Ultrathin crystalline-silicon-based strain gauges with deep learning algorithms for silent speech interfaces

Recent advances in soft, implantable electronics for dynamic organs

Unlike conventional rigid counterparts, soft and stretchable electronics forms crack- or defect-free conformal interfaces with biological tissues, enabling precise and reliable interventions in diagnosis and treatment of human diseases. Intrinsically soft and elastic materials, and device designs of innovative configurations and structures leads to the emergence of such features, particularly, the mechanical compliance provides seamless integration into continuous movements and deformations of dynamic organs such as the bladder and heart, without disrupting natural physiological functions. This review introduces the development of soft, implantable electronics tailored for dynamic organs, covering various materials, mechanical design strategies, and representative applications for the bladder and heart, and concludes with insights into future directions toward clinically relevant tools.

A data-efficient and easy-to-use lip language interface based on wearable motion capture and speech movement reconstruction

Lip language recognition urgently needs wearable and easy-to-use interfaces for interference-free and high-fidelity lip-reading acquisition and to develop accompanying data-efficient decoder-modeling methods. Existing solutions suffer from unreliable lip reading, are data hungry, and exhibit poor generalization. Here, we propose a wearable lip language decoding technology that enables interference-free and high-fidelity acquisition of lip movements and data-efficient recognition of fluent lip language based on wearable motion capture and continuous lip speech movement reconstruction. The method allows us to artificially generate any wanted continuous speech datasets from a very limited corpus of word samples from users. By using these artificial datasets to train the decoder, we achieve an average accuracy of 92.0% across individuals (n = 7) for actual continuous and fluent lip speech recognition for 93 English sentences, even observing no training burn on users because all training datasets are artificially generated. Our method greatly minimizes users' training/learning load and presents a data-efficient and easy-to-use paradigm for lip language recognition.

Damage-free dry transfer method using stress engineering for high-performance flexible two- and three-dimensional electronics

Advanced transfer printing technologies have enabled the fabrication of high-performance flexible and stretchable devices, revolutionizing many research fields including soft electronics, optoelectronics, bioelectronics and energy devices. Despite previous innovations, challenges remain, such as safety concerns due to toxic chemicals, the expensive equipment, film damage during the transfer process and difficulty in high-temperature processing. Thus a new transfer printing process is needed for the commercialization of high-performance soft electronic devices. Here we propose a damage-free dry transfer printing strategy based on stress control of the deposited thin films. First, stress-controlled metal bilayer films are deposited using direct current magnetron sputtering. Subsequently, mechanical bending is applied to facilitate the release of the metal bilayer by increasing the overall stress. Experimental and simulation studies elucidate the stress evolution mechanisms during the processes. By using this method, we successfully transfer metal thin films and high-temperature-treated oxide thin films onto flexible or stretchable substrates, enabling the fabrication of two-dimensional flexible electronic devices and three-dimensional multifunctional devices.

Deep Learning Model Coupling Wearable Bioelectric and Mechanical Sensors for Refined Muscle Strength Assessment

Muscle strength (MS) is related to our neural and muscle systems, essential for clinical diagnosis and rehabilitation evaluation. Although emerging wearable technology seems promising for MS assessment, problems still exist, including inaccuracy, spatiotemporal differences, and analyzing methods. In this study, we propose a wearable device consisting of myoelectric and strain sensors, synchronously acquiring surface electromyography and mechanical signals at the same spot during muscle activities, and then employ a deep learning model based on temporal convolutional network (TCN) + Transformer (Tcnformer), achieving accurate grading and prediction of MS. Moreover, by combining with deep clustering, named Tcnformer deep cluster (TDC), we further obtain a 25-level classification for MS assessment, refining the conventional 5 levels. Quantification and validation showcase a patient's postoperative recovery from level 3.2 to level 3.6 in the first few days after surgery. We anticipate that this system will importantly advance precise MS assessment, potentially improving relevant clinical diagnosis and rehabilitation outcomes.

E-Tattoos: Toward Functional but Imperceptible Interfacing with Human Skin

The human body continuously emits physiological and psychological information from head to toe. Wearable electronics capable of noninvasively and accurately digitizing this information without compromising user comfort or mobility have the potential to revolutionize telemedicine, mobile health, and both human-machine or human-metaverse interactions. However, state-of-the-art wearable electronics face limitations regarding wearability and functionality due to the mechanical incompatibility between conventional rigid, planar electronics and soft, curvy human skin surfaces. E-Tattoos, a unique type of wearable electronics, are defined by their ultrathin and skin-soft characteristics, which enable noninvasive and comfortable lamination on human skin surfaces without causing obstruction or even mechanical perception. This review article offers an exhaustive exploration of e-tattoos, accounting for their materials, structures, manufacturing processes, properties, functionalities, applications, and remaining challenges. We begin by summarizing the properties of human skin and their effects on signal transmission across the e-tattoo-skin interface. Following this is a discussion of the materials, structural designs, manufacturing, and skin attachment processes of e-tattoos. We classify e-tattoo functionalities into electrical, mechanical, optical, thermal, and chemical sensing, as well as wound healing and other treatments. After discussing energy harvesting and storage capabilities, we outline strategies for the system integration of wireless e-tattoos. In the end, we offer personal perspectives on the remaining challenges and future opportunities in the field.

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human-machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

Bionic artificial skin with a fully implantable wireless tactile sensory system for wound healing and restoring skin tactile function

Tactile function is essential for human life as it enables us to recognize texture and respond to external stimuli, including potential threats with sharp objects that may result in punctures or lacerations. Severe skin damage caused by severe burns, skin cancer, chemical accidents, and industrial accidents damage the structure of the skin tissue as well as the nerve system, resulting in permanent tactile sensory dysfunction, which significantly impacts an individual's daily life. Here, we introduce a fully-implantable wireless powered tactile sensory system embedded artificial skin (WTSA), with stable operation, to restore permanently damaged tactile function and promote wound healing for regenerating severely damaged skin. The fabricated WTSA facilitates (i) replacement of severely damaged tactile sensory with broad biocompatibility, (ii) promoting of skin wound healing and regeneration through collagen and fibrin-based artificial skin (CFAS), and (iii) minimization of foreign body reaction via hydrogel coating on neural interface electrodes. Furthermore, the WTSA shows a stable operation as a sensory system as evidenced by the quantitative analysis of leg movement angle and electromyogram (EMG) signals in response to varying intensities of applied pressures.

Enhancing cardiomyocytes contraction force measuring in drug testing: Integration of a highly sensitive single-crystal silicon strain sensor into SU-8 cantilevers

The development of efficient tools for predicting drug-induced cardiotoxicity in the preclinical phase would greatly benefit the drug development process. This study presents an SU-8 cantilever integrated with a single-crystal silicon strain sensor to enhance force sensitivity in toxicity screening methods based on changes in the contraction force of cardiomyocytes. The proposed cantilever device enables real-time measurements of cardiomyocytes contraction force with high sensitivity, thereby facilitating the assessment of drug cardiotoxicity. The experimental results obtained herein demonstrate the responsiveness of the proposed platform in detecting forces smaller than 0.02 μN with a force sensitivity that is nearly 17 times higher than those of conventional metal-based strain sensors. Moreover, the integration of strain sensors demonstrates the potential for manufacturing cantilever arrays that can be used in high-throughput screening applications. The developed methodology successfully facilitates in vitro culturing of cardiomyocytes and allows for continuous monitoring of their contraction force. The practical applicability of the proposed platform is further validated through cardiotoxicity analysis. The cultured cardiomyocytes are treated with two cardiovascular drugs, namely verapamil (an L-type calcium channel blocker) and isoproterenol (a sympathomimetic drug targeting β1 and β2 adrenergic receptors), to analyze the drug induced effects in the cardiomyocytes.

Decoding silent speech commands from articulatory movements through soft magnetic skin and machine learning

Silent speech interfaces have been pursued to restore spoken communication for individuals with voice disorders and to facilitate intuitive communications when acoustic-based speech communication is unreliable, inappropriate, or undesired. However, the current methodology for silent speech faces several challenges, including bulkiness, obtrusiveness, low accuracy, limited portability, and susceptibility to interferences. In this work, we present a wireless, unobtrusive, and robust silent speech interface for tracking and decoding speech-relevant movements of the temporomandibular joint. Our solution employs a single soft magnetic skin placed behind the ear for wireless and socially acceptable silent speech recognition. The developed system alleviates several concerns associated with existing interfaces based on face-worn sensors, including a large number of sensors, highly visible interfaces on the face, and obtrusive interconnections between sensors and data acquisition components. With machine learning-based signal processing techniques, good speech recognition accuracy is achieved (93.2% accuracy for phonemes, and 87.3% for a list of words from the same viseme groups). Moreover, the reported silent speech interface demonstrates robustness against noises from both ambient environments and users' daily motions. Finally, its potential in assistive technology and human-machine interactions is illustrated through two demonstrations - silent speech enabled smartphone assistants and silent speech enabled drone control.

Recent development of piezoelectric biosensors for physiological signal detection and machine learning assisted cardiovascular disease diagnosis

As cardiovascular disease stands as a global primary cause of mortality, there has been an urgent need for continuous and real-time heart monitoring to effectively identify irregular heart rhythms and to offer timely patient alerts. However, conventional cardiac monitoring systems encounter challenges due to inflexible interfaces and discomfort during prolonged monitoring. In this review article, we address these issues by emphasizing the recent development of the flexible, wearable, and comfortable piezoelectric passive sensor assisted by machine learning technology for diagnosis. This innovative device not only harmonizes with the dynamic mechanical properties of human skin but also facilitates continuous and real-time collection of physiological signals. Addressing identified challenges and constraints, this review provides insights into recent advances in piezoelectric cardiac sensors, from devices to circuit systems. Furthermore, this review delves into the integration of machine learning technologies, showcasing their pivotal role in facilitating continuous and real-time assessment of cardiac status. The synergistic combination of flexible piezoelectric sensor design and machine learning holds substantial potential in automating the detection of cardiac irregularities with minimal human intervention. This transformative approach has the power to revolutionize patient care paradigms.

Stretchable and Skin-Mountable Temperature Sensor Array Using Reduction-Controlled Graphene Oxide for Dermatological Thermography

Since thermometry of human skin is critical information that provides important aspects of human health and physiology, accurate and continuous temperature measurement is required for the observation of physical abnormalities. However, conventional thermometers are uncomfortable because of their bulky and heavy features. In this work, we fabricated a thin, stretchable array-type temperature sensor using graphene-based materials. Furthermore, we controlled the degree of graphene oxide reduction and enhanced the temperature sensitivity. The sensor exhibited an excellent sensitivity of 2.085% °C^-1. The overall device was designed in a wavy meander shape to provide stretchability for the device so that precise detection of skin temperature could be performed. Furthermore, polyimide film was coated to secure the chemical and mechanical stabilities of the device. The array-type sensor enabled spatial heat mapping with high resolution. Finally, we introduced some practical applications of skin temperature sensing, suggesting the possibility of skin thermography and healthcare monitoring.

Fluorescent-based biodegradable microneedle sensor array for tether-free continuous glucose monitoring with smartphone application

Continuous glucose monitoring (CGM) allows patients with diabetes to manage critical disease effectively and autonomously and prevent exacerbation. A painless, wireless, compact, and minimally invasive device that can provide CGM is essential for monitoring the health conditions of freely moving patients with diabetes. Here, we propose a glucose-responsive fluorescence-based highly sensitive biodegradable microneedle CGM system. These ultrathin and ultralight microneedle sensor arrays continuously and precisely monitored glucose concentration in the interstitial fluid with minimally invasive, pain-free, wound-free, and skin inflammation-free outcomes at various locations and thicknesses of the skin. Bioresorbability in the body without a need for device removal after use was a key characteristic of the microneedle glucose sensor. We demonstrated the potential long-term use of the bioresorbable device by applying the tether-free CGM system, thus confirming the successful detection of glucose levels based on changes in fluorescence intensity. In addition, this microneedle glucose sensor with a user-friendly designed home diagnosis system using mobile applications and portable accessories offers an advance in CGM and its applicability to other bioresorbable, wearable, and implantable monitoring device technology.

Fabrication of gold-doped crystalline-silicon nanomembrane-based wearable temperature sensor

Wearable temperature sensors with high thermal sensitivity are required for precise and continuous body temperature monitoring. Here, we present a protocol for fabricating a thin, stretchable, and ultrahigh thermal-sensitive wearable sensor based on gold-doped crystalline-silicon nanomembrane (SiNM). We provide detailed steps of gold doping technique to SiNM and fabrication processes for gold-doped crystalline-SiNM based wearable temperature sensor. For complete details on the use and execution of this protocol, please refer to Sang et al. (2022).1.

Soft Wireless Headband Bioelectronics and Electrooculography for Persistent Human-Machine Interfaces

Recent advances in wearable technologies have enabled ways for people to interact with external devices, known as human-machine interfaces (HMIs). Among them, electrooculography (EOG), measured by wearable devices, is used for eye movement-enabled HMI. Most prior studies have utilized conventional gel electrodes for EOG recording. However, the gel is problematic due to skin irritation, while separate bulky electronics cause motion artifacts. Here, we introduce a low-profile, headband-type, soft wearable electronic system with embedded stretchable electrodes, and a flexible wireless circuit to detect EOG signals for persistent HMIs. The headband with dry electrodes is printed with flexible thermoplastic polyurethane. Nanomembrane electrodes are prepared by thin-film deposition and laser cutting techniques. A set of signal processing data from dry electrodes demonstrate successful real-time classification of eye motions, including blink, up, down, left, and right. Our study shows that the convolutional neural network performs exceptionally well compared to other machine learning methods, showing 98.3% accuracy with six classes: the highest performance till date in EOG classification with only four electrodes. Collectively, the real-time demonstration of continuous wireless control of a two-wheeled radio-controlled car captures the potential of the bioelectronic system and the algorithm for targeting various HMI and virtual reality applications.

Machine Learning-Enhanced Flexible Mechanical Sensing

To realize a hyperconnected smart society with high productivity, advances in flexible sensing technology are highly needed. Nowadays, flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device's software. Significant research efforts have been devoted to improving materials, sensing mechanism, and configurations of flexible sensing systems in a quest to fulfill the requirements of future technology. Meanwhile, advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of sensors. Machine learning (ML) as an important branch of artificial intelligence can efficiently handle such complex data, which can be multi-dimensional and multi-faceted, thus providing a powerful tool for easy interpretation of sensing data. In this review, the fundamental working mechanisms and common types of flexible mechanical sensors are firstly presented. Then how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated, which includes health monitoring, human-machine interfaces, object/surface recognition, pressure prediction, and human posture/motion identification. Finally, the advantages, challenges, and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are discussed. These will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.

Bioinspired Dual-Mode Stretchable Strain Sensor Based on Magnetic Nanocomposites for Strain/Magnetic Discrimination

Recently, flexible stretchable sensors have been gaining attention for their excellent adaptability for electronic skin applications. However, the preparation of stretchable strain sensors that achieve dual-mode sensing while still retaining ultra-low detection limit of strain, high sensitivity, and low cost is a pressing task. Herein, a high-performance dual-mode stretchable strain sensor (DMSSS) based on biomimetic scorpion foot slit microstructures and multi-walled carbon nanotubes (MWCNTs)/graphene (GR)/silicone rubber (SR)/Fe₃ O₄ nanocomposites is proposed, which can accurately sense strain and magnetic stimuli. The DMSSS exhibits a large strain detection range (≈160%), sensitivity up to 100.56 (130-160%), an ultra-low detection limit of strain (0.16% strain), and superior durability (9000 cycles of stretch/release). The sensor can accurately recognize sign language movement, as well as realize object proximity information perception and whole process information monitoring. Furthermore, human joint movements and micro-expressions can be monitored in real-time. Therefore, the DMSSS of this work opens up promising prospects for applications in sign language pose recognition, non-contact sensing, human-computer interaction, and electronic skin.