Physiological Signal Monitoring Research Articles

Two-step construction of KPDMS/Al2O3 ultra-barriers for wearable sensors.

Wearable devices hold significant potential in healthcare and medical diagnostics. One major challenge in realizing this potential is the low barrier property of polymer substrates, which fail to withstand surrounding moisture and biofluids. In this work, a two-step strategy involving ALI followed by a UV-curing process is developed to fabricate a K48PDMS/Al2O3 ultra-barrier with high barrier property of 7.82 × 10-5 g m-2 day-1 under stretching strain, representing one of the highest values among current works. Moreover, the K48PDMS/Al2O3 barrier enables Ca-tested devices to exhibit extended operational lifetimes of up to 12 days in simulated rain. It also ensures the high sensitivity of strain sensors for real-time monitoring of health-related physiological signals, even when exposed to aggressive solutions such as PBS, KOH, and glucose. A clear "filling-cross-linking" mechanism is revealed, involving the filling of the void spaces within polymer chains followed by the cross-linking of polymer side chains to enhance the density of the hybrid layer. Adjusting porosity and functional group density ensures complete Al2O3 infiltration into the polymer. The cross-linking increases from 12.31% to 73.79% compared to the UV-curing process alone due to the presence of Al2O3, further enhancing the density of the hybrid layer and its barrier properties. The proposed strategy in our work shows great potential for providing highly reliable encapsulation for wearable electronics.

Highly stretchable nanocomposite piezofibers: a step forward into practical applications in biomedical devices.

High-performance biocompatible composite materials are gaining attention for their potential in various fields such as neural tissue scaffolds, bio-implantable devices, energy harvesting, and biomechanical sensors. However, these devices currently face limitations in miniaturization, finite battery lifetimes, fabrication complexity, and rigidity. Hence, there is an urgent need for smart and self-powering soft devices that are easily deployable under physiological conditions. Herein, we present a straightforward and efficient fabrication technique for creating flexible/stretchable fiber-based piezoelectric structures using a hybrid nanocomposite of polyvinylidene fluoride (PVDF), reduced graphene oxide (rGO), and barium-titanium oxide (BT). These nanocomposite fibers are capable of converting biomechanical stimuli into electrical signals across various structural designs (knit, braid, woven, and coil). It was found that a stretchable configuration with higher output voltage (4 V) and a power density (87 μW cm-3) was obtained using nanocomposite coiled fibers or knitted fibers, which are ideal candidates for real-time monitoring of physiological signals. These structures are being proposed for practical transition to the development of the next generation of fiber-based biomedical devices. The cytotoxicity and cytocompatibility of nanocomposite fibers were tested on human mesenchymal stromal cells. The obtained results suggest that the developed fibers can be utilized for smart scaffolds and bio-implantable devices.

Unobtrusive Multimodal Monitoring of Physiological Signals for Driver State Analysis

Unobtrusive Multimodal Monitoring of Physiological Signals for Driver State Analysis

Integrated Ink Printing Paper Based Self-Powered Electrochemical Multimodal Biosensing (IFP-Multi ) with ChatGPT-Bioelectronic Interface for Personalized Healthcare Management.

Personalized healthcare management is an emerging field that requires the development of environment-friendly, integrated, and electrochemical multimodal devices. In this study, the concept of integrated paper-based biosensors (IFP-Multi ) for personalized healthcare management is introduced. By leveraging ink printing technology and a ChatGPT-bioelectronic interface, these biosensors offer ultrahigh areal-specific capacitance (74633 mF cm-2 ), excellent mechanical properties, and multifunctional sensing and humidity power generation capabilities. Moreimportantly, the IFP-Multi devices have the potential to simulate deaf-mute vocalization and can be integrated into wearable sensors to detect muscle contractions and bending motions. Moreover, they also enable monitoring of physiological signals from various body parts, such as the throat, nape, elbow, wrist, and knee, and successfully record sharp and repeatable signals generated by muscle contractions. In addition, the IFP-Multi devices demonstrate self-powered handwriting sensing and moisture power generation for sweat-sensing applications. As a proof-of-concept, a GPT 3.5 model-based fine-tuning and prediction pipeline that utilizes recorded physiological signals through IFP-Multi is showcased, enabling artificial intelligence with multimodal sensing capabilities for personalized healthcare management. This work presents a promising and ecofriendly approach to developing paper-based electrochemical multimodal devices, paving the way for a new era of healthcare advancements.

Design of flexible wearable sensing systems

The aging population and the increasing prevalence of chronic diseases in the elderly have brought a significant economic burden to families and society. The non-invasive wearable sensing system can continuously and real-time monitor important physiological signs of the human body and evaluate health status. In addition, it can provide efficient and convenient information feedback, thereby reducing the health risks caused by chronic diseases in the elderly. A wearable system for detecting physiological and behavioral signals was developed in this study. We explored the design of flexible wearable sensing technology and its application in sensing systems. The wearable system included smart hats, smart clothes, smart gloves, and smart insoles, achieving long-term continuous monitoring of physiological and motion signals. The performance of the system was verified, and the new sensing system was compared with commercial equipment. The evaluation results demonstrated that the proposed system presented a comparable performance with the existing system. In summary, the proposed flexible sensor system provides an accurate, detachable, expandable, user-friendly and comfortable solution for physiological and motion signal monitoring. It is expected to be used in remote healthcare monitoring and provide personalized information monitoring, disease prediction, and diagnosis for doctors/patients.

Multi-Tap NIR Lock-In CMOS Imager for Non-Contact Physiological Signal Monitoring

Multi-Tap NIR Lock-In CMOS Imager for Non-Contact Physiological Signal Monitoring

Low-cost and portable physiological signal monitor using PhysRate model

In this work, we propose a lightweight non-contact physiological signal monitor. The proposed system is equipped with the modified DeepPhys model and the rate estimation model PhysRate that enable non-contact physiological signal measurements without markers using the low-cost color camera. We introduce the constraint term guided by scene information into the original DeepPhys model to mitigate the overfitting problem of the original DeepPhys model. The results of validation experiments in ICU observation ward show that the modified DeepPhys model can effectively counteract the overfitting problem of the original DeepPhys model. The experimental results of PhysRate indicate that it exhibits certain performance improvements compared to other rate prediction models. In terms of system integration, the proposed physiological signal detection monitor includes Raspberry Pi 4B, RGB camera, 4G module, mobile power, and mobile terminal. With the help of above five modules, the system can analyze the captured signals at the edge and then transmit the processed data to the cloud via 4G network. The edge side is equipped with physiological signal detection model and the other data processing algorithms. On mobile devices, the users can view the relevant data through the developed Mini App. The PhysRate model code is available at https://github.com/ShawnTan86/PhysRate.

Self-powered motion state monitoring system based on combined triboelectric nanogenerators for human physiological signal monitoring and energy collection

The study of self-powered motion state monitoring systems based on triboelectric nanogenerators has recently received increasing attention. In this paper, we propose a self-powered system consisting of two low-cost and simply manufactured triboelectric nanogenerators for human physiological signal monitoring and energy collection. This system can monitor the trunk information and gait information during human activities, and measure human motion status in a holistic manner. The triboelectric nanogenerator, which monitors body torso information (B-TENG), we optimize its triboelectric layer through the microstructure of sandpaper to increase its contact area with the skin. In addition, by adding iron powder into the B-TENG electrode layer, the magnetic permeability of the induction electrode is increased to improve its output performance, and its maximum open-circuit voltage can reach 44.3 V. The triboelectric nanogenerator, which is installed on the foot to monitor gait information (F-TENG), can reach an average open-circuit voltage of 205.6 V by adding a rectangular protrusion structure to the triboelectric layer. In addition, due to its high output performance (∼4700 μw), the F-TENG can collect mechanical energy generated from the soles of the feet during daily human activities and charge a 100 μF capacitor to 1.4 V within 60 s, subsequently powering the miniature electronics. During foot walking, F-TENG is able to light up more than 60 light-emitting diodes. By having the experimenters wear these two triboelectric nanogenerators, we can record the frequency and amplitude signals of the experimenter's elbow, knee, breath, and gait in real time. We also monitored three running states of the human body through these two triboelectric nanogenerators, including normal state, insufficient exercise capacity, and dyspnea. We believe that this work provides a new direction for the development of big data motion analysis and self-powered smart exercise devices.

Robust integration of "top-down" strategy and triple-structure design for nature-skin derived e-skin with superior elasticity and ascendency strain and vibration sensitivity

The rapid advancement of bio-integrated electronics in recent years has been paralleled by increased interest in the research of stretchable, elastic, conductive, and multi-dimensional sensing electronic skin (e-skin) due to its potential integration with electronic devices and soft tissues. In this work, we presented the fabrication of a nature-skin derived e-skin, denoted as S-P/G@PU e-skin, achieved through an on-demand "top-down" strategy and a triple structure design, employing integrated in-situ polymerization, impregnation, and encapsulation techniques. The collagen fibers weaved hierarchical 3D structure of natural skin, was strategically employed to host conductive polypyrrole and combined with a binary solvent system of glycerin and water. Subsequently, this composite natural skin was coated with polyurethane films, resulting in the engineering of the S-P/G@PU e-skin. This innovative e-skin exhibited remarkable properties, including high tensile strength (8.76 MPa), excellent elasticity (0–180 %), anti-freezing capacity, moisture retention, substantial conductivity (6.3 S/m), and outstanding multi-dimensional sensing capabilities. Importantly, the S-P/G@PU e-skin functioned as both a slow adaptive resistance strain sensor and a fast adaptive single-electrode triboelectric nanogenerator, making it a versatile sensing system that enabled real-time monitoring of various physiological signals from the human body and wide-frequency vibration signals from devices such as cellphones and motors. This study serves as a proof of concept for transforming nature-skin into e-skin, showcasing the potential for integrated wearable electronics, artificial intelligence, and human-machine interfaces.

Single-point curved fiber optic pulse sensor for physiological signal prediction based on the genetic algorithm-support vector regression model

For hypertensive patients, a real-time human physiological signal monitoring system helps to track blood pressure status and provides valuable data to clinicians for early diagnosis and timely intervention. Optical fiber pulse sensing has superior conditions such as resistance to electromagnetic interference and richness of measured pulse characteristics. In this study, a monitoring system based on a single-point curved fiber pulse sensor (CFPS) was used to collect the pulse wave signal of a human radial artery. The features of the pulse wave signal were used to estimate the pulse wave transit time (PTT) and Blood Pressure—systolic blood pressure (SBP) and diastolic blood pressure (DBP)—based on support vector regression (SVR) optimized by a genetic algorithm (GA) (GA-SVR model). The results show that the root mean square error (RMSE) of SBP, DBP and PTT were 1.571 mmHg, 3.250 mmHg and 5.719 ms, respectively, and the results met the Advancement of Medical Instrumentation (AAMI) criteria. Therefore, a real-time pulse wave monitoring system based on a single-point CFPS can well predict human physiological signals such as PTT, SBP and DBP.

Flexible capacitive pressure sensor based on interdigital electrodes with porous microneedle arrays for physiological signal monitoring

Flexible pressure sensors have many potential applications in the monitoring of physiological signals because of their good biocompatibility and wearability. However, their relatively low sensitivity, linearity, and stability have hindered their large-scale commercial application. Herein, a flexible capacitive pressure sensor based on an interdigital electrode structure with two porous microneedle arrays (MNAs) is proposed. The porous substrate that constitutes the MNA is a mixed product of polydimethylsiloxane and NaHCO3. Due to its porous and interdigital structure, the maximum sensitivity (0.07 kPa−1) of a porous MNA-based pressure sensor was found to be seven times higher than that of an imporous MNA pressure sensor, and it was much greater than that of a flat pressure sensor without a porous MNA structure. Finite-element analysis showed that the interdigital MNA structure can greatly increase the strain and improve the sensitivity of the sensor. In addition, the porous MNA-based pressure sensor was found to have good stability over 1500 loading cycles as a result of its bilayer parylene-enhanced conductive electrode structure. Most importantly, it was found that the sensor could accurately monitor the motion of a finger, wrist joint, arm, face, abdomen, eye, and Adam’s apple. Furthermore, preliminary semantic recognition was achieved by monitoring the movement of the Adam’s apple. Finally, multiple pressure sensors were integrated into a 3 × 3 array to detect a spatial pressure distribution. Compared to the sensors reported in previous works, the interdigital electrode structure presented in this work improves sensitivity and stability by modifying the electrode layer rather than the dielectric layer.

Wireless broadband acousto-mechanical sensing system for continuous physiological monitoring.

The human body generates various forms of subtle, broadband acousto-mechanical signals that contain information on cardiorespiratory and gastrointestinal health with potential application for continuous physiological monitoring. Existing device options, ranging from digital stethoscopes to inertial measurement units, offer useful capabilities but have disadvantages such as restricted measurement locations that prevent continuous, longitudinal tracking and that constrain their use to controlled environments. Here we present a wireless, broadband acousto-mechanical sensing network that circumvents these limitations and provides information on processes including slow movements within the body, digestive activity, respiratory sounds and cardiac cycles, all with clinical grade accuracy and independent of artifacts from ambient sounds. This system can also perform spatiotemporal mapping of the dynamics of gastrointestinal processes and airflow into and out of the lungs. To demonstrate the capabilities of this system we used it to monitor constrained respiratory airflow and intestinal motility in neonates in the neonatal intensive care unit (n = 15), and to assess regional lung function in patients undergoing thoracic surgery (n = 55). This broadband acousto-mechanical sensing system holds the potential to help mitigate cardiorespiratory instability and manage disease progression in patients through continuous monitoring of physiological signals, in both the clinical and nonclinical setting.

An Asymmetric Interlocked Structure with Modulus Gradient for Ultrawide Piezocapacitive Pressure Sensing Applications

AbstractHighly sensitive electronic skin (e‐skin) sensors with considerable operating range offer great possibilities for health care, human–‐machine interfaces, and intelligent robotics. Although subtle microstructures have been used in recent years to improve the sensitivity and sensing range of e‐skin sensors, the trade‐off between these features remains a big challenge to overcome. Here, an asymmetric interlocked gradient elastic modulus (AIGM) structure is designed for the first time to provide sufficient deformation space to the traditional interlocked structure while ensuring high sensitivity, and the gradient elastic modulus further extends the sensing range of the AIGM structure‐based sensors, which exhibit a maximum sensitivity of as high as 9.89 kPa−1 with an ultrawide working range that extends to above 530 kPa at the same time. As a demo, this sensor is integrated into an intelligent adjustable mouse platform for physiological signal monitoring, pressure feedback, and modulus adjustment, providing a viable alternative for hand disease prevention.

Flexible and Wearable Piezoresistive Sensors Based on Double Wrinkled Layers for Motion Monitoring and Human Physiological Signal Monitoring

Flexible and Wearable Piezoresistive Sensors Based on Double Wrinkled Layers for Motion Monitoring and Human Physiological Signal Monitoring

Omnidirectional Configuration of Stretchable Strain Sensor Enabled by the Strain Engineering with Chiral Auxetic Metamaterial.

An electromechanical interface plays a pivotal role in determining the performance of a stretchable strain sensor. The intrinsic mechanical property of the elastomer substrate prevents the efficient modulation of the electromechanical interface, which limits the further evolution of a stretchable strain sensor. In this study, a chiral auxetic metamaterial (CAM) is incorporated into the elastomer substrate of a stretchable strain sensor to override the deformation behavior of the pristine device and regulate the device performance. The tunable isotropic Poisson's ratio (from 0.37 to -0.25) achieved by the combination of CAM and elastomer substrate endows the stretchable strain sensor with significantly enhanced sensitivity (53-fold improvement) and excellent omnidirectional sensing ability. The regulation mechanism associated with crack propagation on the deformed substrate is also revealed with finite element simulations and experiments. The demonstration of on-body monitoring of human physiological signals and a smart training assistant for trampoline gymnastics with the CAM-incorporated strain sensor further illustrates the benefits of omnidirectionally enhanced performance.

Biocompatible Gel-Free Coconut Oil and Carbon Black Electrodes for ECG and Respiration Measurements

The current state of the art in telemedicine has increased the interest in long term monitoring of physiological and bioelectric signals. This motivated the development of materials and techniques for the fabrication of biocompatible, user and environmentally friendly alternatives to conventional resistive wet electrodes. Here we report a method for the fabrication of dry flexible and stretchable electrodes based on Coconut-Oil and Carbon Black for the monitoring of electrophysiological signals without conductive gels. The highly stretchable material shows a specific resistance ρ down to 33.2±12.3Ωm, high conformability, and a stretchability up to 1500%. The epidermal electrodes were used to record Electrocardiographic (ECG) signals and measure respiration in a 3-lead configuration and compared to commercial wet electrodes. Even after being elongated by 100% for 100 stretch/release cycles, a reliable recording of the QRS-complex is demonstrated without the need for any contact enhancer or substances that cause skin reaction, demonstrating the potential use of this material for long term ECG monitoring applications.

Smart battery-free and wireless bioelectronic platform based on a nature-skin-derived organohydrogel for chronic wound diagnosis, assessment, and accelerated healing

Using smart electroactive hydrogels with emerging bioelectronics to monitor wound status wirelessly in real-time is a promising approach as an unremitting therapy for refractory chronic wounds. Based on a natural skin-derived organohydrogel (SGC@MA-Gel) triboelectric nanogenerator, a bioresorbable, battery-free, and wireless smart wound treatment bioelectronic system for pharmacological treatment, nanogenerator-driven electrostimulation, and visual wound condition assessment was engineered in this study. The inherent hierarchical architecture and origins of natural skin, in tandem with the substantial loading capabilities of collagen-based biomaterials, endowed SGC@MA-Gel with favorable characteristics. These included mechanical and adhesive robustness, electrical conductivity, antibacterial properties, controlled on-demand drug release capabilities, and human physiological signal monitoring performance. The customizable bioelectronic system, developed in-house, employs a single-electrode triboelectric nanogenerator, which facilitates skin wound healing via electrostimulation. Consequently, the system's biosensors can simultaneously measure glucose, uric acid, and pH at wound sites, thereby enabling accurate wound diagnosis and real-time assessment. This system accelerates cell proliferation and wound healing in vitro and in vivo, which validates its effectiveness in chronic wound treatment. The results demonstrated that the smart wound treatment bioelectronic system could provide a facile therapeutic strategy to assist with integrated wound monitoring, diagnosis, and therapy for hospitalized patients and those recovering at home.

All-Fabric Capacitive Pressure Sensors with Piezoelectric Nanofibers for Wearable Electronics and Robotic Sensing.

Flexible pressure sensors are increasingly sought after for applications ranging from physiological signal monitoring to robotic sensing; however, the challenges associated with fabricating highly sensitive, comfortable, and cost-effective sensors remain formidable. This study presents a high-performance, all-fabric capacitive pressure sensor (AFCPS) that incorporates piezoelectric nanofibers. Through the meticulous optimization of conductive fiber electrodes and P(VDF-TrFE) nanofiber dielectric layers, the AFCPS exhibits exceptional attributes such as high sensitivity (4.05 kPa-1), an ultralow detection limit (0.6 Pa), an extensive detection range (∼100 kPa), rapid response time (<26 ms), and robust stability (>14,000 cycles). The sensor's porous structure enhances its compressibility, while its piezoelectric properties expedite charge separation, thereby increasing the interface capacitance and augmenting overall performance. These features are elucidated further through multiphysical field-coupling simulations and experimental testing. Owing to its comprehensive superior performance, the AFCPS has demonstrated its efficacy in monitoring human activity and physiological signals, as well as in discerning soft robotic grasping movements. Additionally, we have successfully implemented multiple AFCPS units as pressure sensor arrays to ascertain spatial pressure distribution and enable intelligent robotic gripping. Our research underscores the promising potential of the AFCPS device in wearable electronics and robotic sensing, thereby contributing significantly to the advancement of high-performance fabric-based sensors.

Ultra-sensitive flexible pressure sensor with hierarchical structural laser-induced carbon nanosheets/carbon nanotubes composite film

Recently, laser induced carbon nanosheets (LICs) based flexible pressure sensors have received widespread attention. Introducing hierarchical microstructure is an effective method to improve the sensitivity of pressure sensors. However, implementing hierarchical microstructure on LICs film through a simple and low damage process remains a challenge. Herein, taking advantage of the reshaping characteristics of low melting point thermoplastic substrate styrene-isoprene-styrene (SIS) under laser thermal effects, a simple and one-step laser thermoforming process is used to spontaneously form surface microstructure on the LICs film during the formation of LICs. Therefore, a hierarchical structure composed of porous structure and surface microstructure was achieved on the LICs film through the one-step process. Furthermore, a hybrid strategy combining hierarchical microstructure with carbon nanotubes (CNTs)/LICs composite was proposed to improve the sensitivity of LICs based pressure sensor. Benefiting from the hierarchical structure of LICs and the composite conductive network constructed by CNTs and LICs, the LICs/CNTs@SIS sensor exhibits ultrahigh sensitivity of 1089 kPa−1 in 0–5 kPa and ultrahigh gauge factor of 1493 within the strain rate range of 20%–28%. Meanwhile, the sensor has good stability (>1400 cycles), response and recovery times of 23.8 ms and 25.9 ms, excellent reversibility, and 1220% elongation at break. Besides, the application of the sensor in physiological signal monitoring and pressure distribution detection indicates its great potential in flexible electronics.

Predicting obsessive-compulsive disorder episodes in adolescents using a wearable biosensor-A wrist angel feasibility study.

Obsessive-compulsive disorders (OCD) are marked by distress, negative emotions, mental processes and behaviors that are reflected in physiological signals such as heart rate, electrodermal activity, and skin temperature. Continuous monitoring of physiological signals associated with OCD symptoms may make measures of OCD more objective and facilitate close monitoring of prodromal symptoms, treatment progress and risk of relapse. Thus, we explored the feasibility of capturing OCD events in the real world using an unobtrusive wrist worn biosensor and machine learning models. Nine adolescents (ages 10-17 years) with mild to moderate-severe OCD were recruited from child and adolescent mental health services. Participants were asked to wear the biosensor in the lab during conditions of rest and exposure to OCD symptom-triggering stimuli and for up to 8 weeks in their everyday lives and register OCD events. We explored the relationships among physiological data, registered OCD events, age, OCD symptom severity and symptom types. In the machine learning models, we considered detection of OCD events as a binary classification problem. A nested cross-validation strategy with either random 10-folds, leave-one-subject-out, or leave-week(s)-out in both layers was used. We compared the performance of four models: logistic regression, random forest (RF), feedforward neural networks, and mixed-effect random forest (MERF). To explore the ability of the models to detect OCD events in new patients, we assessed the performance of participant-based generalized models. To explore the ability of models to detect OCD events in future, unseen data from the same patients, we compared the performance of temporal generalized models trained on multiple patients with personalized models trained on single patients. Eight of the nine participants collected biosensor signals totaling 2, 405 h and registered 1, 639 OCD events. Better performance was obtained when generalizing across time compared to across patients. Generalized temporal models trained on multiple patients were found to perform better than personalized models trained on single patients. RF and MERF models outperformed the other models in terms of accuracy in all cross-validation strategies, reaching 70% accuracy in random and participant cross-validation. Our pilot results suggest that it is possible to detect OCD episodes in the everyday lives of adolescents using physiological signals captured with a wearable biosensor. Large scale studies are needed to train and test models capable of detecting and predicting episodes. ClinicalTrials.gov: NCT05064527, registered October 1, 2021.