Abstract The detection of surface electromyography (sEMG) signals on the skin has attracted increasing attention because of its ability to monitor muscle conditions in a noninvasive manner and thus possesses great application potential to assess athletic status and training efficiency in real time or to evaluate postoperative muscle rehabilitation conveniently. Here, a flexible wireless sEMG monitoring system that consists of a stretchable sEMG epidermal patch and a flexible printed circuit board to provide real-time evaluation of muscle strength and fatigue is reported. The epidermal patch is designed to have good stretchability and permeability and optimized to ensure a low contact impedance with the skin and minimized background noise for sEMG signal acquisition with high fidelity. Six commonly used time-domain and two frequency-domain features extracted from sEMG signals are systematically analyzed, and a strategy for feature selection and pattern identification is proposed that eventually enables the real-time assessment of muscle strength and fatigue by using an integrated system in a wearable form.
Flexible electronic devices have shown increasingly promising value facilitating our daily lives. However, flexible spintronic devices remain in their infancy. Here, this research demonstrates a type of nonvolatile, low power dissipation, and programmable flexible spin logic device, which is based on the spin-orbit torque in polyimide (PI)/Ta/Pt/Co/Pt heterostructures fabricated via capillary-assisted electrochemical delamination. The magnetization switching ratio is shown to be about 50% for the flexible device and does not change after 100 cycles of bending, indicating the device has stable performance. By designing the path of pulse current, five Boolean logic gates AND, NAND, NOT, NOR, and OR can be realized in an integrated two-element device. Moreover, such peeling-off devices can be successfully transferred to almost any substrate, such as paper and human skin, and maintain high performance. The flexible PI/Ta/Pt/Co/Pt spin logic device serves as logic-in-memory architecture and can be used in wearable electronics.
Triboelectric nanogenerators (TENGs) utilize the phenomena of contact electrification and electrostatic induction to harvest mechanical energy from the environment. A good match between the motion frequency and the circuit characteristic frequency is critical for the effective power generation of a TENG. However, most TENGs have a time-dependent inherent capacitance (TIC-TENG), which hinders an optimal design for efficient energy conversion. Here, we propose a novel structure of a TENG with a constant inherent capacitance (CIC-TENG) and a mathematical model is established to provide analytical expressions of key output parameters of the device, which gives numerical simulation results that are in good agreement with the experimentally obtained results. Figures of merit and an optimization strategy are also given as guidelines for the optimization of material selection, geometry design, etc. Furthermore, a disk-formed CIC-TENG (DCIC-TENG) with polarity-switched triboelectric pairs is constructed to harvest unidirectional mechanical energy continuously, achieving an output power density of 55 mW/m(2). The effects of the motion frequency, the number of electrodes and triboelectric pairs on the charge transfer efficiency of the DCIC-TENG are assessed and a preferred design strategy is given. Finally, the CIC-TENG demonstrates approximately two-fold advantages in power transfer efficiency over the TIC-TENG, and a DCIC-TENG-based self-powered anemometer was fabricated to measure wind speed in real time.
Sign language recognition is of great significance to connect the hearing/speech impaired and non-sign language communities. Compared to isolated word recognition, sentence recognition is more practical in real-world scenarios, but is also more complicated because continuous, high-quality sign data with distinct features must be collected and isolated signs must be identified with high accuracy. Here, we propose a wearable sign language recognition system enabled by a convolutional neural network (CNN) that integrates stretchable strain sensors and inertial measurement units attached to the body to perceive hand postures and movement trajectories. Forty-eight Chinese sign language words commonly used in daily life were collected and used to train the CNN model, and an isolated sign language word recognition accuracy of 95.85% was achieved. For sentence-level sign language recognition, we proposed a method that combines multiple sliding windows and uses correlation analysis to improve the CNN recognition performance, achieving a correct rate of 84% for 50 sign language sentence samples, showing good extendibility.
High-performance stretchable strain sensors are highly desirable for various scenarios, such as health monitoring and human-robot interfaces. Here, we propose a universal strain engineering strategy that introduces an inhomogeneous spatial distribution of stress and promotes crack propagation behavior leading to a critical state between network and channel morphologies, achieving stretchable strain sensors with high sensitivity, a wide working range and good linearity. Approaches for introducing soft-rigid interfaces, enlarging elastic modulus mismatches and matching dimensions have been employed to execute the strategy for network-crack strain sensors with collapsed nanocone cluster structures as representatives. The strain sensors can be tuned to realize a gauge factor of 690.95 in a linear working range of 0–40% (R2 = 0.993) or a gauge factor of 113.70 in a larger linear working range of 0–120% (R2 = 0.999). Intraocular pressure monitoring and dynamic facial asymmetry assessment have been demonstrated based on these sensors to show their great application potential.
Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.
Epidermal electronic systems that simultaneously provide physiological information acquisition, processing, and storage are in high demand for health care/clinical applications. However, these system-level demonstrations using flexible devices are still challenging because of obstacles in device performance, functional module construction, or integration scale. Here, on the basis of carbon nanotubes, we present an epidermal system that incorporates flexible sensors, sensor interface circuits, and an integrated flash memory array to collect physiological information from the human body surface; amplify weak biosignals by high-performance differential amplifiers (voltage gain of 27 decibels, common-mode rejection ratio of >43 decibels, and gain bandwidth product of >22 kilohertz); and store the processed information in the memory array with performance on par with industrial standards (retention time of 108 seconds, program/erase voltages of ±2 volts, and endurance of 106 cycles). The results shed light on the great application potential of epidermal electronic systems in personalized diagnostic and physiological monitoring. A CNT-based epidermal system is proposed for physiological signal capturing, processing, and storage.
Biological nervous systems evolved in nature have marvelous information processing capacities, which have great reference value for modern information technologies. To expand the function of electronic devices with applications in smart health monitoring and treatment, wearable energy-efficient computing, neuroprosthetics, etc., flexible artificial synapses for neuromorphic computing will play a crucial role. Here, carbon nanotube-based ferroelectric synaptic transistors are realized on ultrathin flexible substrates via a low-temperature approach not exceeding 90 °C to grow ferroelectric dielectrics in which the single-pulse, paired-pulse, and repetitive-pulse responses testify to well-mimicked plasticity in artificial synapses. The long-term potentiation and long-term depression processes in the device demonstrate a dynamic range as large as 2000×, and 360 distinguishable conductance states are achieved with a weight increase/decrease nonlinearity of no more than 1 by applying stepped identical pulses. The stability of the device is verified by the almost unchanged performance after the device is kept in ambient conditions without additional passivation for 240 days. An artificial neural network-based simulation is conducted to benchmark the hardware performance of the neuromorphic devices in which a pattern recognition accuracy of 95.24% is achieved.
Transient electronics is an emerging class of electronic devices that can physically degrade or disintegrate after a stable period of service, showing a vast prospect in applications of “green” consumer electronics, hardware-secure devices, medical implants, etc. Complementary metal-oxide–semiconductor (CMOS) technology is dominant in integrated circuit design for its advantages of low static power consumption, high noise immunity, and simple design layout, which also work and are highly preferred for transient electronics. However, the performance of complementary transient electronics is severely restricted by the confined selection of transient materials and compatible fabrication strategies. Here, we report the realization of high-performance transient complementary electronics based on carbon nanotube thin films via a reliable electrostatic doping method. Under a low operating voltage of 2 V, on a 1.5 μm-thick water-soluble substrate made of poly(vinyl alcohol), the width-normalized on-state currents of the p-type and n-type transient thin-film transistors (TFTs) reach 4.5 and 4.7 μA/μm, and the width-normalized transconductances reach 2.8 and 3.7 μS/μm, respectively. Meanwhile, these TFTs show small subthreshold swings no more than 108 mV/dec and current on/off ratios above 106 with good uniformity. Transient CMOS inverters, as basic circuit components, are demonstrated with a voltage gain of 24 and a high noise immunity of 67.4%. Finally, both the degradation of the active components and the disintegration of the functional system are continuously monitored with nontraceable remains after 10 and 5 h, respectively.
Spatiotemporal recognition of multiple mechanical stimuli is essential for electronic skin (e-skin), which can provide more complete and accurate interaction information to enable elaborated functions, such as gesture recognition, object manipulation, and fine tactile discrimination. However, nonspecific sensor response and performance sacrifice for integration limit the perceptual capability of the current systems. Here, we report a bioinspired e-skin that can measure strain, shear and pressure independently with direction information using three-dimensional integrated, mechanically isolated multiple sensors. Novel microstructures of collapsed nanocone clusters, hemi-ellipsoids, and wrinkles are introduced in different sensors to achieve a gauge factor of 6 with a linear working range of 80% (linearity > 0.99) for strain, a sensitivity of 0.1 N−1 for shear force, and a sensitivity of 3.78 kPa−1 for pressure, and all of these sensors possess short response times on the order of 100 ms. The independent, highly sensitive, and fast response of these sensors makes real-time recording and mapping of multiple mechanical stimuli to be achieved. Multi-touch gesture recognition and perception of a red bean (0.065 g) in the hand are demonstrated to illustrate the potential applications in wearables, robotics and bionic prostheses.
High-speed flexible circuits are required in flexible systems to realize real-time information analysis or to construct wireless communication modules for emerging applications. Here, we present scaled carbon nanotube-based thin film transistors (CNT-TFTs) with channel lengths down to 450 nm on 2-mu m-thick parylene substrates, achieving state-of-the-art performances of high on-state current (187.6 mu A mu m(-1)) and large transconductance (123.3 mu S mu m(-1)). Scaling behavior analyses reveal that the enhanced performance introduced by scaling is attributed to channel resistance reduction while the contact resistance (180 +/- 50 k omega per tube) remains unchanged, which is comparable to that achieved in devices on rigid substrates, indicating great potential in ultimate scaled flexible CNT-TFTs with high performance comparable to their counterparts on rigid substrates where contact resistance dominates the performance. Five-stage flexible ring oscillators are built to benchmark the speed of scaled devices, demonstrating a 281 ps stage delay at a low supply voltage of 2.6 V. High-speed flexible circuits are essential in flexible systems for real-time information analysis and wireless communication. Here, flexible circuits are reported with a 281 ps stage delay based on scaled carbon nanotube thin film transistors.