Recent Progress in Intelligent Wearable Sensors for Health Monitoring and Wound Healing Based on Biofluids

Recent developments and future perspectives of microfluidics and smart technologies in wearable devices

Wearable devices are gaining popularity in the fields of health monitoring, diagnosis, and drug delivery. Recent advances in wearable technology have enabled real-time analysis of biofluids such as sweat, interstitial fluid, tears, saliva, wound fluid, and urine. The integration of microfluidics and emerging smart technologies, such as artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT), into wearable devices offers great potential for accurate and non-invasive monitoring and diagnosis. This paper provides an overview of current trends and developments in microfluidics and smart technologies in wearable devices for analyzing body fluids. The paper discusses common microfluidic technologies in wearable devices and the challenges associated with analyzing each type of biofluid. The paper emphasizes the importance of combining smart technologies with microfluidics in wearable devices, and how they can aid diagnosis and therapy. Finally, the paper covers recent applications, trends, and future developments in the context of intelligent microfluidic wearable devices.

Lab-on-a-Fiber Wearable Multi-Sensor for Monitoring Wound Healing

Chronic wounds are regarded as a silent epidemic, affecting 1-2% of the population and representing 2-4% of healthcare expenses. The current methods used to assess the wound healing process are based on the visual evaluation of physical parameters. This work aims to design a wearable non-invasive device capable of evaluating three parameters simultaneously: the pH and the levels of glucose and matrix metalloproteinase (MMP) present in the wound exudate. The device is composed of three independent polymer optical fibers functionalized with fluorescent-based sensing chemistries specific to the targeted analytes. Each fiber is characterized in terms of detection sensitivity and selectivity confirming their suitability for monitoring the targeted parameters in ranges relevant to the wound environment. The selectivity and robustness of the multi-sensing device are confirmed with analyses using complex solutions with different pH levels (5, 6, and 7), different concentrations of glucose (1.25, 2.5, and 5 mm), and MMP (1.25, 2.5, and 5 µg mL-1 ). Given the simple set-up, the affordability of the materials used and the possibility of detecting additional parameters relevant to wound healing, such multi-sensing fiber-based devices could pave the way for novel non-invasive wearable tools enabling the assessment of wound healing from the molecular perspective.

Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care Diagnostics

This work involves exploring non-invasive sensor technologies for data collection and preprocessing, specifically focusing on novel thermal calibration methods and assessing low-cost infrared radiation sensors for facial temperature analysis. Additionally, it investigates innovative approaches to analyzing acoustic signals for quantifying coughing episodes. The research integrates diverse data capture technologies to analyze them collectively, considering their temporal evolution and physical attributes, aiming to extract statistically significant relationships among various variables for valuable insights. The study delineates two distinct aspects: cough detection employing a microphone and a neural network, and thermal sensors employing a calibration curve to refine their output values, reducing errors within a specified temperature range. Regarding control units, the initial implementation with an ESP32 transitioned to a Raspberry Pi model 3B+ due to neural network integration issues. A comprehensive testing is conducted for both fever and cough detection, ensuring robustness and accuracy in each scenario. The subsequent work involves practical experimentation and interoperability tests, validating the proof of concept for each system component. Furthermore, this work assesses the technical specifications of the prototype developed in the preceding tasks. Real-time testing is performed for each symptom to evaluate the system's effectiveness. This research contributes to the advancement of non-invasive sensor technologies, with implications for healthcare applications such as remote health monitoring and early disease detection.

Wearable Clinic: From Microneedle-Based Sensors to Next-Generation Healthcare Platforms

The rapid development of wearable biosensing calls for next-generation devices that allow continuous, real-time, and painless monitoring of health status along with responsive medical treatment. Microneedles have exhibited great potential for the direct access of dermal interstitial fluid (ISF) in a minimally invasive manner. Recent studies of microneedle-based devices have evolved from conventional off-line detection to multiplexed, wireless, and integrated sensing. In this review, the classification and fabrication techniques of microneedles are first introduced, and then the representative examples of microneedles for transdermal monitoring with different sensing modalities are summarized. State-of-the-art advances in therapeutic and closed-loop systems are presented to formulate guidelines for the development of next-generation microneedle-based healthcare platforms. The potential challenges and prospects are discussed to pave a new avenue toward pragmatic applications in the real world.

Novel Wearable Optical Sensors for Vital Health Monitoring Systems-A Review

Wearable sensors are pioneering devices to monitor health issues that allow the constant monitoring of physical and biological parameters. The immunity towards electromagnetic interference, miniaturization, detection of nano-volumes, integration with fiber, high sensitivity, low cost, usable in harsh environments and corrosion-resistant have made optical wearable sensor an emerging sensing technology in the recent year. This review presents the progress made in the development of novel wearable optical sensors for vital health monitoring systems. The details of different substrates, sensing platforms, and biofluids used for the detection of target molecules are discussed in detail. Wearable technologies could increase the quality of health monitoring systems at a nominal cost and enable continuous and early disease diagnosis. Various optical sensing principles, including surface-enhanced Raman scattering, colorimetric, fluorescence, plasmonic, photoplethysmography, and interferometric-based sensors, are discussed in detail for health monitoring applications. The performance of optical wearable sensors utilizing two-dimensional materials is also discussed. Future challenges associated with the development of optical wearable sensors for point-of-care applications and clinical diagnosis have been thoroughly discussed.

Methods for the metrological characterization of wearable devices for the measurement of physiological signals: state of the art and future challenges

Wearable devices are rapidly spreading in many different application fields and with diverse measurement accuracy targets. However, data on their metrological characterization are very often missing or obtained with non-standardized methods, hence resulting in barely comparable results. The aim of this review paper is to discuss the existing methods for the metrological characterization of wearable sensors exploited for the measurement of physiological signals, highlighting the room for research still available in this field. Furthermore, as a case study, the authors report a customized method they have tuned for the validation of wireless electrocardiographic monitors. The literature provides a plethora of test/validation procedures, but there is no shared consensus on test parameters (e.g. test population size, test protocol, output parameters of validation procedure, etc.); on the other hand, manufacturers rarely provide measurement accuracy values and, even when they do, the test protocol and data processing pipelines are generally not disclosed. Given the increasing interest and demand of wearable sensors also for medical and diagnostic purposes, the metrological performance of such devices should be always considered, to be able to adequately interpret the results and always deliver them associated with the related measurement accuracy.•The sensor metrological performance should be always properly considered.•There are no standard methods for wearable sensors metrological characterization.•It is important to define rigorous test protocols, easily tunable for specific target applications.

Surface-enhanced Raman scattering as a potential strategy for wearable flexible sensing and point-of-care testing non-invasive medical diagnosis

As a powerful and effective analytical tool, surface-enhanced Raman scattering (SERS) has attracted considerable research interest in the fields of wearable flexible sensing and non-invasive point-of-care testing (POCT) medical diagnosis. In this mini-review, we briefly summarize the design strategy, the development progress of wearable SERS sensors and its applications in this field. We present SERS substrate analysis of material design requirements for wearable sensors and highlight the benefits of novel plasmonic particle-in-cavity (PIC)-based nanostructures for flexible SERS sensors, as well as the unique interfacial adhesion effect and excellent mechanical properties of natural silk fibroin (SF) derived from natural cocoons, indicating promising futures for applications in the field of flexible electronic, optical, and electrical sensors. Additionally, SERS wearable sensors have shown great potential in the fields of different disease markers as well as in the diagnosis testing for COVID-19. Finally, the current challenges in this field are pointed out, as well as the promising prospects of combining SERS wearable sensors with other portable health monitoring systems for POCT medical diagnosis in the future.

Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment

Nowadays, artificial intelligence (AI) creates numerous promising opportunities in the life sciences. AI methods can be significantly advantageous for analyzing the massive datasets provided by biotechnology systems for biological and biomedical applications. Microfluidics, with the developments in controlled reaction chambers, high-throughput arrays, and positioning systems, generate big data that is not necessarily analyzed successfully. Integrating AI and microfluidics can pave the way for both experimental and analytical throughputs in biotechnology research. Microfluidics enhances the experimental methods and reduces the cost and scale, while AI methods significantly improve the analysis of huge datasets obtained from high-throughput and multiplexed microfluidics. This review briefly presents a survey of the role of AI and microfluidics in biotechnology. Also, the incorporation of AI with microfluidics is comprehensively investigated. Specifically, recent studies that perform flow cytometry cell classification, cell isolation, and a combination of them by gaining from both AI methods and microfluidic techniques are covered. Despite all current challenges, various fields of biotechnology can be remarkably affected by the combination of AI and microfluidic technologies. Some of these fields include point-of-care systems, precision, personalized medicine, regenerative medicine, prognostics, diagnostics, and treatment of oncology and non-oncology-related diseases.

Quality Assessment and Morphological Analysis of Photoplethysmography in Daily Life

The photoplethysmographic (PPG) signal has been applied in various research fields, with promising results for its future clinical application. However, there are several sources of variability that, if not adequately controlled, can hamper its application in pervasive monitoring contexts. This study assessed and characterized the impact of several sources of variability, such as physical activity, age, sex, and health state on PPG signal quality and PPG waveform parameters (Rise Time, Pulse Amplitude, Pulse Time, Reflection Index, Delta T, and DiastolicAmplitude). We analyzed 31 24 h recordings by as many participants (19 healthy subjects and 12 oncological patients) with a wristband wearable device, selecting a set of PPG pulses labeled with three different quality levels. We implemented a Multinomial Logistic Regression (MLR) model to evaluate the impact of the aforementioned factors on PPG signal quality. We then extracted six parameters only on higher-quality PPG pulses and evaluated the influence of physical activity, age, sex, and health state on these parameters with Generalized Linear Mixed Effects Models (GLMM). We found that physical activity has a detrimental effect on PPG signal quality quality (94% of pulses with good quality when the subject is at rest vs. 9% during intense activity), and that health state affects the percentage of available PPG pulses of the best quality (at rest, 44% for healthy subjects vs. 13% for oncological patients). Most of the extracted parameters are influenced by physical activity and health state, while age significantly impacts two parameters related to arterial stiffness. These results can help expand the awareness that accurate, reliable information extracted from PPG signals can be reached by tackling and modeling different sources of inaccuracy.

The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey

Human activity recognition (HAR) has become an intensive research topic in the past decade because of the pervasive user scenarios and the overwhelming development of advanced algorithms and novel sensing approaches. Previous HAR-related sensing surveys were primarily focused on either a specific branch such as wearable sensing and video-based sensing or a full-stack presentation of both sensing and data processing techniques, resulting in weak focus on HAR-related sensing techniques. This work tries to present a thorough, in-depth survey on the state-of-the-art sensing modalities in HAR tasks to supply a solid understanding of the variant sensing principles for younger researchers of the community. First, we categorized the HAR-related sensing modalities into five classes: mechanical kinematic sensing, field-based sensing, wave-based sensing, physiological sensing, and hybrid/others. Specific sensing modalities are then presented in each category, and a thorough description of the sensing tricks and the latest related works were given. We also discussed the strengths and weaknesses of each modality across the categorization so that newcomers could have a better overview of the characteristics of each sensing modality for HAR tasks and choose the proper approaches for their specific application. Finally, we summarized the presented sensing techniques with a comparison concerning selected performance metrics and proposed a few outlooks on the future sensing techniques used for HAR tasks.

The Current State of Optical Sensors in Medical Wearables

Optical sensors play an increasingly important role in the development of medical diagnostic devices. They can be very widely used to measure the physiology of the human body. Optical methods include PPG, radiation, biochemical, and optical fiber sensors. Optical sensors offer excellent metrological properties, immunity to electromagnetic interference, electrical safety, simple miniaturization, the ability to capture volumes of nanometers, and non-invasive examination. In addition, they are cheap and resistant to water and corrosion. The use of optical sensors can bring better methods of continuous diagnostics in the comfort of the home and the development of telemedicine in the 21st century. This article offers a large overview of optical wearable methods and their modern use with an insight into the future years of technology in this field.