A soft neuroprosthetic hand providing simultaneous myoelectric control and tactile feedback

Porous Magnetic Soft Grippers for Fast and Gentle Grasping of Delicate Living Objects

Magnetic soft grippers have attracted intensive interest due to their untethered controllability, rapid response, and biological safety. However, manipulating living objects requires a simultaneous increase in shape adaptability and gripping force, which are typically mutually exclusive. Increasing the magnetic particle content enhances the magnetic strength but also increases the elastic modulus, leading to low adaptability and high impact force. Here, a porous magnetic soft gripper (PMSG) is developed by integrating a porous structure into a magnetic silicone elastomer. The design of porous hard magnetic composite is characterized by high magnetization, low modulus, and rough surface. It offers the PMSG good compliance, high gripping force, and low impact force at fast gripping. The PMSG is capable of performing a variety of tasks, including the fast and gentle grasping of delicate living objects. The study provides insight into the design of novel magnetic grippers and may offer a promising outlook for biomedical or scientific applications in the manipulation of delicate organisms.

Octopus-Inspired Underwater Soft Robotic Gripper with Crawling and Swimming Capabilities

Can a robotic gripper only operate when attached to a robotic arm? The application space of the traditional gripper is limited by the robotic arm. Giving robot grippers the ability to move will expand their range of applications. Inspired by rich behavioral repertoire observed in octopus, we implement an integrated multifunctional soft robotic gripper with 6 independently controlled Arms. It can execute 8 different gripping actions for different objects, such as irregular rigid/soft objects, elongated objects with arbitrary orientation, and plane/curved objects with larger sizes than the grippers. Moreover, the soft gripper can realize omnidirectional crawling and swimming by itself. The soft gripper can perform highly integrated tasks of releasing, crawling, swimming, grasping, and retrieving objects in a confined underwater environment. Experimental results demonstrate that the integrated capabilities of multimodal adaptive grasping and omnidirectional motions enable dexterous manipulations that traditional robotic arms cannot achieve. The soft gripper may apply to highly integrated and labor-intensive tasks in unstructured underwater environments, including ocean litter collecting, capture fishery, and archeological exploration.

Bioelectronics for electrical stimulation: materials, devices and biomedical applications

Bioelectronics is a hot research topic, yet an important tool, as it facilitates the creation of advanced medical devices that interact with biological systems to effectively diagnose, monitor and treat a broad spectrum of health conditions. Electrical stimulation (ES) is a pivotal technique in bioelectronics, offering a precise, non-pharmacological means to modulate and control biological processes across molecular, cellular, tissue, and organ levels. This method holds the potential to restore or enhance physiological functions compromised by diseases or injuries by integrating sophisticated electrical signals, device interfaces, and designs tailored to specific biological mechanisms. This review explains the mechanisms by which ES influences cellular behaviors, introduces the essential stimulation principles, discusses the performance requirements for optimal ES systems, and highlights the representative applications. From this review, we can realize the potential of ES based bioelectronics in therapy, regenerative medicine and rehabilitation engineering technologies, ranging from tissue engineering to neurological technologies, and the modulation of cardiovascular and cognitive functions. This review underscores the versatility of ES in various biomedical contexts and emphasizes the need to adapt to complex biological and clinical landscapes it addresses.

Soft Crawling Microrobot Based on Flexible Optoelectronics Enabling Autonomous Phototaxis in Terrestrial and Aquatic Environments

Many organisms move directly toward light for prey hunting or navigation, which is called phototaxis. Mimicking this behavior in robots is crucially important in the energy industry and environmental exploration. However, the phototaxis robots with rigid bodies and sensors still face challenges in adapting to unstructured environments, and the soft phototaxis robots often have high requirements for light sources with limited locomotion performance. Here, we report a 3.5 g soft microrobot that can perceive the azimuth angle of light sources and exhibit rapid phototaxis locomotion autonomously enabled by three-dimensional flexible optoelectronics and compliant shape memory alloy (SMA) actuators. The optoelectronics is assembled from a planar patterned flexible circuit with miniature photodetectors, introducing the self-occlusion to light, resulting in high sensing ability (error < 3.5°) compared with the planar counterpart. The actuator produces a straightening motion driven by an SMA wire and is then returned to a curled shape by a prestretched elastomer layer. The actuator exhibits rapid actuation within 0.1 s, a significant degree of deformation (curvature change of ∼87 m-1) and a blocking force of ∼0.4 N, which is 68 times its own weight. Finally, we demonstrated the robot is capable of autonomously crawling toward a moving light source in a hybrid aquatic-terrestrial environment without human intervention. We envision that our microrobot could be widely used in autonomous light tracking applications.

An intelligent spinal soft robot with self-sensing adaptability

Self-sensing adaptability is a high-level intelligence in living creatures and is highly desired for their biomimetic soft robots for efficient interaction with the surroundings. Self-sensing adaptability can be achieved in soft robots by the integration of sensors and actuators. However, current strategies simply assemble discrete sensors and actuators into one robotic system and, thus, dilute their synergistic and complementary connections, causing low-level adaptability and poor decision-making capability. Here, inspired by vertebrate animals supported by highly evolved backbones, we propose a concept of a bionic spine that integrates sensing and actuation into one shared body based on the reversible piezoelectric effect and a decoupling mechanism to extract the environmental feedback. We demonstrate that the soft robots equipped with the bionic spines feature locomotion speed improvements between 39.5% and 80% for various environmental terrains. More importantly, it can also enable the robots to accurately recognize and actively adapt to changing environments with obstacle avoidance capability by learning-based gait adjustments. We envision that the proposed bionic spine could serve as a building block for locomotive soft robots toward more intelligent machine-environment interactions in the future.

A distributed, high-channel-count, implanted bidirectional system for restoration of somatosensation and myoelectric control

Objective. We intend to chronically restore somatosensation and provide high-fidelity myoelectric control for those with limb loss via a novel, distributed, high-channel-count, implanted system.Approach.We have developed the implanted Somatosensory Electrical Neurostimulation and Sensing (iSens®) system to support peripheral nerve stimulation through up to 64, 96, or 128 electrode contacts with myoelectric recording from 16, 8, or 0 bipolar sites, respectively. The rechargeable central device has Bluetooth® wireless telemetry to communicate to external devices and wired connections for up to four implanted satellite stimulation or recording devices. We characterized the stimulation, recording, battery runtime, and wireless performance and completed safety testing to support its use in human trials.Results.The stimulator operates as expected across a range of parameters and can schedule multiple asynchronous, interleaved pulse trains subject to total charge delivery limits. Recorded signals in saline show negligible stimulus artifact when 10 cm from a 1 mA stimulating source. The wireless telemetry range exceeds 1 m (direction and orientation dependent) in a saline torso phantom. The bandwidth supports 100 Hz bidirectional update rates of stimulation commands and data features or streaming select full bandwidth myoelectric signals. Preliminary first-in-human data validates the bench testing result.Significance.We developed, tested, and clinically implemented an advanced, modular, fully implanted peripheral stimulation and sensing system for somatosensory restoration and myoelectric control. The modularity in electrode type and number, including distributed sensing and stimulation, supports a wide variety of applications; iSens® is a flexible platform to bring peripheral neuromodulation applications to clinical reality. ClinicalTrials.gov ID NCT04430218.

DistaNet: grasp-specific distance biofeedback promotes the retention of myoelectric skills

Objective.An active myoelectric interface responds to the user's muscle signals to enable movements. Machine learning can decode user intentions from myoelectric signals. However, machine learning-based interface control lacks continuous, intuitive feedback about task performance, needed to facilitate the acquisition and retention of myoelectric control skills.Approach.We propose DistaNet as a neural network-based framework that extracts smooth, continuous, and low-dimensional signatures of the hand grasps from multi-channel myoelectric signals and provides grasp-specific biofeedback to the users.Main results.Experimental results show its effectiveness in decoding user gestures and providing biofeedback, helping users retain the acquired motor skills.Significance.We demonstrates myoelectric skill retention in a pattern recognition setting for the first time.

A Dual-Modal Hybrid Gripper with Wide Tunable Contact Stiffness Range and High Compliance for Adaptive and Wide-Range Grasping Objects with Diverse Fragilities

The difficulties of traditional rigid/soft grippers in meeting the increasing performance expectations (e.g., high grasping adaptability and wide graspable objects range) of a single robotic gripper have given birth to numerous soft-rigid coupling grippers with promising performance. However, it is still hard for these hybrid grippers to adaptively grasp various objects with diverse fragilities intact, such as incense ash and orange, due to their limited contact stiffness adjustable range and compliance. To solve these challenging issues, herein, we propose a dual-modal hybrid gripper, whose fingers contain a detachable elastomer-coated flexible sheet that is restrained by a moving frame as a teardrop shape. The gripper's two modes switched by controlling the moving frame position can selectively highlight the low contact stiffness and excellent compliance of the teardrop-shaped flexible sheets and the high contact stiffness of the moving frames. Moreover, the contact stiffness of the teardrop-shaped sheets can be wide-range adjusted by online controlling the moving frame position and offline replacing the sheets with different thicknesses. The compliance of the teardrop-shaped sheets also proves to be excellent compared with an Ecoflex 10 fingertip with the same profile. Such a gripper with wide-range tunable contact stiffness and high compliance demonstrates excellent grasping adaptability (e.g., it can safely grasp several fragile strawberries with a maximum size difference of 18 mm, a strawberry with a left/right offset of 3 cm, and a strawberry in two different lying poses) and wide-range graspable objects (from 0.1 g super fragile cigarette ashes to 5.1 kg dumbbell).

Teleoperation of an Anthropomorphic Robot Hand with a Metamorphic Palm and Tunable-Stiffness Soft Fingers

Teleoperation in soft robotics can endow soft robots with the ability to perform complex tasks through human-robot interaction. In this study, we propose a teleoperated anthropomorphic soft robot hand with variable degrees of freedom (DOFs) and a metamorphic palm. The soft robot hand consists of four pneumatic-actuated fingers, which can be heated to tune stiffness. A metamorphic mechanism was actuated to morph the hand palm by servo motors. The human fingers' DOF, gesture, and muscle stiffness were collected and mapped to the soft robotic hand through the sensory feedback from surface electromyography devices on the jib. The results show that the proposed soft robot hand can generate a variety of anthropomorphic configurations and can be remotely controlled to perform complex tasks such as primitively operating the cell phone and placing the building blocks. We also show that the soft hand can grasp a target through the slit by varying the DOFs and stiffness in a trail.

A Soft Collaborative Robot for Contact-based Intuitive Human Drag Teaching

Soft material-based robots, known for their safety and compliance, are expected to play an irreplaceable role in human-robot collaboration. However, this expectation is far from real industrial applications due to their complex programmability and poor motion precision, brought by the super elasticity and large hysteresis of soft materials. Here, a soft collaborative robot (Soft Co-bot) with intuitive and easy programming by contact-based drag teaching, and also with exceptional motion repeatability (< 0.30% of body length) and ultra-low hysteresis (< 2.0%) is reported. Such an unprecedented capability is achieved by a biomimetic antagonistic design within a pneumatic soft robot, in which cables are threaded to servo motors through tension sensors to form a self-sensing system, thus providing both precise actuation and dragging-aware collaboration. Hence, the Soft Co-bots can be first taught by human drag and then precisely repeat various tasks on their own, such as electronics assembling, machine tool installation, etc. The proposed Soft Co-bots exhibit a high potential for safe and intuitive human-robot collaboration in unstructured environments, promoting the immediate practical application of soft robots.

Bioinspired Bidirectional Stiffening Soft Actuators Enable Versatile and Robust Grasping

The bending stiffness modulation mechanism for soft grippers has gained considerable attention to improve grasping versatility, capacity, and stability. However, lateral stability is usually ignored or hard to achieve at the same time with good bending stiffness modulation performance. Therefore, this article presents a bioinspired bidirectional stiffening soft actuator (BISA), enabling compliant and stable performance. BISA combines the air tendon actuation (ATA) and a bone-like structure (BLS). The ATA is the main actuation of the BISA, and the bending stiffness can be modulated with a maximum stiffness of about 0.7 N/mm and a maximum magnification of three times when the bending angle is 45°. Inspired by the morphological structure of the phalanx, the lateral stiffness can be modulated by changing the pulling force of the BLS. The actuator with BLSs can improve the lateral stiffness by about 3.9 times compared to the one without BLSs. The maximum lateral stiffness can reach 0.46 N/mm. And the lateral stiffness can be modulated by decoupling about 1.3 times (e.g., from 0.35 to 0.46 N/mm when the bending angle is 45°). The test results show that the influence of the rigid structures on bending is small with about 1.5 mm maximum position errors of the distal point of the actuator in different pulling forces. The advantages brought by the proposed method enable versatile four-finger grasping. The performance of this gripper is characterized and demonstrated on multiscale, multiweight, and multimodal grasping tasks.

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.

Implantable Resistive Strain Sensor-Decorated Colloidal Crystal Hydrogel Catheter for Intestinal Tract Pressure Sensing

In the quest to develop advanced monitoring systems for intestinal peristaltic stress, this study introduces a groundbreaking approach inspired by nature's sensory networks. By the integration of novel materials and innovative manufacturing techniques, a multifunctional Janus hydrogel patch has been engineered. This unique patch not only demonstrates superior stress-sensing capabilities in the intricate intestinal environment but also enables adhesion to wet tissue surfaces. This achievement opens new avenues for real-time physiological monitoring and potential therapeutic interventions in the realm of gastrointestinal health.

Graph-Driven Simultaneous and Proportional Estimation of Wrist Angle and Grasp Force via High-Density EMG

Myoelectric prostheses are generally unable to accurately control the position and force simultaneously, prohibiting natural and intuitive human-machine interaction. This issue is attributed to the limitations of myoelectric interfaces in effectively decoding multi-degree-of-freedom (multi-DoF) kinematic and kinetic information. We thus propose a novel multi-task, spatial-temporal model driven by graphical high-density electromyography (HD-EMG) for simultaneous and proportional control of wrist angle and grasp force. Twelve subjects were recruited to perform three multi-DoF movements, including wrist pronation/supination, wrist flexion/extension, and wrist abduction/adduction while varying grasp force. Experimental results demonstrated that the proposed model outperformed five baseline models, with the normalized root mean square error of 13.2% and 9.7% and the correlation coefficient of 89.6% and 91.9% for wrist angle and grasp force estimation, respectively. In addition, the proposed model still maintained comparable accuracy even with a significant reduction in the number of HD-EMG electrodes. To the best of our knowledge, this is the first study to achieve simultaneous and proportional wrist angle and grasp force control via HD-EMG and has the potential to empower prostheses users to perform a broader range of tasks with greater precision and control, ultimately enhancing their independence and quality of life.

Closed-Loop Wearable Device Network of Intrinsically-Controlled, Bilateral Coordinated Functional Electrical Stimulation for Stroke

Innovative functional electrical stimulation has demonstrated effectiveness in enhancing daily walking and rehabilitating stroke patients with foot drop. However, its lack of precision in stimulating timing, individual adaptivity, and bilateral symmetry, resulted in diminished clinical efficacy. Therefore, a closed-loop wearable device network of intrinsically controlled functional electrical stimulation (CI-FES) system is proposed, which utilizes the personal surface myoelectricity, derived from the intrinsic neuro signal, as the switch to activate/deactivate the stimulation on the affected side. Simultaneously, it decodes the myoelectricity signal of the patient's healthy side to adjust the stimulation intensity, forming an intrinsically controlled loop with the inertial measurement units. With CI-FES assistance, patients' walking ability significantly improved, evidenced by the shift in ankle joint angle mean and variance from 105.53° and 28.84 to 102.81° and 17.71, and the oxyhemoglobin concentration tested by the functional near-infrared spectroscopy. In long-term CI-FES-assisted clinical testing, the discriminability in machine learning classification between patients and healthy individuals gradually decreased from 100% to 92.5%, suggesting a remarkable recovery tendency, further substantiated by performance on the functional movement scales. The developed CI-FES system is crucial for contralateral-hemiplegic stroke recovery, paving the way for future closed-loop stimulation systems in stroke rehabilitation is anticipated.

Multimodal Soft Robotic Actuation and Locomotion

Diverse and adaptable modes of complex motion observed at different scales in living creatures are challenging to reproduce in robotic systems. Achieving dexterous movement in conventional robots can be difficult due to the many limitations of applying rigid materials. Robots based on soft materials are inherently deformable, compliant, adaptable, and adjustable, making soft robotics conducive to creating machines with complicated actuation and motion gaits. This review examines the mechanisms and modalities of actuation deformation in materials that respond to various stimuli. Then, strategies based on composite materials are considered to build toward actuators that combine multiple actuation modes for sophisticated movements. Examples across literature illustrate the development of soft actuators as free-moving, entirely soft-bodied robots with multiple locomotion gaits via careful manipulation of external stimuli. The review further highlights how the application of soft functional materials into robots with rigid components further enhances their locomotive abilities. Finally, taking advantage of the shape-morphing properties of soft materials, reconfigurable soft robots have shown the capacity for adaptive gaits that enable transition across environments with different locomotive modes for optimal efficiency. Overall, soft materials enable varied multimodal motion in actuators and robots, positioning soft robotics to make real-world applications for intricate and challenging tasks.

Integrated Actuation and Sensing: Toward Intelligent Soft Robots

Soft robotics has received substantial attention due to its remarkable deformability, making it well-suited for a wide range of applications in complex environments, such as medicine, rescue operations, and exploration. Within this domain, the interaction of actuation and sensing is of utmost importance for controlling the movements and functions of soft robots. Nonetheless, current research predominantly focuses on isolated actuation and sensing capabilities, often neglecting the critical integration of these 2 domains to achieve intelligent functionality. In this review, we present a comprehensive survey of fundamental actuation strategies and multimodal actuation while also delving into advancements in proprioceptive and haptic sensing and their fusion. We emphasize the importance of integrating actuation and sensing in soft robotics, presenting 3 integration methodologies, namely, sensor surface integration, sensor internal integration, and closed-loop system integration based on sensor feedback. Furthermore, we highlight the challenges in the field and suggest compelling directions for future research. Through this comprehensive synthesis, we aim to stimulate further curiosity among researchers and contribute to the development of genuinely intelligent soft robots.

Multimodal Sensors Enabled Autonomous Soft Robotic System with Self-Adaptive Manipulation

Human hands are amazingly skilled at recognizing and handling objects of different sizes and shapes. To date, soft robots rarely demonstrate autonomy equivalent to that of humans for fine perception and dexterous operation. Here, an intelligent soft robotic system with autonomous operation and multimodal perception ability is developed by integrating capacitive sensors with triboelectric sensor. With distributed multiple sensors, our robot system can not only sense and memorize multimodal information but also enable an adaptive grasping method for robotic positioning and grasp control, during which the multimodal sensory information can be captured sensitively and fused at feature level for crossmodally recognizing objects, leading to a highly enhanced recognition capability. The proposed system, combining the performance and physical intelligence of biological systems (i.e., self-adaptive behavior and multimodal perception), will greatly advance the integration of soft actuators and robotics in many fields.

Anisotropy in magnetic materials for sensors and actuators in soft robotic systems

The field of soft intelligent robots has rapidly developed, revealing extensive potential of these robots for real-world applications. By mimicking the dexterities of organisms, robots can handle delicate objects, access remote areas, and provide valuable feedback on their interactions with different environments. For autonomous manipulation of soft robots, which exhibit nonlinear behaviors and infinite degrees of freedom in transformation, innovative control systems integrating flexible and highly compliant sensors should be developed. Accordingly, sensor-actuator feedback systems are a key strategy for precisely controlling robotic motions. The introduction of material magnetism into soft robotics offers significant advantages in the remote manipulation of robotic operations, including touch or touchless detection of dynamically changing shapes and positions resulting from the actuations of robots. Notably, the anisotropies in the magnetic nanomaterials facilitate the perception and response with highly selective, directional, and efficient ways used for both sensors and actuators. Accordingly, this review provides a comprehensive understanding of the origins of magnetic anisotropy from both intrinsic and extrinsic factors and summarizes diverse magnetic materials with enhanced anisotropy. Recent developments in the design of flexible sensors and soft actuators based on the principle of magnetic anisotropy are outlined, specifically focusing on their applicabilities in soft robotic systems. Finally, this review addresses current challenges in the integration of sensors and actuators into soft robots and offers promising solutions that will enable the advancement of intelligent soft robots capable of efficiently executing complex tasks relevant to our daily lives.

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.

Recent Advances in Tactile Sensory Systems: Mechanisms, Fabrication, and Applications

Flexible electronics is a cutting-edge field that has paved the way for artificial tactile systems that mimic biological functions of sensing mechanical stimuli. These systems have an immense potential to enhance human-machine interactions (HMIs). However, tactile sensing still faces formidable challenges in delivering precise and nuanced feedback, such as achieving a high sensitivity to emulate human touch, coping with environmental variability, and devising algorithms that can effectively interpret tactile data for meaningful interactions in diverse contexts. In this review, we summarize the recent advances of tactile sensory systems, such as piezoresistive, capacitive, piezoelectric, and triboelectric tactile sensors. We also review the state-of-the-art fabrication techniques for artificial tactile sensors. Next, we focus on the potential applications of HMIs, such as intelligent robotics, wearable devices, prosthetics, and medical healthcare. Finally, we conclude with the challenges and future development trends of tactile sensors.

High-Performance Hydrogel Sensors Enabled Multimodal and Accurate Human-Machine Interaction System for Active Rehabilitation

Human-machine interaction (HMI) technology shows an important application prospect in rehabilitation medicine, but it is greatly limited by the unsatisfactory recognition accuracy and wearing comfort. Here, this work develops a fully flexible, conformable, and functionalized multimodal HMI interface consisting of hydrogel-based sensors and a self-designed flexible printed circuit board. Thanks to the component regulation and structural design of the hydrogel, both electromyogram (EMG) and forcemyography (FMG) signals can be collected accurately and stably, so that they are later decoded with the assistance of artificial intelligence (AI). Compared with traditional multichannel EMG signals, the multimodal human-machine interaction method based on the combination of EMG and FMG signals significantly improves the efficiency of human-machine interaction by increasing the information entropy of the interaction signals. The decoding accuracy of the interaction signals from only two channels for different gestures reaches 91.28%. The resulting AI-powered active rehabilitation system can control a pneumatic robotic glove to assist stroke patients in completing movements according to the recognized human motion intention. Moreover, this HMI interface is further generalized and applied to other remote sensing platforms, such as manipulators, intelligent cars, and drones, paving the way for the design of future intelligent robot systems.

Soft Sensors and Actuators for Wearable Human-Machine Interfaces

Haptic human-machine interfaces (HHMIs) combine tactile sensation and haptic feedback to allow humans to interact closely with machines and robots, providing immersive experiences and convenient lifestyles. Significant progress has been made in developing wearable sensors that accurately detect physical and electrophysiological stimuli with improved softness, functionality, reliability, and selectivity. In addition, soft actuating systems have been developed to provide high-quality haptic feedback by precisely controlling force, displacement, frequency, and spatial resolution. In this Review, we discuss the latest technological advances of soft sensors and actuators for the demonstration of wearable HHMIs. We particularly focus on highlighting material and structural approaches that enable desired sensing and feedback properties necessary for effective wearable HHMIs. Furthermore, promising practical applications of current HHMI technology in various areas such as the metaverse, robotics, and user-interactive devices are discussed in detail. Finally, this Review further concludes by discussing the outlook for next-generation HHMI technology.

Enhancing the Versatility and Performance of Soft Robotic Grippers, Hands, and Crawling Robots Through Three-Dimensional-Printed Multifunctional Buckling Joints

Soft robotic grippers and hands offer adaptability, lightweight construction, and enhanced safety in human-robot interactions. In this study, we introduce vacuum-actuated soft robotic finger joints to overcome their limitations in stiffness, response, and load-carrying capability. Our design-optimized through parametric design and three-dimensional (3D) printing-achieves high stiffness using vacuum pressure and a buckling mechanism for large bending angles (>90°) and rapid response times (0.24 s). We develop a theoretical model and nonlinear finite-element simulations to validate the experimental results and provide valuable insights into the underlying mechanics and visualization of the deformation and stress field. We showcase versatile applications of the buckling joints: a three-finger gripper with a large lifting ratio (∼96), a five-finger robotic hand capable of replicating human gestures and adeptly grasping objects of various characteristics in static and dynamic scenarios, and a planar-crawling robot carrying loads 30 times its weight at 0.89 body length per second (BL/s). In addition, a jellyfish-inspired robot crawls in circular pipes at 0.47 BL/s. By enhancing soft robotic grippers' functionality and performance, our study expands their applications and paves the way for innovation through 3D-printed multifunctional buckling joints.

Bioinspired Stimuli-Responsive Materials for Soft Actuators

Biological species can walk, swim, fly, jump, and climb with fast response speeds and motion complexity. These remarkable functions are accomplished by means of soft actuation organisms, which are commonly composed of muscle tissue systems. To achieve the creation of their biomimetic artificial counterparts, various biomimetic stimuli-responsive materials have been synthesized and developed in recent decades. They can respond to various external stimuli in the form of structural or morphological transformations by actively or passively converting input energy into mechanical energy. They are the core element of soft actuators for typical smart devices like soft robots, artificial muscles, intelligent sensors and nanogenerators. Significant progress has been made in the development of bioinspired stimuli-responsive materials. However, these materials have not been comprehensively summarized with specific actuation mechanisms in the literature. In this review, we will discuss recent advances in biomimetic stimuli-responsive materials that are instrumental for soft actuators. Firstly, different stimuli-responsive principles for soft actuators are discussed, including fluidic, electrical, thermal, magnetic, light, and chemical stimuli. We further summarize the state-of-the-art stimuli-responsive materials for soft actuators and explore the advantages and disadvantages of using electroactive polymers, magnetic soft composites, photo-thermal responsive polymers, shape memory alloys and other responsive soft materials. Finally, we provide a critical outlook on the field of stimuli-responsive soft actuators and emphasize the challenges in the process of their implementation to various industries.

Journey from human hands to robot hands: biological inspiration of anthropomorphic robotic manipulators

The development of robotic hands that can replicate the complex movements and dexterity of the human hand has been a longstanding challenge for scientists and engineers. A human hand is capable of not only delicate operation but also crushing with power. For performing tasks alongside and in place of humans, an anthropomorphic manipulator design is considered the most advanced implementation, because it is able to follow humans' examples and use tools designed for people. In this article, we explore the journey from human hands to robot hands, tracing the historical advancements and current state-of-the-art in hand manipulator development. We begin by investigating the anatomy and function of the human hand, highlighting the bone-tendon-muscle structure, skin properties, and motion mechanisms. We then delve into the field of robotic hand development, focusing on highly anthropomorphic designs. Finally, we identify the requirements and directions for achieving the next level of robotic hand technology.

Sensitive, Robust, Wide-Range, and High-Consistency Capacitive Tactile Sensors with Ordered Porous Dielectric Microstructures

Flexible capacitive tactile sensors show great promise in personalized healthcare monitoring and human-machine interfaces, but their practical application is normally hindered because they rarely possess the required comprehensive performance, that is, high pressure sensitivity and fast response within a broad pressure range, high structure robustness, performance consistency, etc. This paper aims to engineer flexible capacitive pressure sensors with highly ordered porous dielectric microstructures and a 3D-printing-based fully solution-processable fabrication process. The proposed dielectric layer with uniformly distributed interior microporous can not only increase its compressibility and dynamic response within an extended pressure range but also enlarge its contact area with electrodes, contributing to a simultaneous improvement in the sensitivity, response speed, detection range, and structure robustness. Meanwhile, owing to its superior abilities in complex structure manufacturing and dimension controlling, the proposed 3D-printing-based fabrication process enables the consistent fabrication of the porous microstructure and thus guarantees device consistency. As a result, the prepared pressure sensors exhibit a high sensitivity of 0.21 kPa-1, fast response and relaxation times of 112 and 152 ms, an interface bonding strength of more than 455.2 kPa, and excellent performance consistency (≤5.47% deviation among different batches of sensors) and tunability. Encouraged by this, the pressure sensor is further integrated with a wireless readout circuit and realizes wireless wearable monitoring of various biosignals (pulse waves and heart rate) and body movements (from slight finger touch to large knee bending). Finally, the influence law of the feature parameters of the porous microstructure on device performance is established by the finite element method, paving the way for sensor optimization. This study motivates the development of flexible capacitive pressure sensors toward practical application.

An All-Protein Multisensory Highly Bionic Skin

To achieve a highly realistic robot, closely mimicking human skin in terms of materials and functionality is essential. This paper presents an all-protein silk fibroin bionic skin (SFBS) that emulates both fast-adapting (FA) and slow-adapting (SA) receptors. The mechanically different silk film and hydrogel, which exhibited skin-like properties, such as stretchability (>140%), elasticity, low modulus (<10 kPa), biocompatibility, and degradability, were prepared through mesoscopic reconstruction engineering to mimic the epidermis and dermis. Our SFBS, incorporating SA and FA sensors, demonstrated a highly sensitive (1.083 kPa-1) static pressure sensing performance (in vitro and in vivo), showed the ability to sense high-frequency vibrations (50-400 Hz), could discriminate materials and sliding, and could even identify the fine morphological differences between objects. As proof of concept, an SFBS-integrated rehabilitation glove was synthesized, which could help stroke patients regain sensory feedback. In conclusion, this work provides a practical approach for developing skin equivalents, prostheses, and smart robots.

Soft Robotic Finger with Energy-Coupled Quadrastability

The performance of the human finger is a significant inspiration for designing soft robotic fingers that can achieve high speed and high force or perform delicate and complex tasks. Existing soft grippers and actuators can be excellent in specific capabilities. However, it is still challenging for them to meet an all-around performance as the human finger, characterized by high actuation speed, wide grasping range, sensing ability, and gentle and high-load grasping capability. The proposed tendon pulley quadrastable (TPQ) finger has combined these qualities in the conducted gripping tasks. A pair of elastic tendons is utilized as the sole energy reservoir to create a novel energy distribution pattern: energy-coupled quadrastability. An energy model is built to analyze and predict the behaviors of the TPQ finger. Mechanical instability is utilized to enhance the actuation speed. The proposed soft lever mechanism endows the TPQ finger with sensing ability. The energy barrier adjusting plates control the energy barrier, adjusting the sensitivity of both active and passive actuation mechanisms. The transition of four stable states forms preplanned trajectories that are applied to create multiple grasping manners. Experiments show that it can respond to stimuli and finish a grasping task in merely 31 ms, and its payload can reach 33.25 kg. At the same time, it can also handle fragile objects such as a piece of rose and grasp a wide range of objects ranging from a thin nut (3.3 mm in height) or a thin card (0.76 mm thick) to a football (220 mm).

Perceived Safety Assessment of Interactive Motions in Human-Soft Robot Interaction

Soft robots, especially soft robotic hands, possess prominent potential for applications in close proximity and direct contact interaction with humans due to their softness and compliant nature. The safety perception of users during interactions with soft robots plays a crucial role in influencing trust, adaptability, and overall interaction outcomes in human-robot interaction (HRI). Although soft robots have been claimed to be safe for over a decade, research addressing the perceived safety of soft robots still needs to be undertaken. The current safety guidelines for rigid robots in HRI are unsuitable for soft robots. In this paper, we highlight the distinctive safety issues associated with soft robots and propose a framework for evaluating the perceived safety in human-soft robot interaction (HSRI). User experiments were conducted, employing a combination of quantitative and qualitative methods, to assess the perceived safety of 15 interactive motions executed by a soft humanoid robotic hand. We analyzed the characteristics of safe interactive motions, the primary factors influencing user safety assessments, and the impact of motion semantic clarity, user technical acceptance, and risk tolerance level on safety perception. Based on the analyzed characteristics, we summarize vital insights to provide valuable guidelines for designing safe, interactive motions in HSRI. The current results may pave the way for developing future soft machines that can safely interact with humans and their surroundings.

Cortical Reorganization after Limb Loss: Bridging the Gap between Basic Science and Clinical Recovery

Despite the increasing incidence and prevalence of amputation across the globe, individuals with acquired limb loss continue to struggle with functional recovery and chronic pain. A more complete understanding of the motor and sensory remodeling of the peripheral and central nervous system that occurs postamputation may help advance clinical interventions to improve the quality of life for individuals with acquired limb loss. The purpose of this article is to first provide background clinical context on individuals with acquired limb loss and then to provide a comprehensive review of the known motor and sensory neural adaptations from both animal models and human clinical trials. Finally, the article bridges the gap between basic science researchers and clinicians that treat individuals with limb loss by explaining how current clinical treatments may restore function and modulate phantom limb pain using the underlying neural adaptations described above. This review should encourage the further development of novel treatments with known neurological targets to improve the recovery of individuals postamputation.Significance Statement In the United States, 1.6 million people live with limb loss; this number is expected to more than double by 2050. Improved surgical procedures enhance recovery, and new prosthetics and neural interfaces can replace missing limbs with those that communicate bidirectionally with the brain. These advances have been fairly successful, but still most patients experience persistent problems like phantom limb pain, and others discontinue prostheses instead of learning to use them daily. These problematic patient outcomes may be due in part to the lack of consensus among basic and clinical researchers regarding the plasticity mechanisms that occur in the brain after amputation injuries. Here we review results from clinical and animal model studies to bridge this clinical-basic science gap.

High-Stroke, High-Output-Force, Fabric-Lattice Artificial Muscles for Soft Robots

Artificial muscles, providing safe and close interaction between humans and machines, are essential in soft robotics. However, their insufficient deformation, output force, or configurability usually limits their applications. Herein, this work presents a class of lightweight fabric-lattice artificial muscles (FAMs) that are pneumatically actuated with large contraction ratios (up to 87.5%) and considerable output forces (up to a load of 20 kg, force-to-weight ratio of over 250). The developed FAMs consist of a group of active air chambers that are zigzag connected into a lattice through passive connecting layers. The geometry of these fabric components is programmable to convert the in-plane lattice of FAMs into out-of-plane configurations (e.g., arched and cylindrical) capable of linear/radial contraction. This work further demonstrates that FAMs can be configured for various soft robotic applications, including the powerful robotic elbow with large motion range and high load capability, the well-fitting assistive shoulder exosuit that can reduce muscle activity during abduction, and the adaptive soft gripper that can grasp irregular objects. These results show the unique features and broad potential of FAMs for high-performance soft robots.

Cybersecurity in neural interfaces: Survey and future trends

With the joint advancement in areas such as pervasive neural data sensing, neural computing, neuromodulation and artificial intelligence, neural interface has become a promising technology facilitating both the closed-loop neurorehabilitation for neurologically impaired patients and the intelligent man-machine interactions for general application purposes. However, although neural interface has been widely studied, few previous studies focused on the cybersecurity issues in related applications. In this survey, we systematically investigated possible cybersecurity risks in neural interfaces, together with potential solutions to these problems. Importantly, our survey considers interfacing techniques on both central nervous systems (i.e., brain-computer interfaces) and peripheral nervous systems (i.e., general neural interfaces), covering diverse neural modalities such as electroencephalography, electromyography and more. Moreover, our survey is organized on three different levels: (1) the data level, which mainly focuses on the privacy leakage issue via attacking and analyzing neural database of users; (2) the permission level, which mainly focuses on the prospects and risks to directly use real time neural signals as biometrics for continuous and unobtrusive user identity verification; and (3) the model level, which mainly focuses on adversarial attacks and defenses on both the forward neural decoding models (e.g. via machine learning) and the backward feedback implementation models (e.g. via neuromodulation and stimulation). This is the first study to systematically investigate cybersecurity risks and possible solutions in neural interfaces which covers both central and peripheral nervous systems, and considers multiple different levels to provide a complete picture of this issue.

Direct-Ink-Writing 3D-Printed Bioelectronics

The development of wearable and implantable bioelectronics has garnered significant momentum in recent years, driven by the ever-increasing demand for personalized health monitoring, remote patient management, and real-time physiological data collection. The elevated sophistication and advancement of these devices have thus led to the use of many new and unconventional materials which cannot be fulfilled through traditional manufacturing techniques. Three-dimension (3D) printing, also known as additive manufacturing, is an emerging technology that opens new opportunities to fabricate next-generation bioelectronic devices. Some significant advantages include its capacity for material versatility and design freedom, rapid prototyping, and manufacturing efficiency with enhanced capabilities. This review provides an overview of the recent advances in 3D printing of bioelectronics, particularly direct ink writing (DIW), encompassing the methodologies, materials, and applications that have emerged in this rapidly evolving field. This review showcases the broad range of bioelectronic devices fabricated through 3D printing including wearable biophysical sensors, biochemical sensors, electrophysiological sensors, energy devices, multimodal systems, implantable devices, and soft robots. This review will also discuss the advantages, existing challenges, and outlook of applying DIW 3D printing for the development of bioelectronic devices toward healthcare applications.

Artificial Intelligence-Powered Electronic Skin

Skin-interfaced electronics is gradually changing medical practices by enabling continuous and noninvasive tracking of physiological and biochemical information. With the rise of big data and digital medicine, next-generation electronic skin (e-skin) will be able to use artificial intelligence (AI) to optimize its design as well as uncover user-personalized health profiles. Recent multimodal e-skin platforms have already employed machine learning (ML) algorithms for autonomous data analytics. Unfortunately, there is a lack of appropriate AI protocols and guidelines for e-skin devices, resulting in overly complex models and non-reproducible conclusions for simple applications. This review aims to present AI technologies in e-skin hardware and assess their potential for new inspired integrated platform solutions. We outline recent breakthroughs in AI strategies and their applications in engineering e-skins as well as understanding health information collected by e-skins, highlighting the transformative deployment of AI in robotics, prosthetics, virtual reality, and personalized healthcare. We also discuss the challenges and prospects of AI-powered e-skins as well as predictions for the future trajectory of smart e-skins.

Tunable Folding Assembly Strategy for Soft Pneumatic Actuators

With intrinsic compliance, soft pneumatic actuators are widely utilized in delicate tasks. However, complex fabrication approaches and limited tunability are still problems. Here, we propose a tunable folding assembly strategy to design and fabricate soft pneumatic actuators called FASPAs (folding assembly soft pneumatic actuators). A FASPA consists only of a folded silicone tube constrained by rubber bands. By designing local stiffness and folding manner, the FASPA can be designed to achieve four configurations, pure bending, discontinuous-curvature bending, helix, and discontinuous-curvature helix. Analytical models are developed to predict the deformation and the tip trajectory of different configurations. Meanwhile, experiments are performed to verify the models. The stiffness, load capacity, output force, and step response are measured, and fatigue tests are performed. Further, grippers with single, double, and triple fingers are assembled by utilizing different types of FASPAs. As such, objects with different shapes, sizes, and weights can be easily grasped. The folding assembly strategy is a promising method to design and fabricate soft robots with complex configurations to complete tough tasks in harsh environments.

Bioinspired Young's Modulus-Hierarchical E-Skin with Decoupling Multimodality and Neuromorphic Encoding Outputs to Biosystems

As key interfaces for the disabled, optimal prosthetics should elicit natural sensations of skin touch or proprioception, by unambiguously delivering the multimodal signals acquired by the prosthetics to the nervous system, which still remains challenging. Here, a bioinspired temperature-pressure electronic skin with decoupling capability (TPD e-skin), inspired by the high-low modulus hierarchical structure of human skin, is developed to restore such functionality. Due to the bionic dual-state amplifying microstructure and contact resistance modulation, the MXene TPD e-skin exhibits high sensitivity over a wide pressure range and excellent temperature insensitivity (91.2% reduction). Additionally, the high-low modulus structural configuration enables the pressure insensitivity of the thermistor. Furthermore, a neural model is proposed to neutrally code the temperature-pressure signals into three types of nerve-acceptable frequency signals, corresponding to thermoreceptors, slow-adapting receptors, and fast-adapting receptors. Four operational states in the time domain are also distinguished after the neural coding in the frequency domain. Besides, a brain-like machine learning-based fusion process for frequency signals is also constructed to analyze the frequency pattern and achieve object recognition with a high accuracy of 98.7%. The TPD neural system offers promising potential to enable advanced prosthetic devices with the capability of multimodality-decoupling sensing and deep neural integration.

An Ultra-Thin MXene Film for Multimodal Sensing of Neuroelectrical Signals with Artifacts Removal

Neuroelectrical signals transmitted onto the skin tend to decay to an extremely weak level, making them highly susceptible to interference from the environment and body movement. Meanwhile, for comprehensively understanding cognitive nerve conduction, multimodal sensing of neural signals, such as magnetic resonance imaging (MRI) and functional near-infrared spectroscopy (fNIRS), is highly required. Previous metal or polymer conductors cannot either provide a seamless on-skin feature for accurate sensing of neuroelectrical signals or be compatible with multimodal imaging techniques without opto- and magnet- artifacts. Herein, a ≈20 nm thick MXene film that is able to simultaneously detect electrophysiological signals and perform imaging by MRI and fNIRS with high fidelity is reported. The ultrathin film is made of crosslinked Ti3 C2 Tx film via poly (3,4-ethylene dioxythiophene): polystyrene sulfonate (PEDOT: PSS), showing a record high electroconductivity and transparency combination (11 000 S cm-1 @89%). Among them, PEDOT: PSS not only plays a cross-linking role to stabilize MXene film but also shortens the interlayer distance for effective charge transfer and high transparency. Thus, it can achieve a low interfacial impedance with skin or neural surfaces for accurate recording of electrophysiological signals with low motion artifacts. Besides, the high transparency originating from the ultrathin feature leads to good compatibility with fNIRS and MRI without optical and magnetic artifacts, enabling multimodal cognitive neural monitoring during prolonged use.

All-Fabric Capacitive Pressure Sensors with Piezoelectric Nanofibers for Wearable Electronics and Robotic Sensing

Flexible pressure sensors are increasingly sought after for applications ranging from physiological signal monitoring to robotic sensing; however, the challenges associated with fabricating highly sensitive, comfortable, and cost-effective sensors remain formidable. This study presents a high-performance, all-fabric capacitive pressure sensor (AFCPS) that incorporates piezoelectric nanofibers. Through the meticulous optimization of conductive fiber electrodes and P(VDF-TrFE) nanofiber dielectric layers, the AFCPS exhibits exceptional attributes such as high sensitivity (4.05 kPa-1), an ultralow detection limit (0.6 Pa), an extensive detection range (∼100 kPa), rapid response time (<26 ms), and robust stability (>14,000 cycles). The sensor's porous structure enhances its compressibility, while its piezoelectric properties expedite charge separation, thereby increasing the interface capacitance and augmenting overall performance. These features are elucidated further through multiphysical field-coupling simulations and experimental testing. Owing to its comprehensive superior performance, the AFCPS has demonstrated its efficacy in monitoring human activity and physiological signals, as well as in discerning soft robotic grasping movements. Additionally, we have successfully implemented multiple AFCPS units as pressure sensor arrays to ascertain spatial pressure distribution and enable intelligent robotic gripping. Our research underscores the promising potential of the AFCPS device in wearable electronics and robotic sensing, thereby contributing significantly to the advancement of high-performance fabric-based sensors.

Stretchable surface electromyography electrode array patch for tendon location and muscle injury prevention

Surface electromyography (sEMG) can provide multiplexed information about muscle performance. If current sEMG electrodes are stretchable, arrayed, and able to be used multiple times, they would offer adequate high-quality data for continuous monitoring. The lack of these properties delays the widespread use of sEMG in clinics and in everyday life. Here, we address these constraints by design of an adhesive dry electrode using tannic acid, polyvinyl alcohol, and PEDOT:PSS (TPP). The TPP electrode offers superior stretchability (~200%) and adhesiveness (0.58 N/cm) compared to current electrodes, ensuring stable and long-term contact with the skin for recording (>20 dB; >5 days). In addition, we developed a metal-polymer electrode array patch (MEAP) comprising liquid metal (LM) circuits and TPP electrodes. The MEAP demonstrated better conformability than commercial arrays, resulting in higher signal-to-noise ratio and more stable recordings during muscle movements. Manufactured using scalable screen-printing, these MEAPs feature a completely stretchable material and array architecture, enabling real-time monitoring of muscle stress, fatigue, and tendon displacement. Their potential to reduce muscle and tendon injuries and enhance performance in daily exercise and professional sports holds great promise.

JamTac: A Tactile Jamming Gripper for Searching and Grasping in Low-Visibility Environments

Humans can feel and grasp efficiently in the dark through tactile feedback, whereas it is still a challenging task for robots. In this research, we create a novel soft gripper named JamTac, which has high-resolution tactile perception, a large detection surface, and integrated sensing-grasping capability that can search and grasp in low-visibility environments. The gripper combines granular jamming and visuotactile perception technologies. Using the principle of refractive index matching, a refraction-free liquid-particle rationing scheme is developed, which makes the gripper itself to be an excellent tactile sensor without breaking its original grasping capability. We simultaneously acquire color and depth information inside the gripper, making it possible to sense the shape, texture, hardness, and contact force with high resolution. Experimental results demonstrate that JamTac can be a promising tool to search and grasp in situations when vision is not available.

Dexterous electrical-driven soft robots with reconfigurable chiral-lattice foot design

Dexterous locomotion, such as immediate direction change during fast movement or shape reconfiguration to perform diverse tasks, are essential animal survival strategies which have not been achieved in existing soft robots. Here, we present a kind of small-scale dexterous soft robot, consisting of an active dielectric elastomer artificial muscle and reconfigurable chiral-lattice foot, that enables immediate and reversible forward, backward and circular direction changes during fast movement under single voltage input. Our electric-driven soft robot with the structural design can be combined with smart materials to realize multimodal functions via shape reconfigurations under the external stimulus. We experimentally demonstrate that our dexterous soft robots can reach arbitrary points in a plane, form complex trajectories, or lower the height to pass through a narrow tunnel. The proposed structural design and shape reconfigurability may pave the way for next-generation autonomous soft robots with dexterous locomotion.

Bioinspired rotary flight of light-driven composite films

Light-driven actuators have great potential in different types of applications. However, it is still challenging to apply them in flying devices owing to their slow response, small deflection and force output and low frequency response. Herein, inspired by the structure of vine maple seeds, we report a helicopter-like rotary flying photoactuator (in response to 0.6 W/cm2 near-infrared (NIR) light) with ultrafast rotation (~7200 revolutions per minute) and rapid response (~650 ms). This photoactuator is operated based on a fundamentally different mechanism that depends on the synergistic interactions between the photothermal graphene and the hygroscopic agar/silk fibroin components, the subsequent aerodynamically favorable airscrew formation, the jet propulsion, and the aerodynamics-based flying. The soft helicopter-like photoactuator exhibits controlled flight and steering behaviors, making it promising for applications in soft robotics and other miniature devices.

Finger Kinematics during Human Hand Grip and Release

A bionic robotic hand can perform many movements similar to a human hand. However, there is still a significant gap in manipulation between robot and human hands. It is necessary to understand the finger kinematics and motion patterns of human hands to improve the performance of robotic hands. This study aimed to comprehensively investigate normal hand motion patterns by evaluating the kinematics of hand grip and release in healthy individuals. The data corresponding to rapid grip and release were collected from the dominant hands of 22 healthy people by sensory glove. The kinematics of 14 finger joints were analyzed, including the dynamic range of motion (ROM), peak velocity, joint sequence and finger sequence. The results show that the proximal interphalangeal (PIP) joint had a larger dynamic ROM than metacarpophalangeal (MCP) and distal interphalangeal (DIP) joints. Additionally, the PIP joint had the highest peak velocity, both in flexion and extension. For joint sequence, the PIP joint moved prior to the DIP or MCP joints during flexion, while extension started in DIP or MCP joints, followed by the PIP joint. Regarding the finger sequence, the thumb started to move before the four fingers, and stopped moving after the fingers during both grip and release. This study explored the normal motion patterns in hand grip and release, which provided a kinematic reference for the design of robotic hands and thus contributes to its development.

Flexible Pressure Sensors Based on Microcrack Structure and Composite Conductive Mechanism for Medical Robotic Applications

With the advancement of intelligent medical robot technology, machine touch utilizing flexible sensors has emerged as a prominent research area. In this study, a flexible resistive pressure sensor was designed incorporating a microcrack structure with air pores and a composite conductive mechanism of silver/carbon. The aim was to achieve enhanced stability and sensitivity with the inclusion of macro through-holes (1-3 mm) to expand the sensitive range. This technology solution was specifically applied to the machine touch system of the B-ultrasound robot. Through meticulous experimentation, it was determined that the optimal approach involved uniformly blending ecoflex and nano carbon powder at a mass ratio of 5:1, and subsequently combining the mixture with an ethanol solution of silver nanowires (AgNWs) at a mass ratio of 6:1. This combination of components resulted in the fabrication of a pressure sensor with optimal performance. Under the pressure testing condition of 5 kPa, a comparison of the resistance change rate was conducted among samples using the optimal formulation from the three processes. It was evident that the sample of ecoflex-C-AgNWs/ethanol solution exhibited the highest sensitivity. Its sensitivity was increased by 19.5% compared to the sample (ecoflex-C) and by 11.3% compared to the sample (ecoflex-C-ethanol). The sample (ecoflex-C-AgNWs/ethanol solution), which only incorporated internal air pore microcracks without through-holes, exhibited sensitive response to pressures below 5 N. However, with the addition of through-holes, the measurement range of its sensitive response increased to 20 N, representing a 400% increase in the measurement range.

Soft Robotics Enables Neuroprosthetic Hand Design

Development and implementation of neuroprosthetic hands is a multidisciplinary field at the interface between humans and artificial robotic systems, which aims at replacing the sensorimotor function of the upper-limb amputees as their own. Although prosthetic hand devices with myoelectric control can be dated back to more than 70 years ago, their applications with anthropomorphic robotic mechanisms and sensory feedback functions are still at a relatively preliminary and laboratory stage. Nevertheless, a recent series of proof-of-concept studies suggest that soft robotics technology may be promising and useful in alleviating the design complexity of the dexterous mechanism and integration difficulty of multifunctional artificial skins, in particular, in the context of personalized applications. Here, we review the evolution of neuroprosthetic hands with the emerging and cutting-edge soft robotics, covering the soft and anthropomorphic prosthetic hand design and relating bidirectional neural interactions with myoelectric control and sensory feedback. We further discuss future opportunities on revolutionized mechanisms, high-performance soft sensors, and compliant neural-interaction interfaces for the next generation of neuroprosthetic hands.

Design of adhesive conducting PEDOT-MeOH:PSS/PDA neural interface via electropolymerization for ultrasmall implantable neural microelectrodes

Conducting polymers are emerging as promising neural interfaces towards diverse applications such as deep brain stimulation due to their superior biocompatibility, electrical, and mechanical properties. However, existing conducting polymer-based neural interfaces still suffer from several challenges and limitations such as complex preparation procedures, weak interfacial adhesion, poor long-term fidelity and stability, and expensive microfabrication, significantly hindering their broad practical applications and marketization. Herein, we develop an adhesive and long-term stable conducting polymer neural interface by a simple two-step electropolymerization methodology, namely, the pre-polymerization of polydopamine (PDA) as an adhesive thin layer followed by electropolymerization of hydroxymethylated 3,4-ethylenedioxythiophene (EDOT-MeOH) with polystyrene sulfonate (PSS) to form stable interpenetrating PEDOT-MeOH:PSS/PDA networks. As-prepared PEDOT-MeOH:PSS/PDA interface exhibits remarkably improved interfacial adhesion against metallic electrodes, showing 93% area retention against vigorous sonication for 20 min, which is one of the best tenacious conducting polymer interfaces so far. Enabled by the simple methodology, we can facilely fabricate the PEDOT-MeOH:PSS/PDA interface onto ultrasmall Pt-Ir wire microelectrodes (diameter: 10 μm). The modified microelectrodes display two orders of magnitude lower impedance than commercial products, and also superior long-term stability to previous reports with high charge injection capacity retention up to 99.5% upon 10,000,000 biphasic input pulse cycles. With these findings, such a simple methodology, together with the fabricated high-performance and stable neural interface, can potentially provide a powerful tool for both advanced neuroscience researches and cutting-edge clinical applications like brain-controlled intelligence.

Single-vat single-cure grayscale digital light processing 3D printing of materials with large property difference and high stretchability

Multimaterial additive manufacturing has important applications in various emerging fields. However, it is very challenging due to material and printing technology limitations. Here, we present a resin design strategy that can be used for single-vat single-cure grayscale digital light processing (g-DLP) 3D printing where light intensity can locally control the conversion of monomers to form from a highly stretchable soft organogel to a stiff thermoset within in a single layer of printing. The high modulus contrast and high stretchability can be realized simultaneously in a monolithic structure at a high printing speed (z-direction height 1 mm/min). We further demonstrate that the capability can enable previously unachievable or hard-to-achieve 3D printed structures for biomimetic designs, inflatable soft robots and actuators, and soft stretchable electronics. This resin design strategy thus provides a material solution in multimaterial additive manufacture for a variety of emerging applications.

Embedment of sensing elements for robust, highly sensitive, and cross-talk-free iontronic skins for robotics applications

Iontronic pressure sensors are promising in robot haptics because they can achieve high sensing performance using nanoscale electric double layers (EDLs) for capacitive signal output. However, it is challenging to achieve both high sensitivity and high mechanical stability in these devices. Iontronic sensors need microstructures that offer subtly changeable EDL interfaces to boost sensitivity, while the microstructured interfaces are mechanically weak. Here, we embed isolated microstructured ionic gel (IMIG) in a hole array (28 × 28) of elastomeric matrix and cross-link the IMIGs laterally to achieve enhanced interfacial robustness without sacrificing sensitivity. The embedded configuration toughens and strengthens the skin by pinning cracks and by the elastic dissipation of the interhole structures. Furthermore, cross-talk between the sensing elements is suppressed by isolating the ionic materials and by designing a circuit with a compensation algorithm. We have demonstrated that the skin is potentially useful for robotic manipulation tasks and object recognition.