Many individuals suffer from stroke, osteoarthritis, or accidental hand injuries, making hand rehabilitation greatly significant. The current hand rehabilitation therapy requires repetitive task-oriented hand exercises, relying on exoskeleton mechanical gloves integrated with different sensors and actuators. However, these conventional mechanical gloves require wearing heavy mechanical components that need weight-bearing and increase hand burden. Additionally, these devices are usually structurally complex, complicated to operate, and require specialized medical institutions. Here, a Virtual Reality (VR) hand rehabilitation system is developed by integrating deep-learning-assisted electromyography (EMG) recognition and VR human-machine interfaces (HMIs). By applying a wet-adhesive, self-healable, and conductive ionic hydrogel electrode array assisted by deep learning, the system can realize 14 Jebsen hand rehabilitation gestures recognition with an accuracy of 97.9%. The recognized gestures further communicate with the VR platform for real-time interaction in a virtual scenario to accomplish VR hand rehabilitation. Compared with present hand rehabilitation devices, the proposed system enables patients to perform immersive hand exercises in real-life scenarios without the need for hand-worn weights, and offers rehabilitation training without time and location limitations. This system could bring great breakthroughs for the development of a load-free hand rehabilitation system available in home-based therapy.
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Triboelectric nanogenerators (TENGs) stand at the forefront of energy harvesting innovation, transforming mechanical energy into electrical power through triboelectrification and electrostatic induction. This groundbreaking technology addresses the urgent need for sustainable and renewable energy solutions, opening new avenues for self-powered systems. Despite their potential, TENGs face challenges such as material optimization for enhanced triboelectric effects, scalability, and improving conversion efficiency under varied conditions. Durability and environmental stability also pose significant hurdles, necessitating further research towards more resilient systems. Nature inspired TENG designs offer promising solutions by emulating biological processes and structures, such as the energy mechanisms of plants and the textured surfaces of animal skins. This biomimetic approach has led to notable improvements in material properties, structural designs, and overall TENG performance, including enhanced energy conversion efficiency and environmental robustness. The exploration into bio-inspired TENGs has unlocked new possibilities in energy harvesting, self-powered sensing, and wearable electronics, emphasizing reduced energy consumption and increased efficiency through innovative design. This review encapsulates the challenges and advancements in nature inspired TENGs, highlighting the integration of biomimetic principles to overcome current limitations. By focusing on augmented electrical properties, biodegradability, and self-healing capabilities, nature inspired TENGs pave the way for more sustainable and versatile energy solutions.
Neurological electronic skin (E-skin) can process and transmit information in a distributed manner that achieves effective stimuli perception, holding great promise in neuroprosthetics and soft robotics. Neurological E-skin with multifunctional perception abilities can enable robots to precisely interact with the complex surrounding environment. However, current neurological E-skins that possess tactile, thermal, and visual perception abilities are usually prepared with rigid materials, bringing difficulties in realizing biologically synapse-like softness. Here, we report a soft multifunctional neurological E-skin (SMNE) comprised of a poly(3-hexylthiophene) (P3HT) nanofiber polymer semiconductor-based stretchable synaptic transistor and multiple soft artificial sensory receptors, which is capable of effectively perceiving force, thermal, and light stimuli. The stretchable synaptic transistor can convert electrical signals into transient channel currents analogous to the biological excitatory postsynaptic currents. And it also possesses both short-term and long-term synaptic plasticity that mimics the human memory system. By integrating a stretchable triboelectric nanogenerator, a soft thermoelectric device, and an elastic photodetector as artificial receptors, we further developed an SMNE that enables the robot to make precise actions in response to various surrounding stimuli. Compared with traditional neurological E-skin, our SMNE can maintain the softness and adaptability of biological synapses while perceiving multiple stimuli including force, temperature, and light. This SMNE could promote the advancement of E-skins for intelligent robot applications.
Screen sensors are the most commonly used human-machine interfaces in our everyday life, which have been extensively applied in personal electronics like cellphones. Touchless screen sensors are attracting growing interest due to their distinct advantages of high interaction freedom, comfortability, and hand hygiene. However, the material compositions of current touchless screen sensors are rigid and fragile, hardly meeting the needs of wearable and stretchable on-skin electronics development. Additionally, these touchless screen sensors are also restricted by high power consumption, limited gesture types of recognition, and the requirement of light conditions. Here, we report a stretchable on-skin touchless screen sensor (OTSS) enabled by an ionic hydrogel-based triboelectric nanogenerator (TENG). Compared with current touchless screen sensors, the OTSS is stretchable, self-powered, and competent to recognize diverse gestures by making use of charges naturally carried on fingers without the need of sufficient light conditions. An on-skin noncontact screen operating system is further demonstrated on the basis of the OTSS, which could unlock a cellphone interface in touchless operation mode on the human skin. This work for the first time introduces the on-skin touchless concept to screen sensors and offers a direction to develop new-generation screen sensors for future cellphones and personal electronics.
As a stretchable seamless device, electronic skin (E-skin) has drawn enormous interest due to its skin-like sensing capability. Besides the basic perception of force and temperature, multiple perception that is beyond existing functions of human skin is becoming an important direction for E-skin developments. However, the present E-skins for multiple perceptions mainly rely on different sensing materials and heterogeneous integration, resulting in a complex device structure. Additionally, their stretchability is usually achieved by the complicated microstructure design of rigid materials. Here, we report an intrinsically stretchable polymer semiconductor based E-skin with a simple structure for multiple perceptions of force, temperature, and visible light. The E-skin is on the basis of poly(3-hexylthiophene) (P3HT) nanofibers percolated polydimethylsiloxane (PDMS) composite polymer semiconductor, which is fabricated by a facile solution method. The E-skin shows reliable sensing capabilities when it is used to perceive strain, pressure, temperature, and visible light. Based on the E-skin, an intelligent robotic hand sensing and controlling system is further demonstrated. Compared with conventional E-skins for multiple perceptions, this E-skin only has a simple monolayer sensing membrane without the need of combining different sensing materials, heterogeneous integration, and complicated microstructure design. Such a strategy of utilizing intrinsically stretchable polymer semiconductor to create simple structured E-skin for multiple perceptions will promote the development of E-skins in a broad application scenario, such as artificial robotic skins, virtual reality, intelligent gloves, and biointegrated electronics.
Human–machine interfaces (HMIs) are important windows for a human to communicate with the outside world. The current HMI devices such as cellphones, tablets, and computers can be used to help people with aphasia for language expression. However, these conventional HMI devices are not friendly to some particular groups who also lose their abilities of physical movements like in the intensive care unit (ICU) or vegetative patients to realize language expression. Herein, we report a breath-driven triboelectric nanogenerator (TENG) acting as a HMI sensor for language expression through human breathing without voice controls or manual operations. The TENG is integrated within a mask and fabricated via a three-dimensional (3D) printing method. When wearing the mask, the TENG can produce responsive electric signals corresponding to the airflow from breathing, which is capable of recognizing human breathing types with different intensities, lengths, and frequencies. On the basis of the breathing recognition ability, a breathing-based language expressing system is further developed through introducing the Morse code as a communication protocol. Compared with conventional language expressing devices, this system can extract subjective information of a person from breathing behaviors and output corresponding language text, which is not relying on voices or physical movements. This research for the first time introduces the self-powered breathing-based language expressing method to the field of HMI technology by using a 3D printed TENG, and could make HMI interactions become more friendly and fascinating.
Hydraulics provide a unique and widely existed mechanical energy source around us, such as in water or oil pipes, and sewers. Here, a non-contact cylindrical rotating triboelectric nanogenerator (TENG) was developed to harvest the mechanical energy from water flows. Operation of the TENG was based on the non-contact free-rotating between a curved Cu foil and a flexible nanostructured fluorinated ethylene propylene (FEP) polymer film. The free-standing distance between two rotating interfaces avoided abrading of electrode materials. The TENG was able to effectively convert mechanical energy of the water flow into electricity. When driven by water flow, the output voltage and current of the TENG reached 1,670 V and 13.4 μA, respectively. Without any energy storage component, the produced electricity could instantaneously power 12 white light emitting diodes (LEDs) bulbs and a digital timer. This non-contact rotating TENG would provide new opportunities for harvesting energy from many types of hydraulics as a self-sustainable power source for sensing, detection, and protection.