Smart Wearable Sensors Research Articles

Design and implementation of an IoT-based monitoring system for early detection of lumpy skin disease in cattle

Livestock farming serves a crucial role in global food security and sustainability, yet infections like Lumpy Skin Disease (LSD) present huge difficulties. LSD reduces the marketability and productivity of cattle, but it is not fatal. Current preventive measures that rely on routine veterinary testing can be time-consuming and miss early signs of infection, requiring a more effective management strategy first works This article presents an IoT-enabled intelligent system to prevent LSD in animals. The system uses a smart wearable sensor device to collect real-time health information including body temperature, heart rate and 3-axis motion data. Advanced analysis of data is considered to identify early diagnosis of potential LSD infection results based on deviations from normal parameters, this data prevents further infection within livestock by supporting immediate isolation. On a cloud-based online platform, and integrated into the mobile application provides a comprehensive experience and timely alerts to provide potential health issues. Furthermore, the IoT features of the system enable farmers and veterinarians to quickly guide and optimize disease control strategies, improving biosecurity protocols and overall livestock health management. This creative approach represents a significant improvement over traditional methods, providing a cost-effective, accurate and real-time solution for animal health monitoring and management.

An Invisible Dermal Nanotattoo-Based Smart Wearable Sensor for eDiagnostics of Jaundice.

Despite substantial progress in the diagnosis of jaundice/hyperbilirubinemia as the most common disease and cause of hospitalization of newborns, on the eve of Industry/Healthcare 5.0, the development of accurate and reliable wearable diagnostic sensors for noninvasive smart monitoring of bilirubin (BIL) is still in high demand. Aiming to fabricate a smart wearable sensor for early diagnosis of neonatal jaundice and its therapeutic monitoring, we here report a fluorescent dermal nanotattoo that further coupled with an IoT-integrated wearable optoelectronic reader for minimally invasive, continuous, and real-time monitoring of BIL in interstitial fluid. Selective recovery of quenched fluorescence of the dermal tattoo sensor, composed of biocompatible dissolving/hydrogel microneedles loaded with fluorescent carbon quantum dots, upon blue light exposure used for jaundice phototherapy was utilized for highly selective BIL sensing. The fascinating features of our developed smart wearable tattoo sensor and its successful results with high correlation with blood BIL results make it a highly promising sensor for easy, minimally invasive, reliable, and smart eDiagnostics and continuous therapeutic eMonitoring of jaundice and other BIL-induced diseases at the point of care. We envision that the developed nanotattoo sensing bioplatform will inspire the development of future smart tattoo sensors in various diagnostic and monitoring scenarios.

Safety 4.0: Harnessing computer vision for advanced industrial protection

In the pursuit of enhanced productivity, reduced costs, and minimized lead times, manufacturers are transitioning from traditional systems to autonomous systems. This shift, driven by the emergence of smart manufacturing and technological advancements such as robotics, collaborative robots (Cobots), automation, and digitalization, necessitates a parallel evolution in safety protocols—termed Safety 4.0—to mitigate the risks associated with such dynamic environments. The integration of smart technologies within manufacturing significantly transforms traditional workflows and intensifies the need for comprehensive safety training and guidelines. Innovations like smart personal protective equipment (PPE) and wearable sensors are pivotal in this transition, yet they often prove financially burdensome for manufacturers due to high costs and the scale of workforce deployment. Moreover, the effective use of these technologies requires continuous monitoring and data analysis, further straining resources. To address these challenges, this paper proposes the adoption of computer vision technology to enhance safety measures within manufacturing facilities, focusing on human and PPE detection. It details a holistic methodology encompassing data collection, preprocessing, training, and execution. The discussion extends to the implementation framework of this technology, emphasizing its role in enabling autonomous decision-making—a crucial step beyond mere detection. Furthermore, the paper explores the utilization of the accumulated data to develop immersive training modules employing Mixed Reality, thereby reinforcing safety protocols and fostering an environment of continuous learning and adaptation. This approach not only contributes to safeguarding personnel but also aligns with the financial and reputational interests of forward-thinking manufacturers.

A multi-stimulus responsive nanogel actuator with the π-π stacking interaction self-assembly and application for bioinspired smart skin with light, heat and humidity responses and superb UV resistance

The fabrication and development of assemblies of bionic smart skin with multi-stimulus response and multi-color tunable properties is a major challenge in the field of advanced materials and smart wearable devices. In this work, an amphiphilic functional nanogel switch connected by long polyether chain segments containing bis-spiropyran with the π-π stacking interactions was designed. A multi-stimulus responsive color-changing nanogel actuator (SP-PEG-NA) was constructed using ionic liquid-assisted self-assembly. A composite bionic smart skin (WPU/SP-PEG@CF) was prepared by compositing SP-PEG-NA, waterborne polyurethane (WPU) and flexible cotton fabric based on physical cross-linking. The smart skin possesses multiple color-changing properties in response to light, heat and humidity. Even under ultra-low UV intensity (0.025 mW/cm2), WPU/SP-PEG@CF also exhibits significant photochromic properties. Surprising, the smart composite constructed with the nanogels possessed extremely high UV shielding performance (UPF 628.68) and photocontrolled hydrophobic-hydrophilic transition properties. The self-assembled nanogel actuators greatly improved the stimulus–response sensitivity. The multiple supramolecular noncovalent interactions of the nanogel actuator such as hydrophilic long polyether structures hydrogen-bonding and π-π stacking interactions endowed nanogel highly self-assembly properties. They showed excellent multi-stimulus response in color-changing, versatility and reversibility. The multi-stimulus responsive and multifunctional smart composites prepared using self-assembled nanogel has potential applications in smart wearable materials, sensors and information display.

Functionalized Face Masks as Smart Wearable Sensors for Multiple Sensing.

Wearable sensors provide continuous physiological information and measure deviations from healthy baselines, resulting in the potential to personalize health management and diagnosis of diseases. With the emergence of the COVID-19 pandemic, functionalized face masks as smart wearable sensors for multimodal and/or multiplexed measurement of physical parameters and biochemical markers have become the general population for physiological health management and environmental pollution monitoring. This Review examines recent advances in applications of smart face masks based on implantation of digital technologies and electronics and focuses on respiratory monitoring applications with the advantages of autonomous flow driving, enrichment enhancement, real-time monitoring, diversified sensing, and easily accessible. In particular, the detailed introduction of diverse respiratory signals including physical, inhalational, and exhalant signals and corresponding associations of health management and environmental pollution is presented. In the end, we also provide a personal perspective on future research directions and the remaining challenges in the commercialization of smart functionalized face masks for multiple sensing.

Highly Sensitive, Degradable, and Rapid Self-Healing Hydrogel Sensor with Semi-Interpenetrating Network for Recognition of Micro-Expressions.

Flexible conductive hydrogels have revolutionized the lives and are widely applied in health monitoring and wearable electronics as a new generation of sensing materials. However, the inherent low mechanical strength, sensitivity, and lack of rapid self-healing capacity results in their short life, poor detection accuracy, and environmental pollution. Inspired by the molecular structure of bone and its chemical characteristics, a novel fully physically cross-linked conductive hydrogel is fabricated by the introduction of nanohydroxyapatite (HAp) as the dynamic junction points. In detail, the dynamically cross-linked network, including multiple physical interactions, provides it with rapid self-healing ability and excellent mechanical properties (elongation at break (>1200%), tensile strength (174kPa), and resilience (92.61%)). Besides, the ions (Cl-, Li+, Ca2+) that move freely within the system impart outstanding electrical conductivity (2.46±0.15 S m-1), high sensitivity (gauge factor, GF>8), good antifreeze (-40.2°C), and humidity properties. The assembled sensor can be employed to sensitively detect various large human motions and subtle changes in behavior (facial expressions, speech recognition). Meanwhile, the hydrogel sensor can also degrade in phosphate-buffered saline solution without causing any environmental pollution. Therefore, the designed hydrogels may become a promising candidate material in the future potential applications for smart wearable sensors and electronic skin.

TECHNOLOGY-BASED EVALUATION IN PARKINSONISM

Parkinson's disease is a neurodegenerative disorder characterized by motor and non-motor symptoms that worsen over time. Today, traditional clinical assessment methods are used to monitor disease progression and evaluate treatment responses. However, these methods are subjective and may fail to measure specific conditions. In recent years, thanks to advances in wearable technologies, smart sensors, and data analysis, technology-based approaches to the assessment of patients with Parkinson's disease have gained more attention. With these technologies, objective data can be obtained by monitoring patients' daily activities, motor functions, and symptoms. Motor symptoms such as tremor severity, rigidity, bradykinesia, postural instabilities, freezing phenomenon, and motor parameters of speech impairment can be objectively measured through these technologies. Furthermore, the ability to remotely transmit these data allows patients to be assessed in their own homes and provides continuous feedback to healthcare professionals. This review highlights the importance and potential of technology-based assessment methods in Parkinson's patients and aims to guide future research.

An efficient pressure sensor based on environmental-friendly CNTs-graphene-PDMS film

Given the rapid advancements in artificial intelligence, there is an escalating demand for wearable sensors. An efficient graphene-based material synthesized from the mesophase pitch of waste slurry oil was integrated into a cost-effective piezoresistive pressure sensor consisting of a conductive film made of carbon nanotubes (CNTs), graphene, and Polydimethylsiloxane (PDMS). A simple fabrication approach has been suggested to infuse PDMS with CNTs-graphene, resulting in a pressure sensor exhibiting superior conductivity, enhanced sensitivity, and quick responsiveness to diverse pressure variations. Moreover, films containing varying percentages of graphene were compared. Scanning electron microscopy was utilized to examine the surface and structural characteristics of the CNTs-graphene-PDMS film, alongside studying the pressure sensor's sensing capabilities. Various applications were examined for both the individual sensor and the array of sensors. The findings demonstrate the successful detection of diverse human motions, Morse code recognition, and effective discernment of various pressures by the fabricated pressure sensor, indicating its potential for applications in smart devices, robotics, and wearable sensors.

Smart wearable flexible sensor based on laser-induced graphene/gold nanoparticles/black phosphorus nanosheets for in situ quercetin detection

In situ detection of metabolic substances in plants is necessary for the development of smart agriculture. However, there is a mechanical mismatch between the traditional rigid sensing interfaces and the softness surface of plant leaves, which can reduce the reliability and accuracy of sensor. Stretchable and flexible sensors provide new solutions to this problem. Herein, using quercetin (Que) as a model, we developed a flexible electrochemical sensor for in situ monitoring of metabolite in plants. A light and thin laser-induced graphene (LIG) electrodes with good flexibility were obtained after polydimethylsiloxane (PDMS) transfer. Gold nanoparticles (AuNPs) and two-dimensional black phosphorus (BP) nanosheets were fabricated on the electrode to further improve the electrochemical performance. The sensor can detect Que concentrations in the range of 1–100 μM with a detection limit of 0.65 μM (S/N=3). The sensor can adapt well to the soft plant leaf surfaces, thus realizing the application of in situ and in vivo detection in the field. This innovative plant wearable biosensor is expected to be a next-generation electronic candidate for intelligent agriculture, paving the way for in-situ and in vivo detection, and facilitating the intelligentsias of modern agriculture.

Use of Silk Fibroin Material Composites for Green, Flexible Supercapacitors.

Within the context of seeking eco-friendly and readily available materials for energy storage, there is a pressing demand for energy storage solutions that employ environmentally sustainable, high-performance, and adaptable constituents. Specifically, such materials are essential for use in wearable technology, smart sensors, and implantable medical devices, whereas, more broadly, their use plays a pivotal role in shaping their efficiency and ecological footprint. Here, we demonstrate an entirely biopolymer-based supercapacitor with a remarkable performance, achieving a capacitance greater than 0.2 F cm-2 at a charge-discharge current of 10 mA cm-2 with 94% capacitance retention after 20,000 cycles. The supercapacitor is composed of three distinct silk fibroin (SF) composite materials, namely, photo-cross-linkable SF (Sil-MA) hydrogel, SF-polydopamine (SF-PDA), and SF bioplastic, to create a gel electrolyte, electrode binder, and encapsulation, respectively. Together, these elements form a mechanically and electrochemically robust skeleton for biofriendly energy storage devices. Moreover, these biomaterial-based supercapacitor devices show stretchability, flexibility, and compressibility while maintaining their electrochemical performance. The biomaterials and fabrication techniques presented can serve as a foundation for investigating various aqueous electrochemical energy storage systems, especially for emerging applications in wearable electronics and environmentally friendly material systems.

Fabrication of a Capacitive 3D Spacer Fabric Pressure Sensor with a Dielectric Constant Change for High Sensitivity.

Smart wearable sensors are increasingly integrated into everyday life, interfacing with the human body to enable real-time monitoring of biological signals. This study focuses on creating high-sensitivity capacitive-type sensors by impregnating polyester-based 3D spacer fabric with a Carbon Nanotube (CNT) dispersion. The unique properties of conductive particles lead to nonlinear variations in the dielectric constant when pressure is applied, consequently affecting the gauge factor. The results reveal that while the fabric without CNT particles had a gauge factor of 1.967, the inclusion of 0.04 wt% CNT increased it significantly to 5.210. As sensor sensitivity requirements vary according to the application, identifying the necessary CNT wt% is crucial. Artificial intelligence, particularly the Multilayer Perception (MLP) model, enables nonlinear regression analysis for this purpose. The MLP model created and validated in this research showed a high correlation coefficient of 0.99564 between the model predictions and actual target values, indicating its effectiveness and reliability.

Novel Smart Wearable Sensors Based on PVDF Reinforced With CNTs for Human Motion Monitoring

Novel Smart Wearable Sensors Based on PVDF Reinforced With CNTs for Human Motion Monitoring

Oxidative Stress in Military Missions-Impact and Management Strategies: A Narrative Analysis.

This narrative review comprehensively examines the impact of oxidative stress on military personnel, highlighting the crucial role of physical exercise and tailored diets, particularly the ketogenic diet, in minimizing this stress. Through a meticulous analysis of the recent literature, the study emphasizes how regular physical exercise not only enhances cardiovascular, cognitive, and musculoskeletal health but is also essential in neutralizing the effects of oxidative stress, thereby improving endurance and performance during long-term missions. Furthermore, the implementation of the ketogenic diet provides an efficient and consistent energy source through ketone bodies, tailored to the specific energy requirements of military activities, and significantly contributes to the reduction in reactive oxygen species production, thus protecting against cellular deterioration under extreme stress. The study also underlines the importance of integrating advanced technologies, such as wearable devices and smart sensors that allow for the precise and real-time monitoring of oxidative stress and physiological responses, thus facilitating the customization of training and nutritional regimes. Observations from this review emphasize significant variability among individuals in responses to oxidative stress, highlighting the need for a personalized approach in formulating intervention strategies. It is crucial to develop and implement well-monitored, personalized supplementation protocols to ensure that each member of the military personnel receives a regimen tailored to their specific needs, thereby maximizing the effectiveness of measures to combat oxidative stress. This analysis makes a valuable contribution to the specialized literature, proposing a detailed framework for addressing oxidative stress in the armed forces and opening new directions for future research with the aim of optimizing clinical practices and improving the health and performance of military personnel under stress and specific challenges of the military field.

CQDs-Cross-Linked Conductive Collagen/PAA-Based Nanocomposite Organohydrogel Coupling Flexibility with Multifunctionality for Dual-Modal Sensing of Human Motions.

Conductive hydrogels are ideal materials for intelligent medical devices, human-machine interfaces, and flexible bioelectrodes due to their adjustable mechanical properties and electrical responsiveness, whereas it is still a great challenge to achieve the integration of excellent flexibility and biocompatibility into one hydrogel sensor while also incorporating self-healing, self-adhesion, environmental tolerance, and antimicrobial properties. Here, a nanocomposite conductive organohydrogel was constructed by using collagen (Col), alginate-derived carbon quantum dots (OSA-CQDs), poly(acrylic acid) (PAA), ethylene glycol reduced AgNPs, and Fe3+ ions. Depending on OSA-CQDs with multiple chemical binding sites and high specific surface area as cross-linkers, while coupling highly biologically active Col chains and PAA chains are serving as an energy dissipation module, the resulting organohydrogel exhibited excellent flexibility (795% of strain, 193 kPa of strength), high cell compatibility (>95% survival rate), self-healing efficiency (HE = 79.5%), antifreezing (-20 °C), moisturizing (>120 h), repeatable adhesion (strength >20 kPa, times >10), inhibitory activity against Escherichia coli and Staphylococcus aureus (9 and 21.5 cm2), conductivity, and strain sensitivity (σ = 1.34 S/m, gauge factor (GF) = 11.63). Based on the all-in-one integration of multifunction, the organohydrogel can collaboratively adapt to the multimode of strain sensing and electrophysiological sensing to realize wireless real-time monitoring of human activities and physiological health. Therefore, this work provides a new and common platform for the design and sensing of next-generation hydrogel-based smart wearable sensors.

Skin-Friendly Flexible Ultraviolet Detectors Based on Reversibly Deformable Worm-Like Polymer Nanoparticles.

Flexible ultraviolet (UV) light detection technology has important applications in wearable devices, smart sensors, and other fields and attracts much attention in recent years. However, for most semiconductor-based UV detectors, the elastic modulus between rigid semiconductors and flexible substrates is mismatched, which makes it difficult to fabricate UV detectors that meet the needs of wearable devices. Herein, a fully flexible, large-scale, skin-friendly UV photodetector component centered on photo-responsive worm-like polymer nanoparticles (NPs) is developed, and the resulting device can quantitatively detect UV illumination. Skin-friendly poly(vinyl alcohol) (PVA), amphiphilic azobenzene-containing polymer NPs (AzNPs), and water-soluble ionic liquids (IL) are formed into (AzNPs-IL)/PVA fabrics by electrospinning. There are interactions such as hydrogen bonding among PVA, AzNPs, and IL, which make the material system stable. The UV detector made of the fabric realizes UV sensing through the illuminance-mechanical stress-electrical signal conversion mechanism. It is capable of achieving a response time of 9 s, a detection range of 10-150mW cm-2 , and stability for 1000 cycle tests upon 365nm UV irradiation. Moreover, it has good skin affinity, and the water contact angle of the fabric is only 23.57°, which holds great promise for wearable smart devices.

Electrically Equivalent Head Tissue Materials for Electroencephalogram Study on Head Surrogates

With the recent advent of smart wearable sensors for monitoring brain activities in real-time, the scopes for using Electroencephalograms (EEGs) and Magnetoencephalography (MEG) in mobile and dynamic environments have become more relevant. However, their application in dynamic and open environments, typical of mobile wearable use, poses challenges. Presently, there is limited clinical data on using EEG/MEG as wearables. To advance these technologies at a time when large-scale clinical trials are not feasible, many researchers have turned to realistic phantom heads to further explore EEG and MEG capabilities. However, to achieve translational results, such phantom heads should have matching geometric features and electrical properties. Here, we have designed and fabricated multilayer chopped carbon fiber–PDMS reinforced composites to represent phantom head tissues. Two types of phantom layers are fabricated, namely seven-layer and four-layer systems with a goal to achieve matching electrical conductivities in each layer. Desired electrical conductivities are obtained by varying the weight fraction of the carbon fibers in PDMS. Then, the prototype system was calibrated and tested with a 32-electrode EEG cap. The test results demonstrated that the phantom effectively generates a variety of scalp potential patterns, achieved through a finite number of internal dipole generators within the phantom sample. This innovative design holds potential as a valuable test platform for assessing wearable EEG technology as well as developing an EEG analysis process.

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.

Retracted: Real-Time Detection of Lower Limb Training Stability Function Based on Smart Wearable Sensors

Retracted: Real-Time Detection of Lower Limb Training Stability Function Based on Smart Wearable Sensors

POF Sensor for Dynamic Physiological Evaluation in a Dog-Assisted ASD Therapy: A Case Study

Optical fiber sensors have been validated for physiological measurement as a potential device for smart textiles and wearable sensors. Also, developing sensors with Polymeric Optical Fiber (POF) shows promising applications in dynamic and real scenarios, as low-cost alternatives. Furthermore, the functioning of POF sensors can be used in no-skin contact applications, such as in highly skin-sensitive children with Autism Spectrum Disorder (ASD) during therapies. On the other hand, Dog-Assisted Therapy (DAT) is a stress-reducing method and has been studied to identify its influence on improving therapies and stress-related events. This work presents a case study in three weekly DAT sessions focused on speech therapy in a subject with ASD. A validated low-cost POF sensor was used in this first dynamic physiological measurement scenario. The analysis of five Heart Rate Variability (HRV) variables was made to compare the participant’s physiological status before and during the DAT. The obtained pulse signal showed the dynamic movements affecting the signal but being able to obtain the pulse peaks. The physiological comparison showed a difference in the mean peakto-peak time interval when the child was interacting with the dog than previous to the interaction in Session 1 and Session 2 (p-value<0.05). Also, an improvement in phonological consciousness was obtained, increasing by eight points after the three weeks of therapy. This case study showed the potential use of POF sensors in dynamic environments, and for wearable or smart textiles for children with ASD.

Situation identification in smart wearable computing systems based on machine learning and Context Space Theory

Wearable devices and smart sensors are increasingly adopted to monitor the behaviors of human and artificial agents. Many applications rely on the capability of such devices to recognize daily life activities performed by the monitored users in order to tailor their behaviors with respect to the occurring situations. Despite the constant evolution of smart sensing technologies and the numerous research in this field, an accurate recognition of in-the-wild situations still represents an open research challenge. This work proposes a novel approach for situation identification capable of recognizing the activities and the situations in which they occur in different environments and behavioral contexts, processing data acquired by wearable and environmental sensors.An architecture of a situation-aware wearable computing system is proposed, inspired by Endsley’s situation-awareness model, consisting of a two-step approach for situation identification. The approach first identifies the daily life activities via a learning-based technique. Simultaneously, the context in which the activities are performed is recognized using Context Space Theory. Finally, the fusion between the context state and the activities allows identifying the complex situations in which the user is acting. The knowledge regarding the situations forms the basis on which novel and smarter applications can be realized.The approach has been evaluated on the ExtraSensory public dataset and compared with state-of-the-art techniques, achieving an accuracy of 96% for the recognition of situations and with significantly low computational time, demonstrating the efficacy of the two-step situation identification approach.