Non-Invasive Flexible and Stretchable Wearable Sensors With Nano-Based Enhancement for Chronic Disease Care

Wearable sweat chloride sensors: materials, fabrication and their applications

Chloride is a crucial anion required for multiple functions in the human body including maintaining acid-base balance, fluid balance, electrical neutrality and supporting muscles and nerve cells. Low-chloride levels can cause nausea, diarrhoea, etc. Chloride levels are measured in different body fluids such as urine, serum, sweat and saliva. Sweat chloride measurements are used for multiple applications including disease diagnosis, sports monitoring, and geriatric care. For instance, a sweat chloride test is performed for cystic fibrosis screening. Further, sweat also offers continuous non-invasive access to body fluids for real-time monitoring of chloride that could be used for sports and geriatric care. This review focuses on wearable chloride sensors that are used for periodic and continuous chloride monitoring. The multiple sections in the paper discuss the clinical significance of chloride, detection methods, sensor fabrication methods and their application in cystic fibrosis screening, sports and geriatric care. Finally, the last section discusses the limitation of current sensors and future directions for wearable chloride sensors.

Exceptionally large fracture strength and stretchability of 2D ReS2 and ReSe2

Two-dimensional rhenium disulfide (ReS2) and rhenium diselenide (ReSe2) have gained popularity due to their outstanding optoelectronic properties. However, their mechanical behavior has not been investigated experimentally and many of their mechanical parameters are still unexplored. Here we conducted atomic force microscopy (AFM) indentation experiments and extracted their Young's moduli and found that it is thickness-independent. In addition, we found that both materials are capable of sustaining large pretension. Importantly, fracture tests showed that these materials exhibit exceptionally large fracture strength (32.9 ± 2.4 GPa and 27.7 ± 3.9 GPa for ReS2 and ReSe2, respectively) and stretchability (up to 24.2% for ReS2 and 23.0% for ReSe2). Therefore, this study shows the superior mechanical properties of ReS2 and ReSe2. Thus, it will open the path for their future integration into advanced applications that will benefit from their outstanding mechanical durability and attractive optoelectronic properties, such as flexible photodetectors, stretchable photonic devices, and strain-engineered electronics.

Perspective of Artificial Intelligence in Disease Diagnosis: A Review of Current and Future Endeavours in the Medical Field

Artificial intelligence (AI) has demonstrated significant promise for the present and future diagnosis of diseases. At the moment, AI-powered diagnostic technologies can help physicians decipher medical pictures like X-rays, magnetic resonance imaging, and computed tomography scans, resulting in quicker and more precise diagnoses. In order to make a prospective diagnosis, AI algorithms may also examine patient information, symptoms, and medical background. The application of AI in disease diagnosis is anticipated to grow as the field develops. In the future, AI may be used to find patterns in enormous volumes of medical data, aiding in disease prediction and prevention before symptoms appear. Additionally, by combining genetic data, lifestyle data, and environmental variables, AI may help in the diagnosis of complicated diseases. It is crucial to remember that while AI can be a powerful tool, it cannot take the place of qualified medical personnel. Instead, AI ought to support and improve diagnostic procedures, enhancing patient care and healthcare results. Future research and the use of AI for disease diagnosis must take ethical issues, data protection, and ongoing model validation into account.

One-Stop Strategy for Obtaining Controllable Sensitivity and Feasible Self-Patterning in Silver Nanowires/Elastomer Nanocomposite-Based Stretchable Ultrathin Strain Sensors

In this study, selective photo-oxidation (SPO) is proposed as a simple, fast, and scalable one-stop strategy that enables simultaneous self-patterning and sensitivity adjustment of ultrathin stretchable strain sensors. The SPO of an elastic substrate through irradiation time-controlled ultraviolet treatment in a confined region enables precise tuning of both the surface energy and the elastic modulus. SPO induces the hydrophilization of the substrate, thereby allowing the self-patterning of silver nanowires (AgNWs). In addition, it promotes the formation of nonpermanent microcracks of AgNWs/elastomer nanocomposites under the action of strain by increasing the elastic modulus. This effect improves sensor sensitivity by suppressing the charge transport pathway. Consequently, AgNWs are directly patterned with a width of 100 μm or less on the elastic substrate, and AgNWs/elastomer-based ultrathin and stretchable strain sensors with controlled sensitivity work reliably in various operating frequencies and cyclic stretching. Sensitivity-controlled strain sensors successfully detect both small and large movements of the human hand.

Wearable Two-Dimensional Nanomaterial-Based Flexible Sensors for Blood Pressure Monitoring: A Review

Flexible sensors have been extensively employed in wearable technologies for physiological monitoring given the technological advancement in recent years. Conventional sensors made of silicon or glass substrates may be limited by their rigid structures, bulkiness, and incapability for continuous monitoring of vital signs, such as blood pressure (BP). Two-dimensional (2D) nanomaterials have received considerable attention in the fabrication of flexible sensors due to their large surface-area-to-volume ratio, high electrical conductivity, cost effectiveness, flexibility, and light weight. This review discusses the transduction mechanisms, namely, piezoelectric, capacitive, piezoresistive, and triboelectric, of flexible sensors. Several 2D nanomaterials used as sensing elements for flexible BP sensors are reviewed in terms of their mechanisms, materials, and sensing performance. Previous works on wearable BP sensors are presented, including epidermal patches, electronic tattoos, and commercialized BP patches. Finally, the challenges and future outlook of this emerging technology are addressed for non-invasive and continuous BP monitoring.

Behavioral Interpretation of Willingness to Use Wearable Health Devices in Community Residents: A Cross-Sectional Study

Wearable health devices (WHDs) have become increasingly advantageous in long-term health monitoring and patient management. However, most people have not yet benefited from such innovative technologies, and the willingness to accept WHDs and their influencing factors are still unclear. Based on two behavioral theories: the theory of planned behavior (TPB) and the diffusion of innovation (DOI), this study aims to explore the influencing factors of willingness to use WHDs in community residents from the perspective of both internal and external factors. A convenience sample of 407 community residents were recruited from three randomly selected Community Health Service Centers (CHSCs) in Nanjing, China, and were investigated with a self-developed questionnaires. The mean score of willingness to use WHDs was 17.00 (range 5-25). In the dimensions of TPB, perceived behavioral control (β = 1.979, p < 0.001) was the strongest influencing factor. Subjective norms (β = 1.457, p < 0.001) and attitudes (β = 0.651, p = 0.016) were also positively associated with willingness. In innovation characteristics of DOI, compatibility (β = 0.889, p < 0.001) and observability (β = 0.576, p = 0.003) had positive association with the willingness to wear a WHD. This study supports the applicability of the two behavioral theories to interpret the willingness to use WHDs in Chinese community residents. Compared with the innovative features of WHDs, individual cognitive factors were more critical predictors of willingness to use.

An epifluidic electronic patch with spiking sweat clearance for event-driven perspiration monitoring

Sensory neurons generate spike patterns upon receiving external stimuli and encode key information to the spike patterns, enabling energy-efficient external information processing. Herein, we report an epifluidic electronic patch with spiking sweat clearance using a sensor containing a vertical sweat-collecting channel for event-driven, energy-efficient, long-term wireless monitoring of epidermal perspiration dynamics. Our sweat sensor contains nanomesh electrodes on its inner wall of the channel and unique sweat-clearing structures. During perspiration, repeated filling and abrupt emptying of the vertical sweat-collecting channel generate electrical spike patterns with the sweat rate and ionic conductivity proportional to the spike frequency and amplitude over a wide dynamic range and long time (> 8 h). With such 'spiking' sweat clearance and corresponding electronic spike patterns, the epifluidic wireless patch successfully decodes epidermal perspiration dynamics in an event-driven manner at different skin locations during exercise, consuming less than 0.6% of the energy required for continuous data transmission. Our patch could integrate various on-skin sensors and emerging edge computing technologies for energy-efficient, intelligent digital healthcare.

Emerging Artificial Intelligence–Empowered mHealth: Scoping Review

Background

Artificial intelligence (AI) has revolutionized health care delivery in recent years. There is an increase in research for advanced AI techniques, such as deep learning, to build predictive models for the early detection of diseases. Such predictive models leverage mobile health (mHealth) data from wearable sensors and smartphones to discover novel ways for detecting and managing chronic diseases and mental health conditions.

Objective

Currently, little is known about the use of AI-powered mHealth (AIM) settings. Therefore, this scoping review aims to map current research on the emerging use of AIM for managing diseases and promoting health. Our objective is to synthesize research in AIM models that have increasingly been used for health care delivery in the last 2 years.

Methods

Using Arksey and O’Malley’s 5-point framework for conducting scoping reviews, we reviewed AIM literature from the past 2 years in the fields of biomedical technology, AI, and information systems. We searched 3 databases, PubsOnline at INFORMS, e-journal archive at MIS Quarterly, and Association for Computing Machinery (ACM) Digital Library using keywords such as “mobile healthcare,” “wearable medical sensors,” “smartphones”, and “AI.” We included AIM articles and excluded technical articles focused only on AI models. We also used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) technique for identifying articles that represent a comprehensive view of current research in the AIM domain.

Results

We screened 108 articles focusing on developing AIM models for ensuring better health care delivery, detecting diseases early, and diagnosing chronic health conditions, and 37 articles were eligible for inclusion, with 31 of the 37 articles being published last year (76%). Of the included articles, 9 studied AI models to detect serious mental health issues, such as depression and suicidal tendencies, and chronic health conditions, such as sleep apnea and diabetes. Several articles discussed the application of AIM models for remote patient monitoring and disease management. The considered primary health concerns belonged to 3 categories: mental health, physical health, and health promotion and wellness. Moreover, 14 of the 37 articles used AIM applications to research physical health, representing 38% of the total studies. Finally, 28 out of the 37 (76%) studies used proprietary data sets rather than public data sets. We found a lack of research in addressing chronic mental health issues and a lack of publicly available data sets for AIM research.

Conclusions

The application of AIM models for disease detection and management is a growing research domain. These models provide accurate predictions for enabling preventive care on a broader scale in the health care domain. Given the ever-increasing need for remote disease management during the pandemic, recent AI techniques, such as federated learning and explainable AI, can act as a catalyst for increasing the adoption of AIM and enabling secure data sharing across the health care industry.

Flexible Electronics and Devices as Human-Machine Interfaces for Medical Robotics

Medical robots are invaluable players in non-pharmaceutical treatment of disabilities. Particularly, using prosthetic and rehabilitation devices with human-machine interfaces can greatly improve the quality of life for impaired patients. In recent years, flexible electronic interfaces and soft robotics have attracted tremendous attention in this field due to their high biocompatibility, functionality, conformability, and low-cost. Flexible human-machine interfaces on soft robotics will make a promising alternative to conventional rigid devices, which can potentially revolutionize the paradigm and future direction of medical robotics in terms of rehabilitation feedback and user experience. In this review, the fundamental components of the materials, structures, and mechanisms in flexible human-machine interfaces are summarized by recent and renowned applications in five primary areas: physical and chemical sensing, physiological recording, information processing and communication, soft robotic actuation, and feedback stimulation. This review further concludes by discussing the outlook and current challenges of these technologies as a human-machine interface in medical robotics.

Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the coronavirus disease 2019 (COVID-19) pandemic, has affected more than 400 million people worldwide. With the recent rise of new Delta and Omicron variants, the efficacy of the vaccines has become an important question. The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions, particularly for healthcare workers. In this paper, we discuss the current literature on invasive/contact and non-invasive/non-contact technologies (including Wi-Fi, radar, and software-defined radio) that have been effectively used to detect, diagnose, and monitor human activities and COVID-19 related symptoms, such as irregular respiration. In addition, we focused on cutting-edge machine learning algorithms (such as generative adversarial networks, random forest, multilayer perceptron, support vector machine, extremely randomized trees, and k-nearest neighbors) and their essential role in intelligent healthcare systems. Furthermore, this study highlights the limitations related to non-invasive techniques and prospective research directions.

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This article describes cutting-edge technology (invasive/non-invasive) and its role in the recognition of COVID-19 symptoms.

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This article summarizes state-of-art machine-learning algorithms and their roles in modern healthcare systems.

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This article presents the challenges associated with wireless sensing techniques and potential future research directions.

The status and perspectives of nanostructured materials and fabrication processes for wearable piezoresistive sensors

The wearable sensors have attracted a growing interest in different markets, including health, fitness, gaming, and entertainment, due to their outstanding characteristics of convenience, simplicity, accuracy, speed, and competitive price. The development of different types of wearable sensors was only possible due to advances in smart nanostructured materials with properties to detect changes in temperature, touch, pressure, movement, and humidity. Among the various sensing nanomaterials used in wearable sensors, the piezoresistive type has been extensively investigated and their potential have been demonstrated for different applications. In this review article, the current status and challenges of nanomaterials and fabrication processes for wearable piezoresistive sensors are presented in three parts. The first part focuses on the different types of sensing nanomaterials, namely, zero-dimensional (0D), one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) piezoresistive nanomaterials. Then, in second part, their fabrication processes and integration are discussed. Finally, the last part presents examples of wearable piezoresistive sensors and their applications.

Review of the Multi-Input Single-Inductor Multi-Output Energy Harvesting Interface Applied in Wearable Electronics

Along with the industrialization and popularization of the wearable electronics, an increasing number of the wireless sensor nodes (WSNs) are deployed. Nevertheless, the conventional battery-based power supply system has no longer satisfied the requirement of large-scale WSNs in terms of battery life, which emerges the energy harvesting (EH) technique. In order to combine various of energy sources and drive multi-loads, the multi-input single-inductor multi-output (MISIMO) EH interface applied to wearable electronics is spotlighted. In this mini-review article, the solutions for improving power conversion efficiency (PCE) and output quality in MISIMO EH interface are summarized. Furthermore, the future trends of MISIMO EH interface are also presented.

Wearable patch delivery system for artificial pancreas health diagnostic-therapeutic application: A review

The advanced stimuli-responsive approaches for on-demand drug delivery systems have received tremendous attention as they have great potential to be integrated with sensing and multi-functional electronics on a flexible and stretchable single platform (all-in-one concept) in order to develop skin-integration with close-loop sensation for personalized diagnostic and therapeutic application. The wearable patch pumps have evolved from reservoir-based to matrix patch and drug-in-adhesive (single-layer or multi-layer) type. In this review, we presented the basic requirements of an artificial pancreas, surveyed the design and technologies used in commercial patch pumps available on the market and provided general information about the latest wearable patch pump. We summarized the various advanced delivery strategies with their mechanisms that have been developed to date and representative examples. Mechanical, electrical, light, thermal, acoustic and glucose-responsive approaches on patch form have been successfully utilized in the controllable transdermal drug delivery manner. We highlighted key challenges associated with wearable transdermal delivery systems, their research direction and future development trends.

Systematic Review on Human Skin-Compatible Wearable Photoplethysmography Sensors

The rapid advances in human-friendly and wearable photoplethysmography (PPG) sensors have facilitated the continuous and real-time monitoring of physiological conditions, enabling self-health care without being restricted by location. In this paper, we focus on state-of-the-art skin-compatible PPG sensors and strategies to obtain accurate and stable sensing of biological signals adhered to human skin along with light-absorbing semiconducting materials that are classified as silicone, inorganic, and organic absorbers. The challenges of skin-compatible PPG-based monitoring technologies and their further improvements are also discussed. We expect that such technological developments will accelerate accurate diagnostic evaluation with the aid of the biomedical electronic devices.

A review of wearable and unobtrusive sensing technologies for chronic disease management

With the rapidly increasing number of patients with chronic disease, numerous recent studies have put great efforts into achieving long-term health monitoring and patient management. Specifically, chronic diseases including cardiovascular disease, chronic respiratory disease and brain disease can threaten patients' health conditions over a long period of time, thus effecting their daily lives. Vital health parameters, such as heart rate, respiratory rate, SpO2 and blood pressure, are closely associated with patients’ conditions. Wearable devices and unobtrusive sensing technologies can detect such parameters in a convenient way and provide timely predictions on health condition deterioration by tracking these biomedical signals and health parameters. In this paper, we review current advancements in wearable devices and unobtrusive sensing technologies that can provides possible tools and technological supports for chronic disease management. Current challenges and future directions of related techniques are addressed accordingly.

Homecare Robotic Systems for Healthcare 4.0: Visions and Enabling Technologies

Powered by the technologies that have originated from manufacturing, the fourth revolution of healthcare technologies is happening (Healthcare 4.0). As an example of such revolution, new generation homecare robotic systems (HRS) based on the cyber-physical systems (CPS) with higher speed and more intelligent execution are emerging. In this article, the new visions and features of the CPS-based HRS are proposed. The latest progress in related enabling technologies is reviewed, including artificial intelligence, sensing fundamentals, materials and machines, cloud computing and communication, as well as motion capture and mapping. Finally, the future perspectives of the CPS-based HRS and the technical challenges faced in each technical area are discussed.

Recent advances of tissue-interfaced chemical biosensors

This review discusses recent advances of tissue interfaced chemical biosensors, highlights current challenges and gives an outlook on future possibilities.

Pulse transit time based respiratory rate estimation with singular spectrum analysis

Respiratory rate (RR) is an important vital sign which can be difficult to measure accurately and unobtrusively in routine clinical practice. Pulse transit time (PTT), on the other hand, is unobtrusive to collect from electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Using PTT is a novel method to estimate and monitor blood pressure (BP) and RR. This study aimed to estimate continuous RR using PTT with singular spectrum analysis to extract respiratory components. The performance of this method was validated on 17 subjects who carried out spontaneous breathing and controlled deep breathing conditions. Three types of estimated RR parameters (average RR by power spectral density (PSD) (RR_PSD), number of breaths (RR_#), and instantaneous RR (RR_inst)) were compared with the corresponding reference RR. The reference RR was collected using a respiratory belt. Our findings demonstrate that the PTT signal reliably tracked respiratory variation with a root mean square error of 0.84, 1.11, and 0.74 breaths/min for RR_PSD, RR_#, and RR_inst estimations, respectively. Overall, RR estimated by PTT was more accurate than heart/pulse rate interval, QRS area, and PPG amplitude, which were also extracted from ECG and PPG. The results suggest that it may be feasible to use PTT as an estimation of RR and that ECG and PPG may be relied upon for monitoring not only RR but also BP and heart rate. Graphical abstract The Pulse Transit Time (PTT) based Respiratory Rate (RR) estimation with Singular Spectrum Analysis (SSA) provides a superior performance than the method with other respiratory indicators extracted from Electrocardiogram (ECG) or Photoplethysmogram (PPG).

Flexible Insole Sensors with Stably Connected Electrodes for Gait Phase Detection

Gait analysis is an important assessment tool for analyzing vital signals collected from individuals and for providing physical information of the human body, and it is emerging in a diverse range of application scenarios, such as disease diagnosis, fall prevention, rehabilitation, and human–robot interaction. Herein, a kind of surface processed conductive rubber was designed and investigated to develop a pressure-sensitive insole to monitor planar pressure in a real-time manner. Due to a novel surface processing method, the pressure sensor was characterized by stable contact resistance, simple manufacturing, and high mechanical durability. In the experiments, it was demonstrated that the developed pressure sensors were easily assembled with the inkjet-printed electrodes and a flexible substrate as a pressure-sensitive insole while maintaining good sensing performance. Moreover, resistive signals were wirelessly transmitted to computers in real time. By analyzing sampled resistive data combined with the gait information monitored by a visual-based reference system based on machine learning method (k-Nearest Neighbor algorithm), the corresponding relationship between plantar pressure distribution and lower limb joint angles was obtained. Finally, the experimental validation of the ability to accurately divide gait into several phases was conducted, illustrating the potential application of the developed device in healthcare and robotics.