis,” European Respiratory Journal, vol. 27, no. 5, pp. 929–936, 2006.
View at: Publisher Site | Google Scholar
F. J. Pasquel and G. E. Umpierrez, “Hyperosmolar hyperglycemic state: a historic review of the clinical presentation, diagnosis, and treatment,” Diabetes Care, vol. 37, no. 11, pp. 3124–3131, 2014.
View at: Publisher Site | Google Scholar
G. de Gennaro, S. Dragonieri, F. Longobardi et al., “Chemical characterization of exhaled breath to differentiate between patients with malignant plueral mesothelioma from subjects with similar professional asbestos exposure,” Analytical and Bioanalytical Chemistry, vol. 398, no. 7-8, pp. 3043–3050, 2010.
View at: Publisher Site | Google Scholar
P. Paredi, S. A. Kharitonov, and P. J. Barnes, “Analysis of expired air for oxidation products,” American Journal of Respiratory and Critical Care Medicine, vol. 166, no. supplement_1, pp. S31–S37, 2002.
View at: Publisher Site | Google Scholar
K. D. G. van de Kant, L. J. T. M. van der Sande, Q. Jobsis, O. C. P. van Schayck, and E. Dompeling, “Clinical use of exhaled volatile organic compounds in pulmonary diseases: a systematic review,” Respiratory Research, vol. 13, no. 1, p. 117, 2012.
View at: Publisher Site | Google Scholar
W. Bernal, G. Auzinger, A. Dhawan, and J. Wendon, “Acute liver failure,” The Lancet, vol. 376, no. 9736, pp. 190–201, 2010.
View at: Publisher Site | Google Scholar
L. W. Chan, M. N. Anahtar, T. H. Ong, K. E. Hern, R. R. Kunz, and S. N. Bhatia, “Engineering synthetic breath biomarkers for respiratory disease,” Nature Nanotechnology, vol. 15, no. 9, pp. 792–800, 2020.
View at: Publisher Site | Google Scholar
S. F. Solga, “Breath volatile organic compounds for the gut-fatty liver axis: promise, peril, and path forward,” World Journal of Gastroenterology, vol. 20, no. 27, pp. 9017–9025, 2014.
View at: Publisher Site | Google Scholar
Z. Wen, J. Chen, M. H. Yeh et al., “Blow-driven triboelectric nanogenerator as an active alcohol breath analyzer,” Nano Energy, vol. 16, pp. 38–46, 2015.
View at: Publisher Site | Google Scholar
X. Y. Xue, Y. M. Fu, Q. Wang, L. L. Xing, and Y. Zhang, “Outputting olfactory bionic electric impulse by PANI/PTFE/PANI sandwich nanostructures and their application as flexible, smelling electronic skin,” Advanced Functional Materials, vol. 26, no. 18, pp. 3128–3138, 2016.
View at: Publisher Site | Google Scholar
Y. Fu, H. He, T. Zhao et al., “A self powered breath analyzer based on PANI/PVDF piezo-gas-sensing arrays for potential diagnostics application,” Nano-Micro Letters, vol. 10, no. 4, p. 76, 2018.
View at: Publisher Site | Google Scholar
S. Wang, H. L. Tai, B. H. Liu et al., “A facile respiration-driven triboelectric nanogenerator for multifunctional respiratory monitoring,” Nano Energy, vol. 58, pp. 312–321, 2019.
View at: Publisher Site | Google Scholar
S. Wang, Y. D. Jiang, H. L. Tai et al., “An integrated flexible self-powered wearable respiration sensor,” Nano Energy, vol. 63, Article ID 103829, 2019.
View at: Publisher Site | Google Scholar
Y. Su, J. Wang, B. Wang et al., “Alveolus-inspired active membrane sensors for self-powered wearable chemical sensing and breath analysis,” ACS Nano, vol. 14, no. 5, pp. 6067–6075, 2020.
View at: Publisher Site | Google Scholar
A. Amann, W. Miekisch, J. Schubert et al., “Analysis of exhaled breath for disease detection,” Annual Review of Analytical Chemistry, vol. 7, no. 1, pp. 455–482, 2014.
View at: Publisher Site | Google Scholar
T. Bruderer, T. Gaisl, M. T. Gaugg et al., “On-line analysis of exhaled breath focus review,” Chemistry Review, vol. 119, no. 19, pp. 10803–10828, 2019.
View at: Publisher Site | Google Scholar
X. L. Chen, J. Y. Shao, N. L. An et al., “Self powered flexible pressure sensors with vertically well-aligned piezoelectric nanowire arrays for monitoring vital signs,” Journal of Materials Chemistry, vol. 3, no. 45, pp. 11806–11814, 2015.
View at: Publisher Site | Google Scholar
X. X. Chen, Y. Song, Z. M. Su et al., “Flexible fiber-based hybrid nanogenerator for biomechanical energy harvesting and physiological monitoring,” Nano Energy, vol. 38, pp. 43–50, 2017.
View at: Publisher Site | Google Scholar
K. Y. Chun, Y. J. Son, S. Seo, H. J. Lee, and C. S. Han, “Nonlinearly frequency-adaptive, self-powered, proton-driven somatosensor inspired by a human mechanoreceptor,” ACS Sensors, vol. 5, no. 3, pp. 845–852, 2020.
View at: Publisher Site | Google Scholar
B. Wang, C. Liu, Y. J. Xiao et al., “Ultrasensitive cellular fluorocarbon piezoelectret pressure sensor for self-powered human physiological monitoring,” Nano Energy, vol. 32, pp. 42–49, 2017.
View at: Publisher Site | Google Scholar
D. Z. Zhang, Z. Y. Xu, Z. M. Yang, and X. S. Song, “High-performance flexible self-powered tin disulfide nanoflowers/reduced graphene oxide nanohybrid-based humidity sensor driven by triboelectric nanogenerator,” Nano Energy, vol. 67, Article ID 104251, 2020.
View at: Publisher Site | Google Scholar
P. Bai, G. Zhu, Q. S. Jing et al., “Membrane-based self-powered triboelectric sensors for pressure change detection and its uses in security surveillance and healthcare monitoring,” Advanced Functional Materials, vol. 24, no. 37, pp. 5807–5813, 2014.
View at: Publisher Site | Google Scholar
Z. X. Liu, Z. Z. Zhao, X. W. Zeng, X. L. Fu, and Y. Hu, “Expandable microsphere-based triboelectric nanogenerators as ultrasensitive pressure sensors for respiratory and pulse monitoring,” Nano Energy, vol. 59, pp. 295–301, 2019.
View at: Publisher Site | Google Scholar
Y. Cheng, X. Lu, K. Hoe Chan et al., “A stretchable fiber nanogenerator for versatile mechanical energy harvesting and self-powered full-range personal healthcare monitoring,” Nano Energy, vol. 41, pp. 511–518, 2017.
View at: Publisher Site | Google Scholar
N. Rao Alluri, V. Vivekananthan, A. Chandrasekhar, and S. J. Kim, “Correction: adaptable piezoelectric hemispherical composite strips using a scalable groove technique for a self-powered muscle monitoring system,” Nanoscale, vol. 10, no. 6, p. 3069, 2018.
View at: Publisher Site | Google Scholar
B. Sadri, A. M. Abete, and R. V. Martinez, “Simultaneous electrophysiological recording and self-powered biosignal monitoring using epidermal, nanotexturized, triboelectronic devices,” Nanotechnology, vol. 30, no. 27, Article ID 274003, 2019.
View at: Publisher Site | Google Scholar
S. Chen, N. Wu, L. Ma et al., “Noncontact heartbeat and respiration monitoring based on a hollow microstructured self-powered pressure sensor,” ACS Applied Materials & Interfaces, vol. 10, no. 4, pp. 3660–3667, 2018.
View at: Publisher Site | Google Scholar
C. Dagdeviren, B. D. Yang, Y. Su et al., “Conformal piezoelectric energy harvesting and storage from motions of the heart, lung, and diaphragm,” Proceedings of the National Academy of Sciences, vol. 111, no. 5, pp. 1927–1932, 2014.
View at: Publisher Site | Google Scholar
H. Sheng, X. Zhang, J. Liang et al., “Recent advances of energy solutions for implantable bioelectronics,” Adv Healthc Mater, vol. 10, no. 17, Article ID e2100199, 2021.
View at: Publisher Site | Google Scholar
Q. Zheng, B. Shi, F. Fan et al., “In vivo powering of pacemaker by breathing-driven implanted triboelectric nanogenerator,” Advanced Materials, vol. 26, no. 33, pp. 5851–5856, 2014.
View at: Publisher Site | Google Scholar
M. M. Alam, S. Lee, M. Kim, K. S. Han, V. A. Cao, and J. Nah, “Ultra-flexible nanofiber-based multifunctional motion sensor,” Nano Energy, vol. 72, Article ID 104672, 2020.
View at: Publisher Site | Google Scholar
R. C. Reid and I. Mahbub, “Wearable self-powered biosensors,” Current Opinion in Electrochemistry, vol. 19, pp. 55–62, 2020.
View at: Publisher Site | Google Scholar
X. Tao, “Study of fiber-based wearable energy systems,” Accounts of Chemical Research, vol. 52, no. 2, pp. 307–315, 2019.
View at: Publisher Site | Google Scholar
Y. Shi, R. Liu, L. He, H. Feng, Y. Li, and Z. Li, “Recent development of implantable and flexible nerve electrodes,” Smart Materials in Medicine, vol. 1, pp. 131–147, 2020.
View at: Publisher Site | Google Scholar
T. Sun, L. Shen, Y. Jiang et al., “Wearable textile supercapacitors for self-powered enzyme-free smart sensors,” ACS Applied Materials & Interfaces, vol. 12, no. 19, pp. 21779–21787, 2020.
View at: Publisher Site | Google Scholar
H. Wu, Y. A. Huang, F. Xu, Y. Q. Duan, and Z. P. Yin, “Energy harvesters for wearable and stretchable electronics: from flexibility to stretchability,” Advanced Materials, vol. 28, no. 45, pp. 9881–9919, 2016.
View at: Publisher Site | Google Scholar
Z. Z. Zhao, C. Yan, Z. X. Liu et al., “Machine-washable textile triboelectric nanogenerators for effective human respiratory monitoring through loom weaving of metallic yarns,” Advanced Materials, vol. 28, no. 46, pp. 10267–10274, 2016.
View at: Publisher Site | Google Scholar
D. Jiang, B. Shi, H. Ouyang, Y. Fan, Z. L. Wang, and Z. Li, “Emerging implantable energy harvesters and self-powered implantable medical electronics,” ACS Nano, vol. 14, no. 6, pp. 6436–6448, 2020.
View at: Publisher Site | Google Scholar
X. Huang, D. Wang, Z. Yuan et al., “Biodegradable batteries: a fully biodegradable battery for self-powered transient implants (small 28/2018),” Small, vol. 14, no. 28, Article ID 1870129, 2018.
View at: Publisher Site | Google Scholar
Y. Ma, Q. Zheng, Y. Liu et al., “Self powered one-stop, and multifunctional implantable triboelectric active sensor for real-time biomedical monitoring,” Nano Letters, vol. 16, no. 10, pp. 6042–6051, 2016.
View at: Publisher Site | Google Scholar
Q. Zheng, H. Zhang, B. Shi et al., “In vivo self-powered wireless cardiac monitoring via implantable triboelectric nanogenerator,” ACS Nano, vol. 10, no. 7, pp. 6510–6518, 2016.
View at: Publisher Site | Google Scholar
H. Wu, Y. A. Huang, F. Xu, Y. Q. Duan, and Z. P. Yin, “Energy harvesters for wearable and stretchable electronics: from flexibility to stretchability,” Advanced Materials, vol. 28, no. 45, pp. 9881–9919, 2016.
View at: Publisher Site | Google Scholar
T. Yang, H. Pan, G. Tian et al., “Hierarchically structured PVDF/ZnO core-shell nanofibers for self- powered physiological monitoring electronics,” Nano Energy, vol. 72, Article ID 104706, 2020.
View at: Publisher Site | Google Scholar
N. P. Maria Joseph Raj, N. R. Alluri, V. Vivekananthan, A. Chandrasekhar, G. Khandelwal, and S. J. Kim, “Sustainable yarn type-piezoelectric energy harvester as an ecofriendly, cost effective battery-free breath sensor,” Applied Energy, vol. 228, pp. 1767–1776, 2018.
View at: Publisher Site | Google Scholar
X. Chen, X. Li, J. Shao et al., “Nanogenerators: high-performance piezoelectric nanogenerators with imprinted P(VDF-TrFE)/BaTiO₃ nanocomposite micropillars for self-powered flexible sensors (small 23/2017),” Small, vol. 13, no. 23, Article ID 1604245, 2017.
View at: Publisher Site | Google Scholar
X. Ding, H. Cao, X. Zhang, M. Li, and Y. Liu, “Large scale triboelectric nanogenerator and self-powered flexible sensor for human sleep monitoring,” Sensors, vol. 18, no. 6, p. 1713, 2018.
View at: Publisher Site | Google Scholar
Y. L. Cheng, C. Y. Wang, J. W. Zhong et al., “Electrospun polyetherimide electret nonwoven for bi-functional smart face mask,” Nano Energy, vol. 34, pp. 562–569, 2017.
View at: Publisher Site | Google Scholar
Y. Liang, F. Zhao, Z. H. Cheng et al., “Self powered wearable graphene fiber for information expression,” Nano Energy, vol. 32, pp. 329–335, 2017.
View at: Publisher Site | Google Scholar
A. Rajabi-Abhari, J. N. Kim, J. Lee et al., “Diatom bio- silica and cellulose nanofibril for bio-triboelectric nanogenerators and self-powered breath monitoring masks,” ACS Applied Materials & Interfaces, vol. 13, no. 1, pp. 219–232, 2021.
View at: Publisher Site | Google Scholar
R. Cao, J. N. Wang, S. Y. Zhao et al., “Self-powered nanofiber-based screen print triboelectric sensors for respiratory monitoring,” Nano Research, vol. 11, no. 7, pp. 3771–3779, 2018.
View at: Publisher Site | Google Scholar
G. Chen, Y. Li, M. Bick, and J. Chen, “Smart textiles for electricity generation,” Chemistry Review, vol. 120, no. 8, pp. 3668–3720, 2020.
View at: Publisher Site | Google Scholar
Z. Liu, S. Zhang, Y. M. Jin et al., “Flexible piezoelectric nanogenerator in wearable self-powered active sensor for respiration and healthcare monitoring,” Semiconductor Science and Technology, vol. 32, no. 6, Article ID 064004, 2017.
View at: Google Scholar
M. M. Zhu, M. N. Lou, J. Y. Yu, Z. L. Li, and B. Ding, “Energy autonomous hybrid electronic skin with multi-modal sensing capabilities,” Nano Energy, vol. 78, Article ID 105208, 2020.
View at: Publisher Site | Google Scholar
E. Shahhaidar, B. Padasdao, R. Romine, C. Stickley, and O. Boric-Lubecke, “Piezoelectric and electromagnetic respiratory effort energy harvesters,” in Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3443–3446, Osaka, Japan, 2013.
View at: Google Scholar
E. Shahhaidar, B. Padasdao, R. Romine, C. Stickley, and O. Boric Lubecke, “Electromagnetic respiratory effort harvester: human testing and metabolic cost analysis,” IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 2, pp. 399–405, 2015.
View at: Publisher Site | Google Scholar
P. Vasandani, B. Gattu, J. M. Wu, Z. H. Mao, W. Y. Jia, and M. G. Sun, “Triboelectric nanogenerator using microdome-patterned PDMS as a wearable respiratory energy harvester,” Advanced Materials Technologies, vol. 2, no. 6, Article ID 1700014, 2017.
View at: Publisher Site | Google Scholar

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Copyright © 2022 Hamza Abu Owida et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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