Physiological Signal Monitoring Research Articles

Treefrog-inspired Flexible Electrode with High Permeability, stable Adhesion, and Robust Durability.

Long-term continuous monitoring (LTCM) of physiological electrical signals is an effective means for detecting several cardiovascular diseases. However, the integrated challenges of stable adhesion, low impedance, and robust durability under different skin conditions significantly hinder the application of flexible electrodes in LTCM. This paper proposes a structured electrode inspired by the treefrog web, comprising dispersed pillars at the bottom and asymmetric cone holes at the top. Attachment structures with a dispersed pillar improve the contact stability (adhesion increases 2.79/13.16 times in dry/wet conditions compared to an electrode without structure). Improved permeable duct structure provides high permeability (12 times compared to cotton). Due to high adhesion and permeability, the electrode's durability is 40 times larger than commercial Ag/AgCl electrodes. The treefrog web-like electrode has great advantages in permeability, adhesion, and durability, resulting in prospects for application in physiological electrical signal detection and a new design idea for LTCM wearable dry electrodes. This article is protected by copyright. All rights reserved.

Nano-engineered versatile Janus natural-skin with sandwich structure for wearable all-season personal thermal management

Thermal management wearables hold significant promise for various applications, such as bio-integrated electronics, multifunctional fabrics, thermoelectric devices, etc. Given the complex and volatile external environmental conditions they may encounter, thermal management wearables should possess versatile and comprehensive auxiliary functions to enable cutting-edge advanced applications. Herein, we present a multifunctional nano-engineered Janus-type natural-skin (SHRC-skin) offering dual-modes of solar heating and radiative cooling. Additionally, the SHRC-skin integrated functionalities like flammability resistance, electromagnetic interference (EMI) shielding, and physiological signal monitoring achieved through the integration of traditional spray techniques and a phase-conversion pathway on natural-skin as a substrate, SHRC-skin enabled all-season thermal management. The radiative cooling side of the SHRC-skin incorporates a CA/Mg11(HPO3)8(OH)6 composite coating with an irregular porous structure, while the solar heating side consists of multi-walled carbon nanotubes (MWCNT) with a rough structure. The radiative cooling layer exhibited a solar reflectance of ∼90.13 % and a mid-infrared emittance of ∼87.6 %, whereas the heating layer demonstrated a solar absorptance of ∼89 %. These attributes translated to excellent thermal management performance in outdoor-tests. Furthermore, SHRC-skin offered extensive additional functionalities, including exceptional asymmetric wetting, flame retardancy, electrical conductivity, Joule heating, electromagnetic shielding, and physiological signal monitoring. This versatility significantly enhances SHRC-skin's adaptability to complex and diverse environments. In summary, the multifunctional SHRC-skin seamlessly transitions between cooling and heating modes without additional energy input. This innovation holds great promise for all-season wearable thermal management, environmentally friendly travel, and energy-efficient building furnishings, and opens new possibilities for the development of wearable materials across various scenarios.

Versatile Biomass‐Based Injectable Photothermal Hydrogel for Integrated Regenerative Wound Healing and Skin Bioelectronics

AbstractThe continuously growing utilization of wound healing materials and skin bioelectronics urges the development of flexible hydrogels for personal therapy and health management. Versatile conductive hydrogels prepared from natural biomass are ideal candidates as one of the promising solutions for chronic wound management. Here, the study proposes a kind of robust (strain: 1560.8%), adhesive, self‐healing, injectable, antibacterial (sterilization rate: 99%), near‐infrared (NIR) photothermal responsive, biocompatible, and conductive hydrogel (CPPFe@TA) composed of carboxymethyl cellulose and tannic acid/iron ion complex (TA@Fe3+), featuring rapid self‐assembly and tunable crosslinking time. TA@Fe3+ facilitated the self‐catalysis of the polymerization reaction, and the crosslinking time could be controlled by adjusting Fe3+ concentration. Under NIR irradiation, the hydrogel exhibited remarkable photothermal antibacterial performance. In the full‐thickness skin defect repair experiment on mice, the prepared hydrogel dressing significantly enhanced wound healing. After 14 days, the wound healing rate (95.49%) of CPPFe@TA3 hydrogel + NIR treatment greatly exceeded that of commercial dressings. Meanwhile, the hydrogels has good electrical conductivity and thermo‐responsiveness, making them promising for skin bioelectronics in physiological signal monitoring and rehabilitation exercise management. This work therefore offers a promising strategy for developing versatile biomass‐based hydrogels, which is expected to be applicable to integrated regenerative wound healing and skin bioelectronics.

Flexible piezoelectric pressure sensor based on BaTiO3-CNT/RTV composite film with filter paper microstructures for monitoring hand motions

Flexible piezoelectric sensors are widely used in various applications such as physiological signal monitoring and human-computer interaction. The present study introduces a BaTiO3-CNT/RTV piezoelectric sensor fabricated using a filter paper template. It incorporates micro-scale fiber stacking and a 1% CNT doping in the microstructure, resulting in a notable enhancement of sensor sensitivity, increasing it from 0.07 V N−1 to 0.69 V N−1, representing an almost tenfold improvement. Furthermore, the study investigates the influence of affecting factors like the flexible substrate of the sensing film, thickness, and mass fractions of various materials on the output voltage. The sensor exhibits superior characteristics such as good repeatability under 5000 cyclic loads, high elongation at break, fast response (80 ms) and recovery times (90 ms), and good linearity. It also demonstrates outstanding sensitivity (12 mV/10°) when monitoring different finger bending states, enabling real-time, sensitive, and reliable hand motion tracking. This sensor holds promising prospects for future developments in the fields of intelligent grasping and sign language translation.

An audio-semantic multimodal model for automatic obstructive sleep Apnea-Hypopnea Syndrome classification via multi-feature analysis of snoring sounds.

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a common sleep-related breathing disorder that significantly impacts the daily lives of patients. Currently, the diagnosis of OSAHS relies on various physiological signal monitoring devices, requiring a comprehensive Polysomnography (PSG). However, this invasive diagnostic method faces challenges such as data fluctuation and high costs. To address these challenges, we propose a novel data-driven Audio-Semantic Multi-Modal model for OSAHS severity classification (i.e., ASMM-OSA) based on patient snoring sound characteristics. In light of the correlation between the acoustic attributes of a patient's snoring patterns and their episodes of breathing disorders, we utilize the patient's sleep audio recordings as an initial screening modality. We analyze the audio features of snoring sounds during the night for subjects suspected of having OSAHS. Audio features were augmented via PubMedBERT to enrich their diversity and detail and subsequently classified for OSAHS severity using XGBoost based on the number of sleep apnea events. Experimental results using the OSAHS dataset from a collaborative university hospital demonstrate that our ASMM-OSA audio-semantic multimodal model achieves a diagnostic level in automatically identifying sleep apnea events and classifying the four-class severity (normal, mild, moderate, and severe) of OSAHS. Our proposed model promises new perspectives for non-invasive OSAHS diagnosis, potentially reducing costs and enhancing patient quality of life.

Multi-head CNN-based activity recognition and its application on chest-mounted sensor-belt

In recent years, a great deal of research has been done on the identification, monitoring, and classification of human activities. Human activity recognition (HAR) is a term commonly used to describe the automatic identification of physical activities. For activity recognition, there are primarily vision-based and sensor-based methods available. The computer vision-based method is generally effective in lab settings, but because of clutter, fluctuating light levels, and contrast, it may not perform well in real-world scenarios. Continuous monitoring and analysis of physiological signals obtained from heterogeneous sensors attached to an individual’s body is required to realise sensor-based HAR systems. Most of the previous research in human activity recognition (HAR) is biased along with feature engineering and pre-processing which requires a good amount of domain knowledge. Application-specific modelling and time-taking methods are involved in these approaches. In this work, the multi-head convolutional neural network-based human activity recognition framework is proposed where automatic feature extraction and classification are involved in the form of an end-to-end classification approach. Experiments of this approach are performed by taking raw wearable sensor data with few pre-processing steps and without the involvement of a handcrafted feature extraction technique. 99.23% and 93.55% accuracy are obtained on the WISDM and UCI-HAR datasets which denoted the much improvement in the assessment of HAR over other similar approaches. The model is also tested on locally collected data from a chest mounted belt with fabric sensors and an accuracy of 87.14% has been achieved on that data.

A Multimodal Feature Fusion Brain Fatigue Recognition System Based on Bayes-gcForest.

Modern society increasingly recognizes brain fatigue as a critical factor affecting human health and productivity. This study introduces a novel, portable, cost-effective, and user-friendly system for real-time collection, monitoring, and analysis of physiological signals aimed at enhancing the precision and efficiency of brain fatigue recognition and broadening its application scope. Utilizing raw physiological data, this study constructed a compact dataset that incorporated EEG and ECG data from 20 subjects to index fatigue characteristics. By employing a Bayesian-optimized multi-granularity cascade forest (Bayes-gcForest) for fatigue state recognition, this study achieved recognition rates of 95.71% and 96.13% on the DROZY public dataset and constructed dataset, respectively. These results highlight the effectiveness of the multi-modal feature fusion model in brain fatigue recognition, providing a viable solution for cost-effective and efficient fatigue monitoring. Furthermore, this approach offers theoretical support for designing rest systems for researchers.

Nanofillers Embedded Flexible Piezoelectric Polymer Sensor Pad for Robotic and Human Finger Tap Analysis

Flexible pressure sensors are receiving a lot of attention due to their use in wearable electronic devices, human-machine interactive systems, and physiological signal monitoring. In this paper, we present a novel nanofiller embedded thin film based piezoelectric pressure sensor for finger tapping analysis. The active material is fabricated using poly (vinylidene fluoride) (PVDF) having zirconium oxide (ZrO2) nanofillers. It is observed that the incorporation of ZrO2 nanofillers has a substantial impact on the structural characteristics of PVDF polymer, leading to enhancements in its piezoelectric properties. Moreover, the PVDF/2wt% ZrO2 composite film sensor showed a notable maximum piezoelectric output voltage of ~210 millivolts at a load of 20g (0.88 kPa applied pressure). A four-fold enhancement in response is observed in case of PVDF/2wt% ZrO2 composite in comparison to pristine PVDF sensor. Further, a sensitivity of 0.255 ± 0.083 kPa−1 has been recorded for the composite based sensor. Additionally, maximum of 115mV is obtained when the composite sensor is tapped manually. The recorded maximum values for impulsive pressure (38g), high pressure (108g), and low pressure (45g) are 76mV, 150mV, and 90mV, respectively. The sensor is efficiently able to detect robotic tapping and stress on the human fingers. An efficiency of around 90% was achieved in PVDF/ 2wt% ZrO2 at 5kV/cm using PE hysteresis loop data. The findings indicate that PVDF/ZrO2 composite based piezoelectric pressure sensor has promising capabilities for applications in wearable electronic devices and medical diagnostics.

Enhancing quality assessment of live Pacific oysters (Crassostrea gigas) using flexible sensors real‐time monitoring physiological signals

AbstractShell‐closing strength (SCS) is commonly used to assess oyster vigor and quality as an important physiological stress indicator of the oyster organism. In this study, a quality decision support system based on flexible wireless sensor network and web services was designed and developed to explore the application of SCS in the rapid assessment of vitality and quality. Based on physiological and environmental information collected by flexible wireless sensor networks, this study proposes a novel quantitative vitality assessment method to identify oyster life decline cycles and processes. Meanwhile, TVB‐N, TVC, and pH were selected as quality indicators, and a back‐propagation artificial neural network (BP‐ANN) was used to predict and evaluate oyster quality indicators. The results showed that the root mean square errors (RMSE) for TVB‐N, TVC, and pH were 0.4624, 0.8827, and 0.1941; the coefficients of determination (R2) were 0.8919, 0.8578, and 0.6249. Therefore, the rapid assessment of oyster vitality and quality based on a flexible wireless sensor network is a reliable and effective means.Practical applicationsThis study aims to explore the application of live oyster SCS for rapid quality evaluation. In this article, a quality decision support system based on a flexible wireless sensor network (WSN) and web services is developed to improve the quality and health of live oysters in the supply chain. The use of the multi‐sensor system enables the monitoring and collection of environmental and physiological signals of live oysters throughout the supply chain process. The evaluation of the quality of live oysters using physiological and environmental information collected by flexible wireless sensor networks is a reliable and efficient method. This will provide an effective and reliable quality evaluation and management for the oyster industry.

Wireless Technologies in Flexible and Wearable Sensing: from Materials Design, System Integration to Applications.

Wireless and wearable sensors attract considerable interest in personalized healthcare by providing a unique approach for remote, non-contact, and continuous monitoring of various health-related signals without interference with daily life. Recent advances in wireless technologies and wearable sensors have promoted practical applications due to their significantly improved characteristics, such as reduction in size and thickness, enhancement in flexibility and stretchability, and improved conformability to the human body. Currently, most researches focus on active materials and structural designs for wearable sensors, with just a few exceptions reflecting on the technologies for wireless data transmission. This review provides a comprehensive overview of the state-of-the-art wireless technologies and related studies on empowering wearable sensors. The emerging functional nanomaterials utilized for designing unique wireless modules are highlighted, which include metals, carbons, and MXenes. Additionally, the review outlines the system-level integration of wireless modules with flexible sensors, spanning from novel design strategies for enhanced conformability to efficient transmitting data wirelessly. Furthermore, the review introduces representative applications for remote and non-invasive monitoring of physiological signals through on-skin and implantable wireless flexible sensing systems. Finally, the challenges, perspectives, and unprecedented opportunities for wireless and wearable sensors are discussed. This article is protected by copyright. All rights reserved.

Reduced Graphene Oxide Interlocked Carbonized Loofah for Energy Harvesting and Physiological Signal Monitoring

Reduced Graphene Oxide Interlocked Carbonized Loofah for Energy Harvesting and Physiological Signal Monitoring

Wearable smart contact lenses: A critical comparison of three physiological signals outputs for health monitoring

Smart contact lenses (SCLs) have been considered as novel wearable devices for out-of-hospital and self-monitoring applications. They are capable of non-invasively and continuously monitoring physiological signals in the eyes, including vital biophysical (e.g., intraocular of pressure, temperature, and electrophysiological signal) and biochemical signals (e.g., pH, glucose, protein, nitrite, lactic acid, and ions). Recent progress mainly focuses on the rational design of wearable SCLs for physiological signal monitoring, while also facilitating the treatment of various ocular diseases. It covers contact lens materials, fabrication technologies, and integration methods. We also highlight and discuss a critical comparison of SCLs with electrical, microfluidic, and optical signal outputs in health monitoring. Their advantages and disadvantages could help researchers to make decisions when developing SCLs with desired properties for physiological signal monitoring. These unique capabilities make SCLs promising diagnostic and therapeutic tools. Despite the extensive research in SCLs, new technologies are still in their early stages of development and there are a few challenges to be addressed before these SCLs technologies can be successfully commercialized particularly in the form of rigorous clinical trials.

A multimodal physiological dataset for driving behaviour analysis

Physiological signal monitoring and driver behavior analysis have gained increasing attention in both fundamental research and applied research. This study involved the analysis of driving behavior using multimodal physiological data collected from 35 participants. The data included 59-channel EEG, single-channel ECG, 4-channel EMG, single-channel GSR, and eye movement data obtained via a six-degree-of-freedom driving simulator. We categorized driving behavior into five groups: smooth driving, acceleration, deceleration, lane changing, and turning. Through extensive experiments, we confirmed that both physiological and vehicle data met the requirements. Subsequently, we developed classification models, including linear discriminant analysis (LDA), MMPNet, and EEGNet, to demonstrate the correlation between physiological data and driving behaviors. Notably, we propose a multimodal physiological dataset for analyzing driving behavior(MPDB). The MPDB dataset’s scale, accuracy, and multimodality provide unprecedented opportunities for researchers in the autonomous driving field and beyond. With this dataset, we will contribute to the field of traffic psychology and behavior.

A Low-Cost Smart Wearable Glove for Non-Invasive Health Monitoring

Wearable physiological signal monitoring systems hold notable potential in the future of personal healthcare by seamlessly integrating into daily life, providing continuous monitoring, and aiding in the early detection of health issues. This research presents a wearable health monitoring glove, with a focus on cost-effectiveness while maintaining efficiency for developing countries. The wearable glove can track vital physiological indicators like Blood Pressure, Body Temperature, Glucose level, Blood Oxygen Saturation, Hemoglobin level, ECG, Room Temperature, Humidity, and Motion Tracking. A user-friendly interface facilitates easy interaction, while efforts in energy-efficient design and power management aim to prolong battery life. Also, real-time data monitoring ensures precision in signal analysis and the extraction of vital health data of individuals. The proposed wearable hand glove utilizes a collection of sensors and integrates them towards the diverse detection of skin humidity, temperature, blood oxygen, hemoglobin, etc. non-invasively. Apart from its technical features, the research explores potential applications in healthcare, fitness tracking, and research fields, presenting a versatile solution. Beyond its technical attributes, the research explores potential applications in medical and personal healthcare, fitness tracking, sports, etc. Collaborative efforts with regulatory bodies assess the feasibility of obtaining necessary approvals or certifications, while scalability considerations pave the way for potential mass production and market deployment.

Building Synthetic Biosensors Using Red Blood Cell Proteins.

As the use of engineered cell therapies expands from pioneering efforts in cancer immunotherapy to other applications, an attractive but less explored approach is the use of engineered red blood cells (RBCs). Compared to other cells, RBCs have a very long circulation time and reside in the blood compartment, so they could be ideally suited for applications as sentinel cells that enable in situ sensing and diagnostics. However, we largely lack tools for converting RBCs into biosensors. A unique challenge is that RBCs remodel their membranes during maturation, shedding many membrane components, suggesting that an RBC-specific approach may be needed. Toward addressing this need, here we develop a biosensing architecture built on RBC membrane proteins that are retained through erythropoiesis. This biosensor employs a mechanism in which extracellular ligand binding is transduced into intracellular reconstitution of a split output protein (including either a fluorophore or an enzyme). By comparatively evaluating a range of biosensor architectures, linker types, scaffold choices, and output signals, we identify biosensor designs and design features that confer substantial ligand-induced signal in vitro. Finally, we demonstrate that erythroid precursor cells engineered with our RBC-protein biosensors function in vivo. This study establishes a foundation for developing RBC-based biosensors that could ultimately address unmet needs including noninvasive monitoring of physiological signals for a range of diagnostic applications.

Design and signal processing of plastic optical fiber respiratory sensors based on smartphones

A respiratory signal sensing scheme based on plastic optical fiber and smartphone is proposed. Respiratory signals are achieved by detecting the changes in abdominal circumference caused by the inhalation and exhalation of air from the lungs during breathing. A flexible clamp is prepared based on the 3D printing technology and the changes in abdominal circumference are leading to the lateral distance variation between the end face of the two plastic optical fibers in the clamp. The experiment demonstrates the scheme’s capability to detect a broad range of stable respiratory frequencies and weak signals. Finally, an automatic multi-scale peak detection algorithm is utilized to achieve real-time display of transient respiratory rate, which enables the timely feedback of apnea and abnormal signals. The proposed respiratory sensing system provides an efficient, accurate, convenient and low-cost solution for human physiological signal monitoring.

Wearable 3-D Meshed Textile Pressure Sensor for Physiological Signal Monitoring

Wearable 3-D Meshed Textile Pressure Sensor for Physiological Signal Monitoring

Wavelet Transform and SVM Based Heart Disease Monitoring for Flexible Wearable Devices

INTRODUCTION: Heart disease has been a major health challenge globally, therefore the development of reliable and real-time heart disease monitoring methods is crucial for the prevention and management of heart health. The aim of this study is to explore a flexible wearable device approach based on wavelet transform and support vector machine (SVM) to improve the accuracy and portability of heart disease monitoring. OBJECTIVES: The main objective of this study is to develop a wearable device that combines wavelet transform and SVM techniques to achieve accurate monitoring of physiological signals of heart diseases. METHODS: An integrated method for heart disease monitoring was constructed using flexible sensor technology combined with a wavelet transform and support vector machine. The Marr wavelet transform was applied to the ECG signals, and the feature vectors were constructed by feature parameter extraction. Then, the radial basis kernel SVM was utilized to identify the three ECG signals. The performance of the algorithm was optimized by adjusting the SVM parameters to improve the accurate monitoring of heart diseases. RESULTS: The experimental results show that the proposed wavelet transform and SVM-based approach for flexible wearable devices achieves satisfactory results in heart disease monitoring. In particular, the algorithm successfully extracted feature vectors and accurately classified different ECG signals by skillfully combining the wavelet transform and SVM techniques for the processing of premature beat signals. CONCLUSION: The potential application value of the wavelet transform and SVM-based flexible wearable device approach in heart disease monitoring is emphasized. By efficiently processing ECG signals, the method provides an innovative and comfortable solution for real-time monitoring of cardiac diseases.

PAM/CNTs-Au microcrack sensor with high sensitivity and wide detection range for multi-scale human motion detection

As the quintessential material for flexible sensors, conductive hydrogels face an inherent sensitivity-sensing range paradox and electrical-mechanical balance, which significantly curtail its applicability in multiscale motion detection. Inspired by the spider slit sensor, PAM/CNTs-Au hydrogel microcrack sensor is obtained by depositing conductive gold film on the surface of PAM/CNTs hydrogel using magnetron sputtering. By modulating the cross-linking density of conductive hydrogels and the content of carbon nanotubes (CNTs), it is possible to tailor the mechanical properties and adhesion of PAM/CNTs-Au hydrogel crack-based sensors. This self-adhering flexible sensor aids in minimizing detection errors arising from mechanical mismatch between flexible sensors and human tissues. Moreover, the exceptional electrical conductivity of CNTs, coupled with their unique one-dimensional structure, facilitates the formation of an interconnected conductive network "bridge" within the hydrogel. The harmonization of electrical and mechanical attributes in the PAM/CNTs flexible substrate is achieved by finely tuning the crosslinking density of the PAM hydrogel and the content of carbon CNTs. Simultaneously, the high-conductivity "islands" formed by the Au conductive layer during stretching ensures elevated sensitivity (gauge factor (GF) = 54.89) over an extensive detection range (>1200%). The distinctive "island-bridge" configuration of the PAM/CNTs-Au hydrogel microcrack sensor effectively suppresses crack propagation, thereby markedly augmenting the sensor's stability. Successful monitoring of physiological and motion signals across various scales attests to the potential of the PAM/CNTs-Au hydrogel microcrack sensor in wearable biosensor.

Highly-sensitive, broad-range, and highly-dynamic MXene pressure sensors with multi-level nano-microstructures for healthcare and soft robots applications

Flexible pressure sensors are urgently needed in the fast-growing field of intelligent robots and human healthcare. However, most current pressure sensors can rarely achieve high sensitivity and dynamic behavior within a broad detection range, which greatly hinders their practical application. Herein, we develop a highly-sensitive, wide-range, and highly-dynamic MXene piezoresistive pressure sensor by constructing a multi-level nano-micro (MLNM) sensing structure. The unique structure was formed by combining the accordion-like layered nano-structures of MXene nanosheets and the abundant micro-protrusions transferred from sandpapers. It can dramatically increase the structure compressibility, the interlayer contact area, and thus resistance variations during compression, resulting in a synergistic enhancement in sensitivity, detection range, and dynamic performance. A fully solution-processable process based on sandpaper casting and oxygen plasma treatment enabled tough interface bonding was developed for low-cost and large-area fabrication. As a result, a high sensitivity of 3.94 kPa−1, a broad range of 0–117.5 kPa, rapid response and relaxation times of 70 ms and 84 ms, and long-term stability of > 7500 cyclic compression were obtained, which exhibited significant enhancement in comprehensive performance compared to previous reports. Successful monitoring of diverse human physiological signals and robot movements underscores the considerable practical utility of our MXene-based sensors.