Development Of Wearable Sensors Research Articles

Wearable electro-optical sensor for monitoring of nitrogen dioxide gas in environment

Environmental monitoring for detection of harmful pollutants is a challenge in today’s world. Development of wearable sensors with cost-effectiveness and practicality, while using accessible electronic components can be a solution to this challenge. In the present work ultraviolet light emitting diodes (UV-LEDs) and a Light Dependent Resistor (LDR) pair with carefully designed programmable circuitry is used to detect Nitrogen Dioxide (NO2) in the environment. The sensor is battery operated and rechargeable, making it convenient and eco-friendly. The sensor developed is miniaturized and wearable in nature for the detection of analyte gas in the environment. The sensor is stable and inexpensive as compared to the existing NO2 sensors available, which are electro-chemical, or oxide based. The sensor system is sensitive and selective to its exposure to NO2 gas and has no interference with typical environmental attributes such as humidity and temperature, which is a common challenge in gas detection technologies. The principle of gas detection is absorption based, light falling on the detector absorbed by the test gas present in the environment, thus changing the detector resistance. Absorption-based gas detection is indigenously simple but highly effective technique. This method not only ensures good sensitivity but enhances the selectivity of the electro-optical sensor towards the test gas preventing false triggering in the presence of other interfering gases.

Diaper-based wearable SERS sensing system with a silver nano dual-structure composite hydrogel for the detection of biomarkers and pH in urine

Wearable sensors are essential for continuous monitoring of an individual’s health status to enable personalized health management. Urine as a typical non-invasive biological fluid is rich in biological information. Although diaper-based biosensors have been developed, the development of wearable sensors for multiple biomarkers in urine remains a major challenge to be explored. In this paper, a hydrogel SERS sensor consisting of two metal nanostructures of silver nanowires (Ag NWs) and silver nanocubes (Ag NCs) was developed and adhered to nappies to form a wearable nappy-based SERS sensor for the rapid and sensitive detection of bio markers (creatinine, uric acid and bilirubin) and pH in urine. The hydrogel’s excellent adsorption and water-locking properties greatly simplify the collection of urine samples. At the same time, its encapsulation effect can effectively maintain the freshness of the urine sample and ensure the accuracy of the test. The introduction of silver nano dual-structure improves the antimicrobial properties of the hydrogel and reduces the risk of infection when the sensor comes into contact with the skin. Moreover, the silver nano dual-structure triggers abundant electromagnetic “hot spots” around intersecting nanojunctions and gaps, which greatly improves SERS sensitivity. For the detection of creatinine, uric acid and bilirubin, the LODs were as low as 0.59 μM, 69 nM, 89 nM. Finally, the urine pH was accurately detected using a functionalized Ag NWs-Ag NCs/CCNF/Gel/PVA hydrogel. This wearable diaper sensor is expected to be used for personal health condition monitoring and holds great promise for effective care of infants, the elderly, and those with limited mobility.

Customized flexible platform - starting point for the development of wearable sensor for the direct electrochemical detection of kynurenic acid in biological samples

Kynurenic acid (KA) is an active metabolite of tryptophan with notable biological effects, such as antioxidant, neuroprotective, and anti-inflammatory properties. It often undergoes changes of the concentration in biological fluids in chronic diseases. Thus, detecting KA is of great importance for diagnosing inflammatory and neurodegenerative conditions, monitoring disease progression, and assessing responses to pharmacological treatment.This study aimed to design a tailored, flexible platform for sensitive and direct electrochemical detection of KA in biological fluids. Carbon-based electrodes were custom-printed in the lab using specialized inks and flexible substrates. The working electrodes were further functionalized with graphene oxide and subsequently electrochemically reduced to increase the sensitivity toward the analyte. An optimized differential pulse voltammetry protocol was developed for KA detection. The elaborated platform was firstly characterized and then evaluated regarding the analytical performances. It showed a good limit of detection (3 nM and demonstrated the capability to detect KA across a broad concentration range (0.01–500 μM). Finally, the elaborated flexible platform, was succesfully applied for KA determination in serum and saliva samples, in comparison with an optimized HPLC-UV method.The developed platform is the first example of in-lab printed flexible platform reported in literature so far for KA detection. It is also the first study reported in the literature of detection of KA in raw saliva collected from 10 subjects. The sensitivity towards the target analyte, coupled with the adaptability and portability, showcases the potential of this platform for thus illustrating great potential for further development of wearable sensors and biomedical applications.

(Invited) Tellurene Wearable Sensors

The development of wearable sensors for continually monitoring biomarkers is a promising alternative to the costly tools currently utilized in healthcare. Two-dimensional (2D) materials exhibit high sensitivity to physiology-relevant signals. However, few studies report 2D materials-based wearable sensors, primarily due to the intrinsic limitations of related materials that render them poor performance for sensing. Ongoing efforts in 2D materials also face difficulty scaling up due to restrictions on the synthesis conditions and stability. I will discuss our recent progress in developing tellurene-based wearable sensors with multiple modalities for continuously monitoring vital signs. We show that wearable sensors based on tellurene, an emerging 2D semiconductor with intriguing properties, hold substantial promise for addressing the challenges of implementing 2D materials wearable sensors with high sensitivity and specificity. We aim to leverage our platform to fill the gaps in developing clinically applicable 2D materials-based wearable sensors.

An Explainable Deep Learning Approach for Stress Detection in Wearable Sensor Measurements.

Stress has various impacts on the health of human beings. Recent success in wearable sensor development, combined with advancements in deep learning to automatically detect features from raw data, opens several interesting applications related to detecting emotional states. Being able to accurately detect stress-related emotional arousal in an acute setting can positively impact the imminent health status of humans, i.e., through avoiding dangerous locations in an urban traffic setting. This work proposes an explainable deep learning methodology for the automatic detection of stress in physiological sensor data, recorded through a non-invasive wearable sensor device, the Empatica E4 wristband. We propose a Long-Short Term-Memory (LSTM) network, extended through a Deep Generative Ensemble of conditional GANs (LSTM DGE), to deal with the low data regime of sparsely labeled sensor measurements. As explainability is often a main concern of deep learning models, we leverage Integrated Gradients (IG) to highlight the most essential features used by the model for prediction and to compare the results to state-of-the-art expert-based stress-detection methodologies in terms of precision, recall, and interpretability. The results show that our LSTM DGE outperforms the state-of-the-art algorithm by 3 percentage points in terms of recall, and 7.18 percentage points in terms of precision. More importantly, through the use of Integrated Gradients as a layer of explainability, we show that there is a strong overlap between model-derived stress features for electrodermal activity and existing literature, which current state-of-the-art stress detection systems in medical research and psychology are based on.

Flexible electrochemical sensor based on a ZnO-doped SnO2 heterojunction for simultaneous electrochemical determination of p-aminophenol and acetaminophen

In this work, a novel flexible electrochemical sensor was successfully developed for the simultaneous determination of p-aminophenol and acetaminophen. The sensor is composed of a methanesulfonic acid-treated poly(3,4-ethylenedioxythiophene)/poly(styrenesulfonic acid) film on a poly(ethylene terephthalate) substrate, which has been modified using a zinc oxide-doped tin oxide heterojunction applied through drop-coating (C-ZnO/SnO2-PEDOT/PET). The oxygen vacancies resulting from zinc oxide-doped tin oxide heterojunctions within highly conductive metal oxide nanomaterials enhance mass transfer and synergize seamlessly with methanesulfonic acid-treated electrically conductive dielectric PEDOT/PET films, leading to a substantial improvement in electrochemical activity. Under the optimal conditions, a wider linear range and lower detection limits were shown for p-aminophenol (2–511 μM, LOD=0.437 μM) and acetaminophen (2–591 μM, LOD=0.562 μM). In addition, the sensors demonstrated high accuracy in detecting p-aminophenol and acetaminophen in real samples, with recoveries of 99.36 %-99.60 % and 97.56 %-99.80 %, respectively. Furthermore, the flexible sensors maintained a superior electrochemical response when detecting p-aminophenol and acetaminophen at different bending angles, with relative standard deviations of 0.62 % and 1.68 %, respectively. These findings suggest potential applications for the development of wearable sensors in the future.

Advancements in Polymer-Assisted Layer-by-Layer Fabrication of Wearable Sensors for Health Monitoring.

Recent advancements in polymer-assisted layer-by-layer (LbL) fabrication have revolutionized the development of wearable sensors for health monitoring. LbL self-assembly has emerged as a powerful and versatile technique for creating conformal, flexible, and multi-functional films on various substrates, making it particularly suitable for fabricating wearable sensors. The incorporation of polymers, both natural and synthetic, has played a crucial role in enhancing the performance, stability, and biocompatibility of these sensors. This review provides a comprehensive overview of the principles of LbL self-assembly, the role of polymers in sensor fabrication, and the various types of LbL-fabricated wearable sensors for physical, chemical, and biological sensing. The applications of these sensors in continuous health monitoring, disease diagnosis, and management are discussed in detail, highlighting their potential to revolutionize personalized healthcare. Despite significant progress, challenges related to long-term stability, biocompatibility, data acquisition, and large-scale manufacturing are still to be addressed, providing insights into future research directions. With continued advancements in polymer-assisted LbL fabrication and related fields, wearable sensors are poised to improve the quality of life for individuals worldwide.

Advanced Multiparallel-Connected Piezoresistive Physical Sensors: Elevating Performance Reliability of Flexible Strain and Pressure Sensors.

A physical sensor with a sensing medium comprising multiparallel-connected (MPC) piezoresistive pathways in both the vertical and horizontal directions was developed to achieve improved sensing performance. The MPC sensing medium reduces the total resistance and offsets noise, offering enhanced signal stability and device reliability and providing a high-performance sensing platform. The signal change and gauge factor (GF) of the 3PW-5L strain sensor (comprising three lines and five layers of piezoresistive pathways horizontally and vertically, respectively) were, respectively, 5.9 and 4.7 times higher than those of the 1PW-1L sensor composed of a monosensing pathway; the hysteresis of the detected signal was also significantly reduced. The linearity of the detected signal increased from 0.912 for 1PW-1L to 0.995 for 3PW-5L, indicating a greater sensing reliability. The direction of the applied tensile strain was successfully detected using the MPC sensing medium with an orthogonal configuration. The MPC piezoresistive sensor composing vertically stacked piezoresistive pathways demonstrated excellent performance as a pressure sensor; the 3PW-5L pressure sensor afforded a GF of 0.121 ± 0.002 kPa-1 with a linearity of 0.998 under an applied pressure ≥16.4 kPa. The MPC piezoresistive physical sensor offers a superior sensing performance and should contribute to the future development of wearable sensors and electronic devices.

Recent advances in smart wearable sensors for continuous human health monitoring

In recent years, the biochemical and biological research areas have shown great interest in a smart wearable sensor because of its increasing prevalence and high potential to monitor human health in a non-invasive manner by continuous screening of biomarkers dispersed throughout the biological analytes, as well as real-time diagnostic tools and time-sensitive information compared to conventional hospital-centered system. These smart wearable sensors offer an innovative option for evaluating and investigating human health by incorporating a portion of recent advances in technology and engineering that can enhance real-time point-of-care-testing capabilities. Smart wearable sensors have emerged progressively with a mixture of multiplexed biosensing, microfluidic sampling, and data acquisition systems incorporated with flexible substrate and bodily attachments for enhanced wearability, portability, and reliability. There is a good chance that smart wearable sensors will be relevant to the early detection and diagnosis of disease management and control. Therefore, pioneering smart wearable sensors into reality seems extremely promising despite possible challenges in this cutting-edge technology for a better future in the healthcare domain. This review presents critical viewpoints on recent developments in wearable sensors in the upcoming smart digital health monitoring in real-time scenarios. In addition, there have been proactive discussions in recent years on materials selection, design optimization, efficient fabrication tools, and data processing units, as well as their continuous monitoring and tracking strategy with system-level integration such as internet-of-things, cyber-physical systems, and machine learning algorithms.

Otago Exercises Monitoring for Older Adults by a Single IMU and Hierarchical Machine Learning Models.

Otago Exercise Program (OEP) is a rehabilitation program for older adults to improve frailty, sarcopenia, and balance. Accurate monitoring of patient involvement in OEP is challenging, as self-reports (diaries) are often unreliable. The development of wearable sensors and their use in Human Activity Recognition (HAR) systems has lead to a revolution in healthcare. However, the use of such HAR systems for OEP still shows limited performance. The objective of this study is to build an unobtrusive and accurate system to monitor OEP for older adults. Data was collected from 18 older adults wearing a single waist-mounted Inertial Measurement Unit (IMU). Two datasets were recorded, one in a laboratory setting, and one at the homes of the patients. A hierarchical system is proposed with two stages: 1) using a deep learning model to recognize whether the patients are performing OEP or activities of daily life (ADLs) using a 10-minute sliding window; 2) based on stage 1, using a 6-second sliding window to recognize the OEP sub-classes. Results showed that in stage 1, OEP could be recognized with window-wise f1-scores over 0.95 and Intersection-over-Union (IoU) f1-scores over 0.85 for both datasets. In stage 2, for the home scenario, four activities could be recognized with f1-scores over 0.8: ankle plantarflexors, abdominal muscles, knee bends, and sit-to-stand. These results showed the potential of monitoring the compliance of OEP using a single IMU in daily life. Also, some OEP sub-classes are possible to be recognized for further analysis.

The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review

Disease diagnosis and monitoring using conventional healthcare services is typically expensive and has limited accuracy. Wearable health technology based on flexible electronics has gained tremendous attention in recent years for monitoring patient health owing to attractive features, such as lower medical costs, quick access to patient health data, ability to operate and transmit data in harsh environments, storage at room temperature, non-invasive implementation, mass scaling, etc. This technology provides an opportunity for disease pre-diagnosis and immediate therapy. Wearable sensors have opened a new area of personalized health monitoring by accurately measuring physical states and biochemical signals. Despite the progress to date in the development of wearable sensors, there are still several limitations in the accuracy of the data collected, precise disease diagnosis, and early treatment. This necessitates advances in applied materials and structures and using artificial intelligence (AI)-enabled wearable sensors to extract target signals for accurate clinical decision-making and efficient medical care. In this paper, we review two significant aspects of smart wearable sensors. First, we offer an overview of the most recent progress in improving wearable sensor performance for physical, chemical, and biosensors, focusing on materials, structural configurations, and transduction mechanisms. Next, we review the use of AI technology in combination with wearable technology for big data processing, self-learning, power-efficiency, real-time data acquisition and processing, and personalized health for an intelligent sensing platform. Finally, we present the challenges and future opportunities associated with smart wearable sensors.

Intelligent Estimation of Exercise Induced Energy Expenditure Including Excess Post-Exercise Oxygen Consumption (EPOC) with Different Exercise Intensity.

The limited availability of calorimetry systems for estimating human energy expenditure (EE) while conducting exercise has prompted the development of wearable sensors utilizing readily accessible methods. We designed an energy expenditure estimation method which considers the energy consumed during the exercise, as well as the excess post-exercise oxygen consumption (EPOC) using machine learning algorithms. Thirty-two healthy adults (mean age = 28.2 years; 11 females) participated in 20 min of aerobic exercise sessions (low intensity = 40% of maximal oxygen uptake [VO2 max], high intensity = 70% of VO2 max). The physical characteristics, exercise intensity, and the heart rate data monitored from the beginning of the exercise sessions to where the participants' metabolic rate returned to an idle state were used in the EE estimation models. Our proposed estimation shows up to 0.976 correlation between estimated energy expenditure and ground truth (root mean square error: 0.624 kcal/min). In conclusion, our study introduces a highly accurate method for estimating human energy expenditure during exercise using wearable sensors and machine learning. The achieved correlation up to 0.976 with ground truth values underscores its potential for widespread use in fitness, healthcare, and sports performance monitoring.

A review on intelligent aid diagnosis for dysphagia using swallowing sounds

Abstract Dysphagia, a widespread clinical condition in the elderly, can lead to malnutrition, aspiration pneumonia, and even death. Swallowing sounds emanate from vibrations that occur during the contraction of muscles in the mouth, pharynx, and laryngeal; the opening or closure of the glottis and esophageal sphincter; or the movement of food particles through the throat during swallowing. The development of wearable sensors, data science, and machine learning has spurred growing attention to the clinical method of monitoring swallowing sounds for accurate dysphagia diagnosis. This review delves into the acoustic theory foundation and the application of swallowing sound signal analysis methods, elucidating their potential clinical value for dysphagia diagnosis and treatment.

Ionic conductive amylopectin hydrogels for biocompatible and anti-freezing wearable sensors

Conductive hydrogels with excellent flexibility, durability and functional diversity have great development prospects in the field of flexible strain sensors. However, most hydrogels inevitably lose their properties (such as elasticity, electrical conductivity, fatigue resistance, etc.) when frozen and crystallized in low temperature environments, severely hindering their application in extreme environments. Hence, the hydrogel was successfully prepared by introducing amylopectin, polyvinyl alcohol, lithium chloride and glycerol, which possessed the ability of freezing resistance and water retention. The interaction between amylopectin and polyvinyl alcohol formed hydrogen bonds, which endowed the hydrogel excellent tensile strain (608%) and stress (581 kPa). In addition, the hydrogel possessed significant water retention and excellent freeze resistance due to the introduction of glycerol and lithium chloride. Simultaneously, the PGLA hydrogel was placed into mice for 14 days without redness, swelling, allergy and other reactions, indicating the hydrogel with biocompatibility. More importantly, the PGLA hydrogel could detect not only the large strain caused via joint campaign but also the small strain exhibited by weak mastication. This research provides a new way to the development of wearable sensors with the ability to operate in low-temperature environments and biocompatibility.

LAUREATE

The latest developments in wearable sensors have resulted in a wide range of devices available to consumers, allowing users to monitor and improve their physical activity, sleep patterns, cognitive load, and stress levels. However, the lack of out-of-the-lab labelled data hinders the development of advanced machine learning models for predicting affective states. Furthermore, to the best of our knowledge, there are no publicly available datasets in the area of Human Memory Augmentation. This paper presents a dataset we collected during a 13-week study in a university setting. The dataset, named LAUREATE, contains the physiological data of 42 students during 26 classes (including exams), daily self-reports asking the students about their lifestyle habits (e.g. studying hours, physical activity, and sleep quality) and their performance across multiple examinations. In addition to the raw data, we provide expert features from the physiological data, and baseline machine learning models for estimating self-reported affect, models for recognising classes vs breaks, and models for user identification. Besides the use cases presented in this paper, among which Human Memory Augmentation, the dataset represents a rich resource for the UbiComp community in various domains, including affect recognition, behaviour modelling, user privacy, and activity and context recognition.

SignRing

Sign language is a natural language widely used by Deaf and hard of hearing (DHH) individuals. Advanced wearables are developed to recognize sign language automatically. However, they are limited by the lack of labeled data, which leads to a small vocabulary and unsatisfactory performance even though laborious efforts are put into data collection. Here we propose SignRing, an IMU-based system that breaks through the traditional data augmentation method, makes use of online videos to generate the virtual IMU (v-IMU) data, and pushes the boundary of wearable-based systems by reaching the vocabulary size of 934 with sentences up to 16 glosses. The v-IMU data is generated by reconstructing 3D hand movements from two-view videos and calculating 3-axis acceleration data, by which we are able to achieve a word error rate (WER) of 6.3% with a mix of half v-IMU and half IMU training data (2339 samples for each), and a WER of 14.7% with 100% v-IMU training data (6048 samples), compared with the baseline performance of the 8.3% WER (trained with 2339 samples of IMU data). We have conducted comparisons between v-IMU and IMU data to demonstrate the reliability and generalizability of the v-IMU data. This interdisciplinary work covers various areas such as wearable sensor development, computer vision techniques, deep learning, and linguistics, which can provide valuable insights to researchers with similar research objectives.

Fully degradable chitosan–based triboelectric nanogenerators applying in disposable medical products for information transfer

Chitosan (CS) is a natural material with remarkable biocompatibility, biodegradability, and electron-donating ability, making it an excellent candidate for use in triboelectric nanogenerators (TENGs) for the development of wearable sensors. The output performance of CS has been improved by modifications with fillers or functional groups. However, it is still difficult to realize excellent power generation and desired degradation simultaneously. Herein, we design a CS friction layer by plasticizer addition and a template method to obtain a high–performance and fully degradable TENG–based sensor for effective information transmission. Because of the microstructure design of the friction film and glycerol plasticizer addition, the CS–glycerol friction layer has a high contact area in the friction process. Therefore, this CS–glycerol TENG achieves a higher output performance with an open–circuit voltage of 127 V, 2.32 times higher than that of a CS–based TENG fabricated without a template method. Finally, the CS–glycerol TENG sensor is used in an alphabetic code transfer application and can be completely degraded in the soil within 69 days after completing its working cycle. The template and plasticizer strategy effectively improves the CS based TENG output performance and maintains the complete degradation. Furthermore, because of the extensibility, positive cooling ability, and antibacterial activity, the degradable CS–glycerol TENG sensor has great potential for application in multifunctional wearable sensors, especially for disposable medical products.

Recent advances in wearable sensors and data analytics for continuous monitoring and analysis of biomarkers and symptoms related to COVID-19.

The COVID-19 pandemic has changed the lives of many people around the world. Based on the available data and published reports, most people diagnosed with COVID-19 exhibit no or mild symptoms and could be discharged home for self-isolation. Considering that a substantial portion of them will progress to a severe disease requiring hospitalization and medical management, including respiratory and circulatory support in the form of supplemental oxygen therapy, mechanical ventilation, vasopressors, etc. The continuous monitoring of patient conditions at home for patients with COVID-19 will allow early determination of disease severity and medical intervention to reduce morbidity and mortality. In addition, this will allow early and safe hospital discharge and free hospital beds for patients who are in need of admission. In this review, we focus on the recent developments in next-generation wearable sensors capable of continuous monitoring of disease symptoms, particularly those associated with COVID-19. These include wearable non/minimally invasive biophysical (temperature, respiratory rate, oxygen saturation, heart rate, and heart rate variability) and biochemical (cytokines, cortisol, and electrolytes) sensors, sensor data analytics, and machine learning-enabled early detection and medical intervention techniques. Together, we aim to inspire the future development of wearable sensors integrated with data analytics, which serve as a foundation for disease diagnostics, health monitoring and predictions, and medical interventions.

Crack-Based Sensor with Microstructures for Strain and Pressure Sensing

Recent extensive research on flexible electronics has led to the development of various flexible sensors. In particular, sensors inspired by the slit organs of a spider, which utilize cracks in a metal film to measure strain, have garnered considerable interest. This method exhibited significantly high sensitivity, repeatability, and durability in measuring strain. In this study, a thin-film crack sensor was developed using a microstructure. The results exhibited its ability to simultaneously measure the tensile force and pressure in a thin film, further expanding its applications. Furthermore, the strain and pressure characteristics of the sensor were measured and analyzed using an FEM simulation. The proposed method is expected to contribute to the future development of wearable sensors and artificial electronic skin research.

Resistive Flexible Pressure Sensors and Their Applications

Resistive flexible sensors are widely used in many fields. Especially now, resistive flexible sensors are making great contributions to the development of wearable sensors and artificial intelligence. Therefore, the current situation in this article studies the resistive flexible sensor. This article will first introduce the background of the research, and then elaborate on sensing mechanism, the sensing improvement strategy, materials of dielectric composite, and had a great discussion in detail. The research significance of this article is to provide ideas and some summaries of the current situation for future research accuracy. Therefore, the research in this paper will be of great value to the application and research of wearable and more accurate treatment and diagnosis of future patients with the help of resistive flexible sensors. It is worth mentioning that the working principle and The sensitivity improvement strategy of this kind of sensor are introduced in detail in the following paper, and the physical and chemical properties of the raw materials of the flexible sensor are well analyzed, and then in-depth discussion is carried out. It provides some feasible ideas for improving performance.