Wearable Forms Research Articles

Stretchable and Self-Healable Fiber-Shaped Conductors Suitable for Harsh Environments.

Fiber-shaped conductors with high electrical conductivity, stretchability, and durability have attracted increasing attention due to their potential for integration into arbitrary wearable forms. However, these fiber conductors still suffer from low reliability and short life span, particularly in harsh environments. Herein, a conductive, environment-tolerant, stretchable, and healable fiber conductor (CESH), which consists of a self-healable and stretchable organohydrogel fiber core, a conductive and buckled silver nanowire coating, and a self-healable and waterproof protective sheath, is reported. Such a multilayer core-sheath design not only offers high stretchability (≈2400%), high electrical conductivity (1.0 × 106 S m-1 ), outstanding self-healing ability and durability, but also possesses unprecedented tolerance in harsh environments including wide working temperature (-60-20 °C), arid (≈10 % RH (RH: room humidity)), and underwater conditions. As proof-of-concept demonstrations, CESHs are integrated into various wearable formats as interconnectors to steadily perform the electric function under different mechanical deformations and harsh conditions. Such a new type of multifunctional fiber conductors can bridge the gap in stretchable and self-healing fiber technologies by providing ultrastable electrical conductance and excellent environmental tolerance, which can greatly expand the range of applications for fiber conductors.

Smart wearables addressing gait disorders: A review

Gait disorders are disorders related to the movement of a person which often leads to the inability to walk. Wearable technology in the form of consumer smart wearable devices can improve healthcare by addressing gait-related disorders. In the past decade, wearable technology has evolved a great deal. Currently, smartwatches are the widely used form of smart wearables and soft wearables are the promising future-smart wear. In this contemporary and generalized review, we have explored the current standing on the use of smart wearables and soft wearables in resolving gait-associated disorders. In this article, we have summarized our observations which exemplify multiple outcomes in measuring gait parameters, monitoring tremors, associated diagnoses, assisting motions, and improving patient care. We have also discussed the applications of wearables in the context of neurological disorders like Parkinson’s disease and essential tremors which often result in gait disorders. We have extended our discussions to other smart wearables including eyeglasses, textiles and soft flexible wearables.

Foraging for fashion’s future: The use of mycelium materials and fungi intelligence in fashion design

In reaction to the climate crisis, we have seen the emergence of environmental fashion trends that seek to limit energy use and cut emissions, with the aim of building sustainability. The provocation to design in concert with our biosphere is driving fashion designers to renegotiate our relationship with living systems in the quest for innovative ecological design models. This article explores the transformative coupling of fungi fabric and fashion. It considers how fashion brands and designers might develop new languages by bringing fungi’s root intelligence into wearable forms. A body of remarkable experiments has shown that fungi engage in decision-making, are capable of learning and possess short-term memory. The intelligence of the fungal ecosystem and its ability to repair damage in its own structure brings new possibilities to the idea of a ‘smart’ textile. Preserving these active qualities in a textile raises challenges for fashion brands and consumers about how to store the garments and whether we would need to feed our wardrobe of the future to keep it alive. If use of mycelium in fashion is to progress beyond the Petri dish or catwalk novelty, challenges of consistency and scale need to be addressed. Far from the ‘perfect cure’, fungi materials raise questions about their eco-credentials, finishing treatments and disposability, and the ethics of working with living organisms. With a rising experimentation in bio-fabrics, I suggest the need for a critical discourse of materials that aims to promote new questions and scholarship on the intersections between body and botany, decomposition and drapery, and engineering and ecosystems.

Is neuromorphic computing disruptive enough to 1) advance our understanding of the brain and 2) make the design and working of (bio)electronic devices efficient and scalable?

Neuromorphic computing is an approach to computing that is inspired by the structure and function of the brain. In terms of basic research, it aims at improving our understanding of biological intelligence by replicating aspects of its physical substrate – spiking neurons, synaptic plasticity etc. – and harness them towards also replicating its function. With respect to technological advances, it aims to inherit the brain’s combination of computational prowess and extreme energy efficiency; this is thought to foster a plethora of applications, from large-scale neuromorphic systems for machine learning to small-scale edge devices for signal processing and control, for example in the form of wearables for healthcare or adaptive sensors/processors for autonomous agents. The demand and usefulness of neuromorphic computing in bioelectronics is likely to increase in the future as researchers continue to explore its capabilities and develop new applications.

Self‐Cooling Gallium‐Based Transformative Electronics with a Radiative Cooler for Reliable Stiffness Tuning in Outdoor Use (Adv. Sci. 24/2022)

Transformative Electronics Gallium-based transformative electronics, which can convert between handheld and wearable forms, are vulnerable to a hot outdoor environment, which leads to unwanted softening of the electronics due to liquefaction of gallium. For the reliable rigid-mode operation in hot outdoors, the transformative electronics integrating a radiative cooler are developed to promote self-cooling through solar reflection and thermal radiation. More details can be found in article number 2202549 by Young Min Song, Jae-Woong Jeong, and co-workers.

Self-powered sensing technologies for human Metaverse interfacing

Dr. Jun Chen is currently an assistant professor in the Department of Bioengineering at the University of California, Los Angeles. His current research focuses on nanotechnology and bioelectronics for energy, sensing, and therapeutic applications in the form of smart textiles, wearables, and body area networks. With a current h-index of 85, he has published 2 books, 1 book chapter, and 230 journal articles, 130 in which he is a corresponding author, in <i>Chemical Reviews</i>, <i>Chemical Society Reviews</i>, <i>Nature Materials</i>, <i>Nature Electronics</i>, <i>Nature Communications</i>, <i>Science Advances</i>, <i>Joule</i>, <i>Matter</i>, and more. Among his many accolades are the Fellow of International Association of Advanced Materials, ACS PMSE Young Investigator Award, UCLA Society of Hellman Fellows Award, Okawa Foundation Research Award, Advanced Materials Rising Star, Materials Today Rising Star Award, ACS Nano Rising Stars Lectureship Award, Chem. Soc. Rev. Emerging Investigator Award, and many others. Beyond research, he is an associate editor of <i>Biosensors and Bioelectronics</i>. Yihao Zhou is currently a postdoc research scholar in the Department of Bioengineering at the University of California, Los Angeles under the supervision of Professor Jun Chen. He holds a BS in materials science from Nanjing University (2013) and a PhD in materials science from Duke University (2019). He has published more than 30 papers, and he appears as first/co-first author for articles in <i>Nature Materials</i>, <i>Nature Communications</i>, and <i>Matter</i>. His research interest includes wearable bioelectronics and electrochemistry. Xiao Xiao is a PhD student in the Department of Bioengineering at the University of California, Los Angeles under the supervision of Professor Jun Chen. Xiao obtained his BS in materials science and engineering from Beihang University in 2020. His research focuses on wearable electronics and smart textiles for energy, sensing, and therapeutic applications. Xiao has already published 47 journal articles, 25 in which he appears as first author, in <i>Science Advances</i>, <i>Chemical Reviews</i>, <i>Advanced Materials</i>, <i>Matter</i>, <i>Trends in Chemistry</i>, <i>Nano Letter</i>, <i>ACS Nano</i>, <i>Nano Energy</i>, and many others. Guorui Chen is currently a PhD candidate in the Department of Bioengineering at the University of California, Los Angeles under the supervision of Professor Jun Chen. He obtained his BS in materials science and engineering from Zhejiang University, China. His research thrust includes bioelectronics, smart textiles, and personalized healthcare. Guorui has already published 34 journal articles, 12 of them in which he appears as first author in <i>Nature Electronics</i>, <i>Chemical Reviews</i>, <i>Matter</i>, <i>ACS Nano</i>, <i>Trends in Biotechnology</i>, and many others. Xun Zhao is a PhD student in the Department of Bioengineering at the University of California, Los Angeles under the supervision of Professor Jun Chen. His current research focuses on wearable and implanted bioelectronics for healthcare applications. He is interested in studying and developing soft materials and systems. Xun has already published 30 journal articles, 10 in which he appears as first author, in <i>Chemical Reviews</i>, <i>Nature Materials</i>, <i>Nature Communications</i>, <i>Joule</i>, <i>Matter</i>, <i>Nano Energy</i>, <i>Advanced Energy Materials</i>, and many others.

Unobtrusive Monitoring of Sleep Cycles: A Technical Review

Polysomnography is the gold-standard method for measuring sleep but is inconvenient and limited to a laboratory or a hospital setting. As a result, the vast majority of patients do not receive a proper diagnosis. In an attempt to solve this issue, sleep experts are continually looking for unobtrusive and affordable alternatives that can provide longitudinal sleep tracking. Collecting longitudinal data on sleep can accelerate epidemiological studies exploring the effect of sleep on health and disease. These alternatives can be in the form of wearables (e.g., actigraphs) or nonwearable (e.g., under-mattress sleep trackers). To this end, this paper aims to review the several attempts made by researchers toward unobtrusive sleep monitoring, specifically sleep cycle. We have performed a literature search between 2016 and 2021 and the following databases were used for retrieving related articles to unobtrusive sleep cycle monitoring: IEEE, Google Scholar, Journal of Clinical Sleep Medicine (JCSM), and PubMed Central (PMC). Following our survey, although existing devices showed promising results, most of the studies are restricted to a small sample of healthy individuals. Therefore, a broader scope of participants should be taken into consideration during future proposals and assessments of sleep cycle tracking systems. This is because factors such as gender, age, profession, and social class can largely affect sleep quality. Furthermore, a combination of sensors, e.g., smartwatches and under-mattress sleep trackers, are necessary to achieve reliable results. That is, wearables and nonwearable devices are complementary to each other, and so both are needed to boost the field of at-home sleep monitoring.

The determination of skin surface pH via the skin volatile emission using wearable colorimetric sensors

Biodiagnostic sensors in the form of wearables have become of widespread interest in both the scientific and clinical communities in recent years due to their potential for monitoring human health, well-being and physical performance. However, collecting biofluids from the skin to enable biochemical analysis using wearables has proved challenging to date. This research seeks to overcome the need for fluid collection by designing a wearable sensing platform capable of monitoring the volatile emission from skin with the aim of collecting biodiagnostic information from the body. We investigate the use of a simple wearable colorimetric sensing platform incorporating sensor spots comprising encapsulated bromocresol green pH indicator dye in an enclosed headspace above the skin. The sensor spots undergo a colour change in response to basic volatile nitrogen compounds such as ammonia and amines being emitted from skin. By deploying this wearable in a healthy participant study, a strong correlation between sensor colour response and skin surface pH was demonstrated, despite a significant inter-individual variability being noted. Sensor response was observed to be highly dependent on gender as well as body site, and attributed to factors such as gland and microbial composition differences. Finally, the wearable's ability to detect changes in skin surface pH in response to topical skin treatments was demonstrated. Overall, this work demonstrates a novel and simple approach to wearable biodiagnostics that exploits the skin volatile emission to monitor skin physiology without the need for microneedles or the requirement to harvest fluid from the skin.

Capturing the nurse’s kinesthetic experience of wearing an exoskeleton

In this study, we introduce a video-ethnographic study of a research process in which nursing students try on exoskeletons—wearable forms of technology that are meant to decrease lower back strain when lifting something. We adopt microanalysis of video-recorded interaction to analyze moments in which a nurse tests how her body feels with the exoskeleton. Moreover, we explore how the nurse simultaneously makes accountable—observable and reportable (Garfinkel, 1967, p. vii)—to others how her body feels “inside,” i.e. her experience of kinaesthesia, or the ability of the human body to perceive its own movements and states as a ”body-in-motion” (Sheets-Johnstone, 2002, p. 138). We reflect on how the fact that we video recorded the whole process of testing the exoskeleton with three cameras and complemented our video analysis with observations and post-questionnaires enabled us to capture some of the kinesthetic, interactive, and context-specific aspects of trying on the exoskeleton.

Sodium alginate/bismuth (III) oxide composites for γ‐ray shielding applications

AbstractIn the present work, we have explored the efficacy of bismuth (III) oxide (Bi2O3) loaded, calcium ion cross‐linked solution cast sodium alginate composite films for radioprotective applications. Calcium ion cross‐linking increased the water and chemical resistance, which further improved on introduction of Bi2O3 into the composites. The 40 wt% Bi2O3 loaded films showed good heat resistance with the peak degradation temperature reaching as high as 251°C. The Bi2O3 loaded composites showed enhanced tensile strength (TS) and Youngs modulus (YM). Compared to high‐modulus polymers like epoxy, high‐density polyethylene (HDPE) and poly (vinyl chloride) (PVC), these exhibit relatively greater extent of stretching before breaking. The γ‐ray attenuation experiments showed that mass attenuation coefficients of the composites at various γ‐ray energies increased with filler loading. These composites are effective in shielding γ‐rays from radioactive sources like 137Cs, 22Na, 133Ba, and 60Co that are widely employed in several medical and industrial applications. The overall enhancement in thermal, mechanical, and radiation shielding characteristics of the composites may be attributed to the uniform distribution of the fillers in alginate matrix. These nontoxic sodium alginate/Bi2O3 composites can be used as soft and biodegradable radiation shields, which may be processed to wearable forms.

Design, manufacturing and applications of wearable triboelectric nanogenerators

Amid the rapid flourishment of wearable electronics, the issue of power consumption has become increasingly prominent. With an extremely simple and universal configuration, triboelectric nanogenerators (TENGs) have emerged and performed huge advantages in converting low-frequency mechanical energy into electricity. Thus, TENGs in wearable forms are expected to capture the energy in human motions and in-situ drive wearable devices. Moreover, such a type of energy conversion allows us to excavate the rich information behind the output signals. In this review, we first introduce the basic design of TENGs suitable for wearable devices, including material selection, process modulation and optimization of the form factor. Then, we elaborate on three key roles that wearable TENGs play in the current era of smart Internet of Things (IOT), i.e., active sensors, actuators, and human-machine interfaces. Next, we summarize the progress of wearable TENG-powered systems at the performance level and process level. In the end, we make concrete prospects for this technology to move further into the actual market and approach the general public lives. Triboelectric nanogenerators (TENGs) have built a bridge between human’s daily routine and wearable electronics due to their ability to capture the energy in human motions. Moreover, such a type of energy conversion allows us to excavate the rich information behind the output signals. In this review, we will show the latest progress of TENGs in the wearable field from the basic design to practical applications in detail. Wearable forms and working modes of TENGs. • The basic design of wearable TENGs including materials selection, process modulation and performance optimization. • Three roles played by wearable TENGs at device level, i.e., active sensors, actuators, and human-machine interface. • The progress of wearable TENG-powered systems at the performance level and process level. • The concrete prospects for this technology to move further into actual market and daily life.

A Smart Machine Learning Model for the Detection of Brain Hemorrhage Diagnosis Based Internet of Things in Smart Cities

Generally, the emergence of Internet of Things enabled applications inspired the world during the last few years, providing state-of-the-art and novel-based solutions for different problems. This evolutionary field is mainly lead by wireless sensor network, radio frequency identification, and smart mobile technologies. Among others, the IoT plays a key role in the form of smart medical devices and wearables, with the ability to collect varied and longitudinal patient-generated health data, and at the same time also offering preliminary diagnosis options. In terms of efforts made for helping the patients using IoT-based solutions, experts exploit capabilities of the machine learning algorithms to provide efficient solutions in hemorrhage diagnosis. To reduce the death rates and propose accurate treatment, this paper presents a smart IoT-based application using machine learning algorithms for the human brain hemorrhage diagnosis. Based on the computerized tomography scan images for intracranial dataset, the support vector machine and feedforward neural network have been applied for the classification purposes. Overall, classification results of 80.67% and 86.7% are calculated for the support vector machine and feedforward neural network, respectively. It is concluded from the resultant analysis that the feedforward neural network outperforms in classifying intracranial images. The output generated from the classification tool gives information about the type of brain hemorrhage that ultimately helps in validating expert’s diagnosis and is treated as a learning tool for trainee radiologists to minimize the errors in the available systems.

Agent-based Modeling for Ontology-driven Analysis of Patient Trajectories

Patients are often required to follow a medical treatment after discharge, e.g., for a chronic condition, rehabilitation after surgery, or for cancer survivor therapies. The need to adapt to new lifestyles, medication, and treatment routines, can produce an individual burden to the patient, who is often at home without the full support of healthcare professionals. Although technological solutions –in the form of mobile apps and wearables– have been proposed to mitigate these issues, it is essential to consider individual characteristics, preferences, and the context of a patient in order to offer personalized and effective support. The specific events and circumstances linked to an individual profile can be abstracted as a patient trajectory, which can contribute to a better understanding of the patient, her needs, and the most appropriate personalized support. Although patient trajectories have been studied for different illnesses and conditions, it remains challenging to effectively use them as the basis for data analytics methodologies in decentralized eHealth systems. In this work, we present a novel approach based on the multi-agent paradigm, considering patient trajectories as the cornerstone of a methodology for modelling eHealth support systems. In this design, semantic representations of individual treatment pathways are used in order to exchange patient-relevant information, potentially fed to AI systems for prediction and classification tasks. This paper describes the major challenges in this scope, as well as the design principles of the proposed agent-based architecture, including an example of its use through a case scenario for cancer survivors support.

Wearable artificial kidney: problems, progress and prospects.

The most common treatment option worldwide for persons with kidney failure is in-centre haemodialysis; however, this treatment has remained largely unchanged over decades owing to a lack of true patient-centred technological innovation. The development of safe and effective wearable forms of dialysis has the potential to transform the lives of these patients.

Security Considerations for Internet of Things: A Survey

Interconnecting "things" and devices that take the form of wearables, sensors, actuators, mobiles, computers, meters, or even vehicles is a critical requirement for the current era. These internetworked connections are serving the emerging applications home and building automation, smart cities and infrastructure, smart industries, and smart-everything. However, the security of these connected Internet of things (IoT) plays a centric role with no margin for error. After a review of the relevant, online literature on the topic and after looking at the market trends and developments, one can notice that there are still concerns with regard to security in IoT products and services. This paper is focusing on a survey on IoT security and aims to highlight the most significant problems related to safety and security in the IoT ecosystems. This survey identifies the general threat and attack vectors against IoT devices while highlighting the flaws and weak points that can lead to breaching the security. Furthermore, this paper presents solutions for remediation of the compromised security, as well as methods for risk mitigation, with prevention and improvement suggestions.

Survey on Filtering Techniques Applied to ECG Signal

This Analysis of ECG signals is crucial for early detection of cardiac ailments. With the sensors becoming more and more predominant and available in all wearable forms for measuring multiple physiological parameters, the importance of algorithms to detect the abnormalities are also gaining importance. The physiological signals are measured through noninvasive means unless very critical and hence are very weak in nature. Bio-amplifiers are applied during signal acquisition. The signals are weak and hence susceptible for the electrical disturbances in the environment. Selection of Filtering techniques is very important and is decided by many factors. This paper is a survey which presents digital filtering techniques the researchers have applied in the last decade. The study reveals the different types FIR/IIR filters the researchers have worked and also the performance metrics adapted by the researchers electronic document is a “live” template and already.

Motion based smart assistant for visually impaired people

Objectives/Aim: The present paper deals with building a smart assistant with the aim of assisting the visually impaired people in mobility with confidence by realizing the nearby obstacles and also implement image processing techniques to recognize people. Methods: We use Raspberry Pi 4 which has increased computing performance and is interfaced to the picamera, GPS and GSM modules. Arduino pro mini with buzzer, ultrasonic sensors and vibration motors are used for obstacle detection. Certain libraries important for image processing are used such as OpenCV, Dlib, face detection (Haar cascades, HOG + Linear SVM, or CNNs), espeak for converting text to speech. Programming was implemented through Python and Arduino compiler. Findings: Analysis was carried out using the proposed system for the blind and visually impaired people who could move around comfortably with confidence and were also able to detect objects and recognize people. Hence the proposed system removes the use for the cumbersome white cane in exchange for small and handy modules which can be used in the form of wearables mounted on shoulders, hands and legs to detect the obstacles from multiple directions and provide for a comfortable wear. It was determined that the Raspberry pi 4 was incapable of running CNN detection for which ideally computer GPU was required, so HOG detection method was used instead. Application/Improvements: This project can be implemented mainly in the commercial field of helping visually impaired people with poor eyesight or being completely blind. Industrial applications can be devised and enhanced like robots and machineries along with Security, Identifying and Tracking. Keywords: Face Recognition; Obstacle Detection; Open Source Computer Vision (OpenCV); Deep Learning; Histogram of Oriented Gradients (HOG); Support Vector Machine (SVM); Convolutional Neural Network (CNN)

Smart Sensing Systems Using Wearable Optoelectronics

A wearable smart sensing system is capable of continuously monitoring biometric information such as respiration, pulse, and body temperature while attached to an arbitrary surface on the user's body to be utilized freely during activities. Research conducted on new materials, fabrication processes, structure of electrodes, and wireless communication technologies has made constant progress in the development of smart sensing systems that use entirely wearable forms of devices to go beyond conventional devices that are based on intrinsically rigid components, such as silicon. Among various platforms that can be used in the development of smart sensing systems, optoelectronics provides innovative sensing functions (e.g., human–machine interfaces and phototherapy) that can be transferred from traditional healthcare to smart healthcare, such as point of care. Optoelectronics are light‐mediated displays that allow a light source to be controlled and a large light spectrum to be detected. Recently, this platform that uses smart and wearable optoelectronic sensing systems has become increasingly advanced to better help human beings treat their diseases through self‐healthcare. Herein, recent advanced technologies in materials, structures, and wireless platforms for smart sensors using wearable optoelectronics are reviewed.

Stretchable Ionics - A Promising Candidate for Upcoming Wearable Devices.

As many devices for human utility aim for fast and convenient communication with users, superb electronic devices are demonstrated to serve as hardware for human-machine interfaces in wearable forms. Wearable devices for daily healthcare and self-diagnosis offer more human-like properties unconstrained by deformation. In this sense, stretchable ionics based on flexible and stretchable hydrogels are on the rise as another means to develop wearable devices for bioapplications for two main reasons: i) ionic currents and choosing the same signal carriers for biological areas, and ii) the adoption of hydrogel ionic conductors, which are intrinsically stretchable materials with biocompatibility. Here, the current status of stretchable ionics and future applications are introduced, whose positive effects can be magnified by stretchable ionics.

Sewable soft shields for the \u03b3-ray radiation

Soft shields are required to protect the human body during a radioactive accident. However, the modulus of most soft shields, such as HDPE and epoxy, is high, thereby making it difficult to process them in wearable forms like gloves and clothes. We synthesized a soft shield based on a hydrogel that is very compliant, stretchable, and biocompatible. The shields were fabricated by integrating γ-ray-shield particles into hydrogels with an interpenetrating network. The soft shields containing 3.33 M of PbO2 exhibited a high attenuation coefficient (0.284 cm−1) and were stretched to 400% without a rupture. Furthermore, the fabricated soft shield can be sewn without a fabric support due to its high energy-dispersion ability. A wearable arm shield for the γ-ray radiation was demonstrated using a direct sewing of the soft-shield materials.