Wearable sensors for respiration monitoring: a review

This paper provides an overview of flexible and wearable respiration sensors with emphasis on their significance in healthcare applications. The paper classifies these sensors based on their operating frequency distinguishing between high-frequency sensors, which operate above 10 MHz, and low-frequency sensors, which operate below this level. The operating principles of breathing sensors as well as the materials and fabrication techniques employed in their design are addressed. The existing research highlights the need for robust and flexible materials to enable the development of reliable and comfortable sensors. Finally, the paper presents potential research directions and proposes research challenges in the field of flexible and wearable respiration sensors. By identifying emerging trends and gaps in knowledge, this review can encourage further advancements and innovation in the rapidly evolving domain of flexible and wearable sensors.This work was supported by the Spanish Government (MICINN) under Projects TED2021-131209B-I00 and PID2021-124288OB-I00.Peer ReviewedPostprint (published version

Piezoelectric Materials for Medical Applications

This chapter describes the history and development strategy of piezoelectric materials for medical applications. It covers the piezoelectric properties of materials found inside the human body including blood vessels, skin, and bones as well as how the piezoelectricity innate in those materials aids in disease treatment. It also covers piezoelectric materials and their use in medical implants by explaining how piezoelectric materials can be used as sensors and can emulate natural materials. Finally, the possibility of using piezoelectric materials to design medical equipment and how current models can be improved by further research is explored. This review is intended to provide greater understanding of how important piezoelectricity is to the medical industry by describing the challenges and opportunities regarding its future development

Mechanical energy harvesting and self-powered electronic applications of textile-based piezoelectric nanogenerators: a systematic review

Environmental pollution resulting from fossil fuel consumption and the limited lifespan of batteries has shifted the focus of energy research towards the adoption of green renewable technologies. On the other hand, there is a growing potential for small, wearable, portable electronic devices. Therefore, considering the pollution caused by fossil fuels, the drawbacks of chemical batteries, and the potential applications of small-scale wearables and portable electronic devices, the development of a more effective lightweight power source is essential. In this context, piezoelectric energy harvesting technology has attracted keen attention. Piezoelectric energy harvesting technology is a process that converts mechanical energy into electrical energy and vice-versa. Piezoelectric energy harvesters can be fabricated in various ways, including through solution casting, electrospinning, melt spinning, and solution spinning techniques. Solution and melt-spun filaments can be used to develop woven, knitted, and braided textile-based piezoelectric energy harvesters. The integration of textile-based piezoelectric energy harvesters with conventional textile clothing will be a key enabling technology in realising the next generation smart wearable electronics. This review focuses on the current achievements on textile based piezoelectric nanogenerators (T-PENGs), basic knowledge about piezoelectric materials and the piezoelectric mechanism. Additionally, the basic understanding of textiles, different fabrication methods of T-PENGs, and the strategies to improve the performance of piezoelectric nanogenerators are discussed in the subsequent sections. Finally, the challenges faced in harvesting energy using textile based piezoelectric nanogenerators (T-PENGs) are identified, and a perspective to inspire researchers working in this area is presented

H2B: Heartbeat-based Secret Key Generation Using Piezo Vibration Sensors

We present Heartbeats-2-Bits (H2B), which is a system for securely pairing wearable devices by generating a shared secret key from the skin vibrations caused by heartbeat. This work is motivated by potential power saving opportunity arising from the fact that heartbeat intervals can be detected energy-efficiently using inexpensive and power-efficient piezo sensors, which obviates the need to employ complex heartbeat monitors such as Electrocardiogram or Photoplethysmogram. Indeed, our experiments show that piezo sensors can measure heartbeat intervals on many different body locations including chest, wrist, waist, neck and ankle. Unfortunately, we also discover that the heartbeat interval signal captured by piezo vibration sensors has low Signal-to-Noise Ratio (SNR) because they are not designed as precision heartbeat monitors, which becomes the key challenge for H2B. To overcome this problem, we first apply a quantile function-based quantization method to fully extract the useful entropy from the noisy piezo measurements. We then propose a novel Compressive Sensing-based reconciliation method to correct the high bit mismatch rates between the two independently generated keys caused by low SNR. We prototype H2B using off-the-shelf piezo sensors and evaluate its performance on a dataset collected from different body positions of 23 participants. Our results show that H2B has an overwhelming pairing success rate of 95.6%. We also analyze and demonstrate H2B's robustness against three types of attacks. Finally, our power measurements show that H2B is very power-efficient

Engineering of hybrid materials for self-powered flexible sensors

Department of Energy Engineering (Energy Engineering)Along with the 4th industrial revolution, the great advance in wearable electronics has led a new paradigm in our life. Especially, wearable sensor technology has received great attention as promising candidates to improve the quality of life by realizing the ???Internet of Things??? which can be utilized in daily healthcare, intelligent control, daily activity monitoring, and human-machine interface systems. The ideal wearable devices require several characteristics providing light weight, flexible, unobtrusive, autonomously powered for the convenience of user and sustainable uses. Although various emerging technologies have been suggested to meet these requirements, there are still challenges for highly flexible and unobtrusive forms, multifunctionality, and sustainable uses, which are directly related to widespread practical applications. In response to these requirements, several approaches to explore functional materials and to design the effective structures for advanced sensor performances with sustainable uses, high sensitivity, and multifunctionality. For sustainable uses, self-powered sensing system can be developed by triboelectric/piezoelectric/pyroelectric effect, which can rule out any problems with power sources. For wearable and flexible form factors, textile and extremely thin films, which are mountable and attachable on the human body, are used instead of conventional obtrusive devices, improving the wearing sensing of devices. Moreover, the selection of multifunctional materials and modification of material characteristics can realize multifunctionality which can respond to different stimuli (pressure and temperature) simultaneously. Furthermore, soft/hard and organic/inorganic hybrid materials can be used for effective design of high performance wearable sensor by distribution control in dissimilar materials, which is attributed to effectively localized strain and large contrast of dielectric properties. Therefore, self-powered wearable sensors can be developed with functional materials, unique design and novel approach for characteristic modification, which can provide a promising platform to realize ideal wearable sensors for future applications such as daily healthcare, intelligent control, daily activity monitoring, and human-machine interface systems. In this thesis, we suggest the strategy for advanced sustainable wearable sensors with better wearing sensation, multimodality, and enhanced sensory functions through structure design and modification of material characteristics. Firstly, we briefly summarize the fundamental working principles, the latest research trends, and potential applications in Chapter 1. In Chapter 2, we demonstrate as-spun P(VDF) fiber-based self-powered textile sensors with high sensitivity, mechanical stability, and washing durability. In Chapter 3, we introduce multimodal wearable sensors without signal interference based on triboelectric and pyroelectric effect, which is attributed to controllable polarity of P(VDF-TrFE) via ferroelectric polarization. In Chapter 4, we suggest a novel method for high performance of triboelectric sensors based on alternating P(VDF-TrFE)/BaTiO3 multilayer nanocomposites, which is attributed to the efficient stress concentration and large contrast of dielectric properties. Lastly, we summarize this thesis with future prospects in Chapter 5.clos

The application of impantable sensors in the musculoskeletal system: a review

As the population ages and the incidence of traumatic events rises, there is a growing trend toward the implantation of devices to replace damaged or degenerated tissues in the body. In orthopedic applications, some implants are equipped with sensors to measure internal data and monitor the status of the implant. In recent years, several multi-functional implants have been developed that the clinician can externally control using a smart device. Experts anticipate that these versatile implants could pave the way for the next-generation of technological advancements. This paper provides an introduction to implantable sensors and is structured into three parts. The first section categorizes existing implantable sensors based on their working principles and provides detailed illustrations with examples. The second section introduces the most common materials used in implantable sensors, divided into rigid and flexible materials according to their properties. The third section is the focal point of this article, with implantable orthopedic sensors being classified as joint, spine, or fracture, based on different practical scenarios. The aim of this review is to introduce various implantable orthopedic sensors, compare their different characteristics, and outline the future direction of their development and application

PVDF 필름 센서를 사용한 수면관련 호흡장애 및 수면 단계의 무구속적 모니터링

학위논문 (박사)-- 서울대학교 대학원 : 협동과정 바이오엔지니어링전공, 2015. 8. 박광석.이 연구에서는 PVDF 필름 센서를 사용하여 무구속적으로 수면관련 호흡장애 및 수면 단계를 모니터링 할 수 있는 기법을 개발하였다. PVDF 센서는 4 x 1 배열로 구성되었으며, 센서 시스템의 총 두께는 약 1.1mm 였다. PVDF 센서는 침대보와 매트리스 사이에 위치시켜 참가자의 몸에 직접적인 접촉이 없도록 하였다. 수면무호흡증 검출 연구에는 26명의 수면무호흡증 환자와 6명의 정상인이 참가하였다. PVDF 신호의 표준편차에 근거하여 수면무호흡증 검출 방법을 개발하였고, 추정된 수면무호흡증 검출 결과를 수면 전문의의 판독 결과와 비교하였다. 추정된 결과와 판독 결과 간의 수면 무호흡-저호흡 지수 상관계수는 0.94 (p < 0.001) 이었다. 코골이 검출 연구에는 총 20명의 수면무호흡증 환자가 참가하였다. PVDF 신호를 단시간 푸리에 변환하여 얻은 파워 비율과 최대 주파수를 주파수 영역 특징으로 추출하였다. 추출된 특징들을 서포트 벡터 머신 분류기에 입력하였고, 분류기의 검출 결과에 따라 코골이 또는 코골이가 아닌 구간으로 나누었다. 제안된 방법에서 추정한 코골이 검출 결과를 정상 성인 3명의 청각 및 시각 기반 코골이 판독 결과와 비교하였다. 코골이 검출에 대한 평균 민감도 및 양성예측도는 각각 94.6% 및 97.5% 이었다. 수면 단계 검출 연구에는 11명의 정상인과 13명의 수면무호흡증 환자가 참가하였다. 렘 (REM) 수면 구간은 호흡의 주기와 그 변동률에 근거하여 추정하였다. 깸 구간은 움직임 신호에 근거하여 추정하였다. 깊은 수면 구간은 분당 호흡수의 변화폭에 기반하여 추정하였다. 각 수면 단계 검출 결과를 통합하여 최종 결과를 수면 전문의의 판독 결과와 비교하였다. 30초 단위로 수면 단계를 검출하였을 때, 평균 정확도는 71.3% 이었으며 평균 카파 값은 0.48 이었다. 연구에서 제안된 PVDF 센서와 알고리즘을 통하여 상용화된 수면 모니터링 장치에 비교할 수 있을 만한 수준의 성능을 확보하였다. 이 연구 결과가 가정환경 기반 수면모니터링 시스템의 활용도와 정확도를 높이는데 기여할 수 있을 것으로 기대한다.In this study, unconstrained sleep-related breathing disorders (SRBD) and sleep stages monitoring methods using a polyvinylidene fluoride (PVDF) film sensor were established and tested. Subjects physiological signals were measured in an unconstrained manner using the PVDF sensor during polysomnography (PSG). The sensor was comprised of a 4×1 array, and the total thickness of the system was approximately 1.1 mm. It was designed to be placed under the subjects back and installed between a bed cover and mattress. In the sleep apnea detection study, twenty six sleep apnea patients and six normal subjects participated. The sleep apnea detection method was based on the standard deviation of the PVDF signals, and the methods performance was assessed by comparing the results with a sleep physicians manual scoring. The correlation coefficient for the apnea-hypopnea index (AHI) values between the methods was 0.94 (p < 0.001). For minute-by-minute sleep apnea detection, the method classified sleep apnea with average sensitivity of 72.9%, specificity of 90.6%, accuracy of 85.5%, and kappa statistic of 0.60. In the snoring detection study, twenty patients with obstructive sleep apnea (OSA) participated. The power ratio and peak frequency from the short-time Fourier transform were used to extract spectral features from the PVDF data. A support vector machine (SVM) was applied to the spectral features to classify the data into either snore or non-snore class. The performance of the method was assessed using manual labelling by three human observers. For event-by-event snoring detection, PVDF data that contained snoring (SN), snoring with movement (SM), and normal breathing (NB) epochs were selected for each subject. The results showed that the overall sensitivity and the positive predictive values were 94.6% and 97.5%, respectively, and there was no significant difference between the SN and SM results. In the sleep stages detection study, eleven normal subjects and thirteen OSA patients participated. Rapid eye movement (REM) sleep was estimated based on the average rate and variability of the respiratory signal. Wakefulness was detected based on the body movement signal. Variability of the respiratory rate was chosen as an indicator for slow wave sleep (SWS) detection. The performance of the method was assessed by comparing the results with manual scoring by a sleep physician. In an epoch-by-epoch analysis, the method classified the sleep stages with average accuracy of 71.3% and kappa statistic of 0.48. The experimental results demonstrated that the performances of the proposed sleep stages and SRBD detection methods were comparable to those of ambulatory devices and the results of constrained sensor based studies. The developed system and methods could be applied to a sleep monitoring system in a residential or ambulatory environment.Chapter 1. Introduction 1 1.1. Background 1 1.2. Sleep Apnea 3 1.3. Snoring 5 1.4. Sleep Stages 8 1.5. Polyvinylidene Fluoride Film Sensor 11 1.6. Purpose 12 Chapter 2. Sleep Apnea Detection 13 2.1. Signal Acquisition System 13 2.2. Methods 16 2.2.1. Participants and PSG Data 16 2.2.2. Apneic Events Detection 18 2.2.3. Statistical Analysis 25 2.3. Results 26 2.3.1. AHI Estimation 26 2.3.2. Diagnosing Sleep Apnea 29 2.3.3. Minute-By-Minute Sleep Apnea Detection 30 2.4. Discussion 34 2.4.1. Agreement between Proposed Method and PSG 34 2.4.2. Comparisons with Previous Studies 34 2.4.3. Validation of PVDF Film Sensors 36 2.4.4. Validation of Sleep Apnea Detection Algorithm 37 Chapter 3. Snoring Detection 40 3.1. Methods 40 3.1.1. Participants and PSG Data 40 3.1.2. Feature Extraction for Snoring Event Detection 42 3.1.3. Data Selection and Reference Snoring Labelling 46 3.1.4. Snoring Event Classification Based on the SVM 48 3.2. Results 52 3.2.1. Event-By-Event Snoring Detection 52 3.2.2. Snoring Event Detection and Sleep Posture 56 3.2.3. Epoch-By-Epoch Snoring Detection 57 3.3. Discussion 59 3.3.1. Agreement between Proposed Method and Reference Snoring 59 3.3.2. Comparisons with Previous Studies 59 3.3.3. Validation of the Snoring Detection Algorithm 61 Chapter 4. Sleep Stages Detection 65 4.1. Methods 65 4.1.1. Participants and PSG Data 65 4.1.2. REM Sleep Detection 68 4.1.3. Wakefulness Detection 74 4.1.4. SWS Detection 80 4.2. Results 86 4.2.1. REM Sleep Detection 86 4.2.2. Wakefulness Detection 89 4.2.3. SWS Detection 92 4.2.4. Sleep Macro- and Microstructure Detection 94 4.3. Discussion 99 4.3.1. Agreement between Proposed Method and PSG 99 4.3.2. Comparisons with Previous Studies 99 4.3.3. Validation of Sleep Stages Detection Algorithm 102 Chapter 5. Conclusion 105 References 107 Abstract in Korean 118Docto

Recent development of respiratory rate measurement technologies

Respiratory rate (RR) is an important physiological parameter whose abnormity has been regarded as an important indicator of serious illness. In order to make RR monitoring simple to do, reliable and accurate, many different methods have been proposed for such automatic monitoring. According to the theory of respiratory rate extraction, methods are categorized into three modalities: extracting RR from other physiological signals, RR measurement based on respiratory movements, and RR measurement based on airflow. The merits and limitations of each method are highlighted and discussed. In addition, current works are summarized to suggest key directions for the development of future RR monitoring methodologies

Applications of nanogenerators for biomedical engineering and healthcare systems

The dream of human beings for long living has stimulated the rapid development of biomedical and healthcare equipment. However, conventional biomedical and healthcare devices have shortcomings such as short service life, large equipment size, and high potential safety hazards. Indeed, the power supply for conventional implantable device remains predominantly batteries. The emerging nanogenerators, which harvest micro/nanomechanical energy and thermal energy from human beings and convert into electrical energy, provide an ideal solution for self‐powering of biomedical devices. The combination of nanogenerators and biomedicine has been accelerating the development of self‐powered biomedical equipment. This article first introduces the operating principle of nanogenerators and then reviews the progress of nanogenerators in biomedical applications, including power supply, smart sensing, and effective treatment. Besides, the microbial disinfection and biodegradation performances of nanogenerators have been updated. Next, the protection devices have been discussed such as face mask with air filtering function together with real‐time monitoring of human health from the respiration and heat emission. Besides, the nanogenerator devices have been categorized by the types of mechanical energy from human beings, such as the body movement, tissue and organ activities, energy from chemical reactions, and gravitational potential energy. Eventually, the challenges and future opportunities in the applications of nanogenerators are delivered in the conclusive remarks. The combination of nanogenerator and biomedicine have been accelerating the development of self‐powered biomedical devices, which show a bright future in biomedicine and healthcare such as smart sensing, and therapy