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.