Wearable Sensor Devices Research Articles

Wearable Sensing Device Integrated with Prestored Reagents for Cortisol Detection in Sweat.

Wearable sweat sensors have achieved rapid development since they hold great potential in personalized health monitoring. However, a typical difficulty in practical processes is the control of working conditions for biorecognition elements, e.g., pH level and ionic strength in sweat may decrease the affinity between analytes and recognition elements. Here, we developed a wearable sensing device for cortisol detection in sweat using an aptamer as the recognition element. The device integrated functions of sweat collection, reagent prestorage, and signal conversion. Especially, the components of prestored reagents were optimized according to the inherent characteristics of sweat samples and electrodes, which allowed us to keep optimal conditions for aptamers. The sweat samples were transferred from the inlet of the device to the reagent prestored chamber, and the dry preserved reagents were rehydrated with sweat and then arrived at the aptamer-modified electrodes. Sweat samples of volunteers were analyzed by the wearable sensing device, and the results showed a good correlation with those of the ELISA kit. We believe that this convenient and reliable wearable sensing device has significant potential in self-health monitoring.

Evaluation of One-Point Calibration for Cuffless BP Wearable Sensor Devices: Stiffness Index

Evaluation of One-Point Calibration for Cuffless BP Wearable Sensor Devices: Stiffness Index

Continuous Monitoring of CRP, IL-6, and Calprotectin in Inflammatory Bowel Disease Using a Perspiration-Based Wearable Device.

Wearable sensor devices represent a noninvasive technology to continuously track biomarkers linked to inflammatory bowel disease (IBD). We assessed the inflammatory markers associated with IBD in human perspiration. Participants with IBD were monitored for 40 to 130 minutes with a proprietary wearable sensor device used to measure C-reactive protein, interleukin-6, and calprotectin. Sensor response using electrochemical impedance spectroscopy and serum samples were measured on the same day. The Mann-Whitney test was used to analyze the relationship between active and remission IBD in serum and perspiration, classified according to endoscopic reports and serum biomarker levels. Asynchronously collected fecal calprotectin from a subset of the population was similarly analyzed. A total of 33 subjects were enrolled. Expression of calprotectin was significantly elevated in the active cohort compared with the remission cohort in perspiration (P < .05; median = 906.69ng/mL; active 95% confidence interval [CI], 466.0-1833ng/mL; remission 95% CI, 328.4-950.8ng/mL), serum (median = 1860.82ng/mL; active 95% CI, 1705-2985ng/mL; remission 95% CI, 870.2-1786ng/mL), and stool (P < .05; median = 126.74 µg/g; active 95% CI, 77.08-347.1 µg/g; remission 95% CI, 5.038-190.4 µg/g). Expression of CRP in perspiration and serum was comparable between the active and remission cohorts (perspiration: P > .05; median = 970.83 pg/mL; active 95% CI, 908.7-992 pg/mL; remission 95% CI, 903.3-991.9 pg/mL; serum: median = 2.34 µg/mL; active 95% CI, 1.267-4.492 µg/mL; remission 95% CI, 1.648-4.287 µg/mL). Expression of interleukin-6 in perspiration was nonsignificant in the active cohort compared with the remission cohort and was significantly elevated in serum (perspiration: P < .05; median = 2.13 pg/mL; active 95% CI, 2.124-2.44 pg/mL; remission 95% CI, 1.661-2.451 pg/mL; serum: median = 1.15 pg/mL; active 95% CI, 1.549-3.964 pg/mL; remission 95% CI, 0.4301-1.257 pg/mL). Analysis of the linear relationship between perspiration and serum calprotectin (R2 = 0.7195), C-reactive protein (R2 = 0.615), and interleukin-6 (R2 = 0.5411) demonstrated a strong to moderate relationship across mediums. We demonstrate the clinical utility of perspiration as a noninvasive medium for continuous measurement of inflammatory markers in IBD and find that the measures correlate with serum and stool markers across a range of disease activity.

Anti-freezing hydrogel regulated by ice-structuring proteins/cellulose nanofibers system as flexible sensor for winter sports

Conductive hydrogels are widely used as sensors in wearable devices. However, hydrogels cannot endure harsh low-temperature environments. Herein, a new regulatory system based on natural ice-structuring proteins (ISPs) and cellulose nanofibers (CNFs) is introduced into hydrogel network consisting of chemically crosslinked network of copolymerized acrylamide and 2-acrylamide-2-methylpropanesulfonic acid, and physically crosslinked polyvinyl alcohol chains, affording an anti-freezing hydrogel with high conductivity (2.63 S/m). These hydrogels show excellent adhesion behavior to various matrices (including aluminum, glass, pigskin, and plastic). Their mechanical properties are significantly improved with the increase in CNF content (tensile strength of 106.4 kPa, elastic modulus of 133.8 kPa). In addition, ISPs inhibit the growth of ice. This endows the hydrogels with anti-freezing property and allows them to maintain satisfactory mechanical properties, conductivity and sensing properties below zero degrees. Moreover, this hydrogel shows high sensitivity to tensile and compressive deformation (GF = 5.07 at 600–800 % strain). Therefore, it can be utilized to develop strain-type pressure sensors that can be attached directly to human skin for detecting various body motions accurately, reliably, and stably. This study proposes a simple strategy to improve the anti-freezing property of hydrogels, which provides new insights for developing flexible hydrogel electronic devices for application in winter sports.

Influencing mechanism on sensitivity of carbonized fabric strain sensors and modification of bridge-connection model: Comparison of three silk woven structures

Given the great potential of carbonized fabric strain sensors in wearable devices, carbonized silk fabrics (CSF) with three characteristic woven structures including georgette (CSF-g), crepe de chine (CSF-c) and crepe satin plain (CSF-p) were employed to investigate the dependence of gauge factor (GF) on two critical parameters: initial resistance (R0) and resistance change (ΔR). The results showed that increasing carbonization temperature can effectively reduce R0 and increase GF of CSF-g, but has little effect on CSF-c and CSF-p. It was further found that the GF of both CSF-c and CSF-p were dominated by ΔR related to the crack propagation path, i.e. high sensitivity CSF was typically accompanied by dense “islands” and fine gaps, while low sensitivity CSF was accompanied by wide cracks and sparse “islands”. Accordingly, a modified bridge connection model was proposed to elucidate the relationship between crack morphology and resistance variation. This model successfully achieved continuous exponential fitting of resistance with admirable fitting goodness. Finally, the prepared sensor exhibited an ultra-high GF (916.5) and was well applied in health monitoring and thermal treatment. These efforts put forward guidance to the design and fabrication of ultrasensitive CSF strain sensors.

Application of wearable devices based on artificial intelligence sensors in sports human health monitoring

With the orderly development of society and economy, people pay more and more attention to sports. How to effectively protect the safety of athletes has become more and more important. With the increasing awareness of health and the popularity of physical exercise, this study aims to explore the application of artificial intelligence-based sensor wearable devices in the monitoring of human health in sports and evaluate their effects on athletes. The research collected relevant literature, analyzed the application of artificial intelligence technology in wearable sensor devices, and designed a sports human health monitoring system according to the needs of athletes. Through experimental tests, it is found that the artificial intelligence-based sensor wearable device can accurately monitor the physiological parameters of athletes, and provide feedback and suggestions in time to help athletes achieve more effective training and health management, improve their training effect and competitive level.

High Strength and Toughness Polymeric Triboelectric Materials Enabled by Dense Crystal-Domain Cross-Linking.

Lightweight, easily processed, and durable polymeric materials play a crucial role in wearable sensor devices. However, achieving simultaneously high strength and toughness remains a challenge. This study addresses this by utilizing an ion-specific effect to control crystalline domains, enabling the fabrication of a polymeric triboelectric material with tunable mechanical properties. The dense crystal-domain cross-linking enhances energy dissipation, resulting in a material boasting both high tensile strength (58.0 MPa) and toughness (198.8 MJ m-3), alongside a remarkable 416.7% fracture elongation and 545.0 MPa modulus. Leveraging these properties, the material is successfully integrated into wearable self-powered devices, enabling real-time feedback on human joint movement. This work presents a valuable strategy for overcoming the strength-toughness trade-off in polymeric materials, paving the way for their enhanced applicability and broader use in diverse sensing applications.

Application of video behavior fast detection based on wearable motion sensor devices in sports training

The sensors can collect the athletes' movement data in real time, providing more accurate and objective feedback for the training process. This study aims to explore the application of video behavior rapid detection based on sports wearable sensor devices in sports training. By integrating sensor data and video information, more comprehensive and accurate behavioral analysis is provided to help coaches better understand athlete performance and progress. The study uses an approach based on motion wearable sensor devices and video technology, in which athletes wear sensor devices and exercise during training. Sensors automatically record movement data, and cameras record video of the training process. Then, computer vision algorithms and machine learning techniques are used, combined with sensor data and video information, for rapid behavior detection. The experimental results show that the video behavior detection based on sports wearable sensor devices has high accuracy and efficiency in sports training. Through the fusion of sensor data and video information, the movement, posture and technical details of the athlete can be analyzed in real time, and accurate evaluation results can be obtained.

Wearable analytical platform for colorimetric detection of sweat lactate using spherical colloidal photonic crystal hydrogel

The construction of reliable and sensitive wearable sensors capable of monitoring specific targets in sweat remains a challenge highly relevant to a range of sensor/diagnostic applications. Herein, we develop a wearable microfluidic platform to monitor sweat lactate, in which spherical colloidal Photonic Crystal Hydrogel (PCH) is synthesized to construct the sensing part. Thanks to the structural color of PCH, lactate concentrations can be obtained via the eye-recognizable and low angle-dependent color variation. Accurate quantitative analysis is performed on a smart phone with Matlab code by integrating the linear relationship between hue value of sample photos and lactate concentrations. The developed sweat sensor presents good selectivity and long-term stability, and it is capable of detecting lactate accurately in human sweat within the physiological range. On-body measurement performed on volunteers successfully demonstrates the applicability of the developed device. This wearable analytical platform provides a promising tool for sweat lactate detection in sports science and medical diagnosis. And it is expected to contribute to developing user-friendly wearable biochemical sensor devices.

Prediction and simulation of wearable sensor devices for sports injury prevention based on BP neural network

In the research of sports injury prevention, the recognition of sports action plays an important role in the action recognition model and the prediction evaluation model. In view of the above problems, this paper constructs a new mathematical model through the idea of BP neural network. The model combines wearable technology and can improve the recognition accuracy of sports actions. The model uses the BP network classifier, which can be used in data. Processes such as feature extraction improve efficiency and reliability. In this paper, the algorithm simulation is carried out, and the experiments are carried out for three movements: running, running and static. The results show that for the recognition of running and running action, when the hidden layer node is 11, the BP neural network classifier shows the best recognition effect. For static motion, the recognition effect of each classifier is basically the same. This paper analyzes the wearable sports action recognition system, including perception layer, application layer and service layer, to realize the recognition and classification of sports actions, predict actions in advance and prevent sports injuries. Finally, this paper analyzes the causes of sports injury, puts forward specific measures to prevent sports injury, and further reduces sports injury events through wearable devices.

Machine learning-based stress classification system using wearable sensor devices

&lt;p&gt;University students often become victims of high-stress levels due to the highly competitive work environment. Unmonitored stress levels in students can inflict severe physiological health problems. This work aims to build a stress classification framework using wearable sensor devices to predict mental stress levels for undergraduate engineering students. It comprises a study to collect a data set of 23 university students using wearable devices for four physiological signals, i.e., electroencephalogram (EEG), electrodermal activity (EDA), skin temperature (SKT), and heart rate (HR), when the students perform the montreal imaging stress task (MIST) for the mental workload. The machine learning models proposed in this work help classify stress into three levels: rest, moderate, and high. The models achieve a classification accuracy of 99.98% using the EEG signals’ time-frequency domain features and an accuracy of 99.51% using the EDA, HR, and SKT signals. The proposed models achieve better scores than all the previous studies on stress classification, using EEG signals and EDA, HR, and SKT signals. This study is novel since it also demonstrates the applicability and proficiency of wearable sensor devices in developing accurate stress classification models to help build real-time stress monitoring systems.&lt;/p&gt;

Research on Speech Communication Enhancement of English Web-based Learning Platform based on Human-computer Intelligent Interaction

This study presents a novel web-based learning platform that leverages human-computer intelligent interaction to enhance English communication skills. The platform integrates cutting-edge technologies to create an immersive learning experience, combining natural language processing, speech recognition, and interactive exercises. Learners engage in real-time conversations with virtual tutors, receive personalized feedback, and access a vast repository of educational resources. The platform not only facilitates language acquisition but also encourages self-paced learning, making it a valuable tool for both educators and students. By harnessing the power of artificial intelligence, this web-based platform represents a significant advancement in the realm of English language education. To overcome these issues this paper proposed SVM with an improved satin Bower bird optimization algorithm (SVM-ISBBO). SVM-ISBBO uses fog computing services that minimize the latency and speeds up the process, effectively handling huge wearable devices. In this proposed work SVM-ISBBO monitors the students communication, vocal parameters, blood pressure, etc, and these values are obtained from wearable sensor devices and their notifications are sent back to teachers. Teachers diagnosed the student information and sent back the alert notifications to the students for taking proper medications. All this information is stored in fog-based cloud storage in a secure manner. The accuracy rate of KNN got 78.56%, NB got 81.74%, SVM got 85.15% and the proposed work of SVM-ISBBO got 92.34%.

Application of motion capture technology based on wearable motion sensor devices in dance body motion recognition

In recent years, people have been committed to studying how to recognize human behavior through the use of wearable devices and understand the intentions expressed by human actions, which has also attracted much attention in human-computer interaction. At present, research on dance mainly relies on computer simulation, while wearable devices can more realistically reflect the details and dynamic processes of dance movements. In order to realize the recognition of dancing human movements, this paper proposes a system that combines Motion capture technology and wearable devices, and uses wearable devices for recognition. Before the experiment began, we established a process for collecting and preprocessing dance action data. On the basis of in-depth research and analysis of Motion capture technology, we have successfully constructed a human Motion capture data-driven system, which can obtain the data needed by the human body to dance through wearable devices. After experimental verification, the system designed in this article performs excellently in recognizing human movements in dance. The recognition results not only contribute to dance training and correcting errors, but also gradually become an important way for dance learning, providing new ideas for dance learning and inheritance.

Security and Privacy of Wearable Wireless Sensors in Healthcare: A Systematic Review

Wearable Wireless Sensor Network (WWSN) devices are widely used in healthcare to monitor health data. However, when WWSN users transmit their data to healthcare professionals or third parties over wireless connections, they face privacy and security vulnerabilities. This paper aims to identify the unsolved privacy and security challenges in wearable sensor devices in healthcare, especially the aspects overlooked by previous research. The main research question is: What are the unsolved privacy and security challenges in wearable sensor devices in healthcare, and what are their implications for users and healthcare professionals? This systematic review employs specific keywords to search for relevant publications on bibliographic databases, including Google Scholar, Scopus, IEEE Xplore, and Web of Science. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) charts helped in screening and summarising the selected papers. The results highlight the critical areas that can make WWSNs vulnerable to security attacks. The findings examine the security and privacy issues of wearable sensor devices in cloud computing, fog computing, the Internet of Things (IoT) and the like. Many studies examine IoT due to its privacy and security challenges, especially regarding handling extensive data, using public channels, deploying advanced technologies, managing sharing policies alongside the growing number of wireless devices, and protecting data from hackers. These challenges seriously threaten the confidentiality, integrity, and availability of health data transmitted by WWSN users to healthcare professionals or third parties in cloud-based environments and IoT and are exacerbated by limited resources. The significant findings thereby focus on unresolved areas in IoT. This paper aims to safeguard against cyber-attacks on healthcare and increase users' adoption rate of WWSN devices.

CONTINUOUS MONITORING OF CRP, IL-6, AND CALPROTECTIN IN INFLAMMATORY BOWEL DISEASE USING A PERSPIRATION BASED WEARABLE

Abstract BACKGROUND Wearable sensor devices represent a noninvasive technology to continuously track biomarkers linked to inflammatory bowel disease (IBD). We assessed the inflammatory markers associated with IBD in human perspiration. METHODS Participants with IBD were monitored for 40-130 minutes with a proprietary wearable sensor device used to measure C-reactive protein (CRP), interleukin-6 (IL-6), and calprotectin. Sensor response using Electrochemical Impedance Spectroscopy (EIS) and serum samples were measured on the same day. Mann Whitney test was used to analyze the relationship between active and remission IBD in serum and perspiration, classified according to endoscopic reports and serum biomarker levels. Asynchronously collected fecal calprotectin from a subset of the population was similarly analyzed. RESULTS A total of 33 subjects were enrolled. Expression of CRP in perspiration and serum were comparable between Active and Remission cohorts (p&amp;gt;0.05). Median expression of IL-6 in perspiration was elevated in the Active cohort compared to Remission cohort, and significantly elevated in serum (p&amp;lt;0.05). Expression of calprotectin in perspiration, serum, and stool were significantly elevated in the Active cohort compared to Remission cohort (p&amp;lt;0.05). Temporal data for three ulcerative colitis and Crohn’s disease patients provided insight into the basal levels of the markers across different disease states. CONCLUSIONS We demonstrate the clinical utility of perspiration as a noninvasive medium for continuous measurement of inflammatory markers in IBD and find that the measures correlate with serum and stool markers across a range of disease activity. Positive linear relationship between serum and sweat of all markers. Serum and sweat levels of both Calprotectin and CRP demonstrate strong linear relationship between mediums. Serum and sweat levels of IL6 demonstrate moderate linear relationship and high homoscedasticity.

A fully healable, mechanical self-strengthening and antibacterial Poly(thiocarbamate-urethane) elastomer constructed via dual reversible dynamic networks

Crosslinked polyurethane elastomers may irreversibly suffer from some external damage such as chemical corrosion, stress and long-term heat treatment, inducing the formation of the microcracks and reduction of mechanical performance. Herein, a novel, fully healable and mechanical self-strengthening crosslinked poly(thiocarbamate-urethane) elastomers (PTU) are constructed by dual reversible dynamic networks, which are based on the thiol-isocyanate click reaction and 2, 4-diamino-6-hydroxy-pyrimidine-Zn2+ (HPM-Znx) coordination effect. Interestingly, the tensile strength and toughness of PTU-HPM-Zn1/4 reached 24.2 MPa and 46.3 MJ/m3, which are 3.6 and 1.6 times higher than those of PTU-HPM, due to the coordination effect between Zn2+ and pyrimidine that formed a denser cross-linking network. Interestingly, the snipped specimen of PTU-HPM-Zn1/4 are fully healable within 12 h at 70 °C. Moreover, the hot-reprocessed PTU-HPM-Zn1/4 sample were further increased to 25.0 MPa and 51.5 MJ/m3, showing a mechanical self-strengthening phenomenon. These impressive mechanical properties of PTU-HPM-Zn1/4 elastomer are attributed to the hierarchical structure evolution including the microphase separation between crystallized and amorphous regions, and the multi reversible dynamic networks reconstruction of thiocarbamate, coordination and hydrogen bonds in the system. Meanwhile, PTU-HPM-Zn1/4 showed excellent antibacterial property against E. Coli. In order to expand their application as flexible and wearable sensor devices, two PTU-HPM-Zn1/4 based conductive films were prepared, which exhibited fast and stable sensing characteristics.

Highly stretchable, robust, sensitive and wearable strain sensors based on mesh-structured conductive hydrogels

With the rapid development of wearable electronic devices, flexible sensors, as the core components of wearable devices, have received extensive attention. Manufacturing of the strain sensors with excellent mechanical properties, wide working range, and high sensitivity is crucial for the development of wearable devices. Herein, for the first time we proposed a strategy based on mesh-structured conductive hydrogel to sensitively detect the body movement in a large workable strain ranges, and demonstrated the facile fabrication procedure of the sensors by using a direct ink writing 3D printing technique. Low cost conductive hydrogels were prepared by a simple one-pot method of combining dispersant-free carbon nanocolloids with easily synthesized PVA/TA/PAM. The printing of complex mesh-structured hydrogels was optimized with high fidelity. The strain sensor based on the mesh-structured hydrogel exhibited excellent mechanical properties, of which the maximum failure stress of the honeycomb was 1.28 MPa and the maximum failure tensile strain was 704 %, amazing sensitivity (GF = 32.95 for the tensile strain range of 3.5 %-5%, GF = 21.5 for the tensile strain range of 100 %-120 %), wide detection range, good stability and durability (for 500 tensile cycles, the ΔR/R0 value remained unchanged). In addition, the working mechanism during the stretching process of mesh-structured hydrogels was revealed by combining finite element simulation with microscopic morphology observation. At last, the applications of the mesh-structured hydrogel-based sensors to accurately and reliably detect both subtle physiological signals and large joint movements were soundly demonstrated, endowing the ability of achieving all-round detection of human motion. Our work could provide a universe strategy for fabricating highly ventilated, stretchable, robust, sensitive and wearable strain sensors, which would greatly expand the applications of strain sensors in wearable devices.

FEASIBILITY AND VALIDITY OF WEARABLE TECHNOLOGY FOR ASSESSING MOBILITY AMONG HOSPITALIZED OLDER ADULTS

Abstract The need for strategies to promote mobility during acute care hospital stays among older adults is increasingly recognized. Wearable technologies show great potential for this purpose. The objective of this study was to determine the most accurate wearable sensor device and location for capturing in-hospital mobility in older patients. Twenty-five older adults (79.6±8.1 years) admitted to a hospital medical care unit volunteered to test the feasibility and concurrent validity of the ActiGraph wGT3X-BT, Mox1, MetaMotionC and Fitbit Versa. All 25 enrolled patients wore the devices simultaneously on the wrist, hip, and ankle while undertaking a mobility activity protocol consisting of supine lying, sitting, and standing tasks. Participants also performed the Timed Up and Go (TUG) and 10-meter walk test (10MWT) wearing the devices. A trained physiotherapist supervised the performance of the tasks. Most participants (58%) preferred to wear the device on the ankle. Overall, the ActiGraph and Mox1 were the easiest to set up and download. The ActiGraph was the most reliable device, retrieving 100% of the collected data. Regarding body posture, the thigh-worn ActiGraph algorithm accurately classified 78% of sitting and lying postures, as well as 84% of standing postures, indicating high overall accuracy. After implementing the recommended low-frequency filter extension for slower gait speeds, the ankle-worn ActiGraph showed the narrowest limits of agreement and observations closer to zero for step count during the TUG and the 10MWT. These findings lay the foundation for subsequent work to validate wearable devices for monitoring “free-living” mobility in this population.

Emerging dissolving strategy of cellulose nanomaterial for flexible electronics sensors in wearable devices: a review

Emerging dissolving strategy of cellulose nanomaterial for flexible electronics sensors in wearable devices: a review

Porous Conductive Hybrid Composite with Superior Pressure Sensitivity and Dynamic Range

AbstractPorous conductive composites hold immense promise in flexible sensors and soft robotics due to their pressure‐responsive electrical conductivity. Unlike non‐porous composites whose pressure sensitivity is limited by relatively high elastic modulus, porous materials show improved pressure sensitivity owing to their lower stiffness. Despite this, existing porous composites still suffer from insufficient pressure sensitivity or narrow detection ranges, severely restricting their applications. This work presents a liquid metal hybrid filler porous composite to address these issues. Through experiment and simulation optimization, the composite exhibits a conductivity increase of five order‐of‐magnitude over 0–250 kPa, demonstrating a 900% higher pressure sensitivity than the best non‐porous counterpart in this work. The composite maintains a highly linear response (R2 of 0.999) over an exceptionally wide dynamic range up to 8.9 MPa, with a pressure sensitivity of 8.1 MPa–1, surpassing the state‐of‐the‐art in both pressure range and sensitivity. A proof‐of‐concept pressure sensor array further demonstrates the composite's excellent sensing performance, showing stable response under 100‐cycle loading with a measured pressure deviation of only 1.4%, outperforming existing commercial pressure sensors in terms of sensitivity, detection range and cyclic stability. The porous material design strategy opens doors for high sensitivity pressure sensors in wearable devices, flexible electronics, and soft robotics.