Real-time Human Motion Research Articles

Strong and tough anisotropic short-chain chitosan-based hydrogels with optimized sensing properties for flexible strain sensors

Conductive hydrogels are ideal candidates for developing flexible electronic devices, but they still cannot exhibit excellent strength, satisfactory toughness and high conductivity in sync, greatly limiting their further applications. Inspired by the structure-enhanced properties of natural tissues, we adopted a straightforward and efficient methodology for constructing strong and tough anisotropic short-chain chitosan-based hydrogels with salting-assisted tensile remodeling treatment. The anisotropic hydrogels present anisotropic mechanical and electrochemical performance due to the oriented arrangement of chitosan and P(AM-AA) macromolecular networks. The remarkable tensile strength, toughness, and conductivity of parallel-oriented hydrogels are up to 9.01 MPa, 32.77 MJ/m3, and 692.30 mS/m, respectively, which are much higher than that of isotropic and vertical-oriented hydrogels, verifying the structure-optimized mechanical and sensing performance. The anisotropic conductive polypolysaccharide hydrogels are assembled as strain sensors for real-time human movement monitoring, Morse code encryption and decryption transmission, and gesture prediction combined with a machine learning algorithm, providing new inspiration for blossoming strong, flexible electronic products.

Bio-Inspired Motion Emulation for Social Robots: A Real-Time Trajectory Generation and Control Approach.

Assistive robotic platforms have recently gained popularity in various healthcare applications, and their use has expanded to social settings such as education, tourism, and manufacturing. These social robots, often in the form of bio-inspired humanoid systems, provide significant psychological and physiological benefits through one-on-one interactions. To optimize the interaction between social robotic platforms and humans, it is crucial for these robots to identify and mimic human motions in real time. This research presents a motion prediction model developed using convolutional neural networks (CNNs) to efficiently determine the type of motions at the initial state. Once identified, the corresponding reactions of the robots are executed by moving their joints along specific trajectories derived through temporal alignment and stored in a pre-selected motion library. In this study, we developed a multi-axial robotic arm integrated with a motion identification model to interact with humans by emulating their movements. The robotic arm follows pre-selected trajectories for corresponding interactions, which are generated based on identified human motions. To address the nonlinearities and cross-coupled dynamics of the robotic system, we applied a control strategy for precise motion tracking. This integrated system ensures that the robotic arm can achieve adequate controlled outcomes, thus validating the feasibility of such an interactive robotic system in providing effective bio-inspired motion emulation.

High-Performance Stretchable Strain Sensors Based on Auxetic Fabrics for Human Motion Detection.

Wearable strain sensors play a pivotal role in real-time human motion detection and health monitoring. Traditional fabric-based strain sensors, typically with a positive Poisson's ratio, face challenges in maintaining sensitivity and comfort during human motion due to conflicting resistance changes in different strain directions. In this work, high-performance stretchable strain sensors are developed based on graphene-modified auxetic fabrics (GMAF) for human motion detection in smart wearable devices. The proposed GMAF sensors, with a negative Poisson's ratio achieved through commercially available warp-knitting technology, exhibit an 8-fold improvement in sensitivity compared to conventional plain fabric sensors. The unique auxetic fabric structure enhances sensitivity by synchronizing resistance changes in both wale and course directions. The GMAF sensors demonstrate excellent washability, showing only slight degradation in auxeticity and an acceptable increase in resistance after 10 standard wash cycles. The GMAF sensors maintain stability under different strain levels and various motion frequencies, emphasizing their dynamic performance. The sensors exhibit superior conformability to joint movements, which effectively monitor a full range of motions, including joint bending, sports activities, and subtle actions like coughing and swallowing. The research underscores a promising approach to achieve industrial-scale production of wearable sensors with improved performance and comfort through fabric structure design.

Rapid photo-crosslinking, highly conductive, and anti-freezing acrylamide-based hydrogels applied for ECG sensors

In recent years, ionic conductive hydrogels have attracted significant attention in the wearable sensors field due to their simplified preparation process, cost- effectiveness, and high conductivity. However, a large amount of water in traditional hydrogels inevitably condense into ice at low temperatures and make them lose various properties in their practical applications. Hence, this study proposes a method to rapidly synthesized a water/glycerol binary anti-freezing hydrogel only in 10 s through photo-initiated free-radical polymerization. The introduction of LiCl into the crosslinked network composed of acrylamide (AAm) and poly (ethylene glycol) diacrylate (PEGDA) regulated the ionic conductivity of the hydrogel. The hydrogel exhibits a fracture strain of 1062.1 %, a tensile strength of 31 kPa, and an adhesive strength of 6.49 kPa. The synergistic effects of glycerol and LiCl imparted excellent anti-freezing properties (−60 °C). The three-dimensional network structure of the hydrogel facilitates ion transport, exhibiting superior conductivity (30.25 S/m), maintaining high conductivity capability of 1.46 S/m even at a low temperature of −55 °C. Notably, the hydrogel demonstrates prolonged durability and stability in real-time human motion monitoring applications while concurrently exhibiting remarkable sensitivity as an ECG sensor. Hence, this as-prepared hydrogel can adapt to extreme environments and deliver comprehensive performance as an ECG sensor.

Graphene-Infused Sustainable Rubber-Based Triboelectric Nanogenerator For Real-Time Human Motion Monitoring.

Triboelectric nanogenerators (TENG) are promising alternatives for clean energy harvesting. However, the material utilization in the development of TENG relies majorly on polymers derived from non-renewable resources. Therefore, minimizing the carbon footprint associated with such TENG development demands a shift toward usage of sustainable materials. This study pioneers using natural rubber (NR) as a sustainable alternative in TENG development. Infusing graphene in NR, its dielectric constant and tribonegativity are optimized, yielding a remarkable enhancement. The optimized sample exhibits a dielectric constant of 411 (at 103Hz) and a contact potential difference (CPD) value of 1.85V. In contrast, the pristine NR sample showed values of 6 and 3.06V for the dielectric constant and CPD. Simulation and experimental studies fine-tune the TENG's performance, demonstrating excellent agreement between theoretical predictions and practical studies. Sensors developed via stencil printing technique possess a remarkably low layer thickness of 270µm, and boast a power density of 420mWm-2, a staggering 250% increase over conventional NR. Moreover, the material is pressure sensitive, enabling precise real-time human motion detection, including finger contact, finger bending, neck bending, and arm bending. This versatile sensor offers wireless monitoring, empowering healthcare monitoring based on the Internet of Things.

Engineering versatile bi-network ionic conductive hydrogels wearable sensors via on demand graft modification for real-time human movement monitoring

With the rapid development of the field of flexible electronics, it is necessary to develop more new flexible sensor materials with a high degree of flexibility, stretchability, biosafety, and good electrical conductivity to better achieve wearable with the human body. Cellulose hydrogels are promising building blocks for flexible sensors due to their renewability and excellent mechanical strength properties. In this study, we demonstrate an imidazolium ionic cellulose/polyacrylic acid hydrogel (ILHPMC/PAA) with high flexibility, antimicrobial properties and electrical conductivity. Firstly, epoxidized imidazole ionic liquids were synthesized using methyl imidazole as raw material, and imidazole ionic cellulose was prepared by grafting modified hydroxypropyl methyl cellulose (HPMC). Imidazolium ionic cellulose hydrogels were prepared by one-step cross-linking and forming a copolymeric bi-network with acrylic acid according to the principle of free radical polymerisation. This hydrogel has high flexibility, stretchability, biocompatibility, and desired electrical conductivity due to the introduction of imidazole ions, and the sensing sensitivity factor (GF) reaches 6.83. The tensile strength can be up to 1.2 MPa, and it also has excellent antibacterial properties, with an antibacterial zone diameter of up to 29.25 mm. This provides insights into the development of cellulose-based sustainable sensing hydrogel sensors for wearable ionic conductive sensing.

Constructing multifunctional polyvinyl alcohol composite hydrogels with human-like skin properties using polyaniline-coated boron nitride nanofibers

Flexible conductive nanofibers play a crucial role in constructing hydrogels with human-like skin properties. However, it is still a challenge to construct flexible conductive nanofibers with good comprehensive properties. In this paper, we prepare composite fibers with excellent comprehensive properties by coating polyaniline (PANI) on the surface of flexible boron nitride nanofiber (BNNFs) template. BNNFs-PANI/PVA multifunctional composite hydrogels are created through the integration of BNNFs-PANI as nanofillers with soft poly (vinyl alcohol) (PVA) matrix using a one-pot self-assembly method. The composite hydrogels exhibit outstanding plasticity, self-adhesion, and self-healing properties, with a self-healing efficiency reaching up to 96% within 120 s. Furthermore, the tensile strength of the composite hydrogels has been enhanced by 36%, with the elongation at break exceeding 1500%. The compressive strength of the composite hydrogels at 60% strain is increased by 62%. Beyond these mechanical and self-healing features similar to human skin, the composite hydrogels also can serve as electronic pen for painting on a touchable electronic screen, simulating the function of human skin. Moreover, the dynamic changes in the abundant conductive network contribute to the composite hydrogels’ high strain sensitivity. Compared with pure PVA hydrogels, it is improved by 29%. Notably, the strain sensitivity of the composite hydrogels is higher than that of most reported hydrogel sensors. Therefore, the composite hydrogels also can be employed as a real-time human motion sensor, capable of detecting and distinguishing various human movements. We believe that employing BNNFs as templates for crafting highly flexible and conductive nanofibers will foster the advancement of flexible conductive nanomaterials with comprehensive properties. Furthermore, the outstanding properties of the prepared composite hydrogels contribute to the ongoing development of conductive hydrogels with skin-like functionalities.

An Ergonomics Analysis of Archers through Motion Tracking to Prevent Injuries and Improve Performance.

Archery ranks among the sports with a high incidence of upper extremity injuries, particularly affecting the drawing shoulder and elbow, as well as inducing stress on the lower back. This study seeks to bridge the gap by integrating real-time human motion with biomechanical software to enhance the ergonomics of archers. Thirteen participants were involved in four tasks, using different bows with varied draw weights and shooting distances. Through the application of advanced integrative technology, this study highlights the distinct postures adopted by both males and females, which indicate the biomechanical differences between genders. Additionally, an analysis of the correlation between exposed spinal forces and these adopted postures provides insights into injury risk assessment during the key archery movements. The findings of this study have the potential to significantly enhance the application of training methodologies and the design of assistive devices. These improvements are geared towards mitigating injury risks and enhancing the overall performance of archers.

Digital Light Processing 4D Printing of Poloxamer Micelles for Facile Fabrication of Multifunctional Biocompatible Hydrogels as Tailored Wearable Sensors.

The lack of both digital light processing (DLP) compatible and biocompatible photopolymers, along with inappropriate material properties required for wearable sensor applications, substantially hinders the employment of DLP 3D printing in the fabrication of multifunctional hydrogels. Herein, we discovered and implemented a photoreactive poloxamer derivative, Pluronic F-127 diacrylate, which overcomes these limitations and is optimized to achieve DLP 3D printed micelle-based hydrogels with high structural complexity, resolution, and precision. In addition, the dehydrated hydrogels exhibit a shape-memory effect and are conformally attached to the geometry of the detection point after rehydration, which implies the 4D printing characteristic of the fabrication process and is beneficial for the storage and application of the device. The excellent cytocompatibility and in vivo biocompatibility further strengthen the potential application of the poloxamer micelle-based hydrogels as a platform for multifunctional wearable systems. After processing them with a lithium chloride (LiCl) solution, multifunctional conductive ionic hydrogels with antifreezing and antiswelling properties along with good transparency and water retention are easily prepared. As capacitive flexible sensors, the DLP 3D printed micelle-based hydrogel devices exhibit excellent sensitivity, cycling stability, and durability in detecting multimodal deformations. Moreover, the DLP 3D printed conductive hydrogels are successfully applied as real-time human motion and tactile sensors with satisfactory sensing performances even in a -20 °C low-temperature environment.

Microwave-assisted DES fabrication of lignin-containing cellulose nanofibrils and its derived composite conductive hydrogel

Deep eutectic solvents (DES) have been regarded as green solvents in the biorefinery of lignocellulosic biomass, but long duration time has severely limited efficiency. The microwave-assisted DES pretreatment along with enzymatic hydrolysis and high-pressure homogenization process was proposed to produce lignin-containing cellulose nanofibrils (LCNF) from corncob. Benefiting from microwave-assisted DES pretreatment, the duration time was greatly shortened; meanwhile the effects of different kinds of DES on the resultant LCNF were investigated. The results showed that, the microwave-assisted DES fabricated LCNF (M-LCNF) was successfully obtained, exhibiting good nano size, thermal stability, colloidal stability, and fluorescence. M-LCNF was further introduced into phytic acid (PA) enhanced poly(acrylamide-co-acrylic acid) (P(AM-co-AA)) network and constructed composite conductive hydrogels (PLP). The obtained hydrogels exhibited good mechanical strength, UV blocking ability, fluorescence, and conductivity. A simple battery assembled with the resultant PLP as electrolyte had an out voltage of 2.41 V. The composite conductive hydrogel showed good sensing performance towards different stimuli (e.g., stretching and compression) and human motions in real time. It is expected that this research would provide an alternative way for green fabrication of LCNF and potential application of LCNF in flexible sensors.

Chemical Resistant Yarn with Hierarchical Core–Shell Structure for Safety Monitoring and Tunable Thermal Management in High-Risk Environments

Chemical resistant textiles are vital for safeguarding humans against chemical hazards in various settings, such as industrial production, chemical accidents, laboratory activities, and road transportation. However, the ideal integration of chemical resistance, thermal and moisture management, and wearer condition monitoring in conventional chemically protective textiles remains challenging. Herein, the design, manufacturing, and use of stretchable hierarchical core–shell yarns (HCSYs) for integrated chemical resistance, moisture regulation, and smart sensing textiles are demonstrated. These yarns contain helically elastic spandex, wrapped silver-plated nylon, and surface-structured polytetrafluoroethylene (PTFE) yarns and are designed and manufactured based on a scalable fabrication process. In addition to their ideal chemical resistance performance, HCSYs can function as multifunctional stretchable electronics for real-time human motion monitoring and as the basic element of intelligent textiles. Furthermore, a desirable dynamic thermoregulation function is achieved by exploiting the fabric structure with stretching modulation. Our HCSYs may provide prospective opportunities for the future development of smart protective textiles with high durability, flexibility, and scalability.

Glycopolymer-Based Antiswelling, Conductive, and Underwater Adhesive Hydrogels for Flexible Strain Sensor Application.

With the fast development of soft electronics, underwater adhesion has become a highly desired feature for various sensing uses. Currently, most adhesive hydrogels are based on catechol-based structures, such as polydopamine, pyrogallol, and tannic acid, with very limited structural variety. Herein, a new type of glycopolymer-based underwater adhesive hydrogel has been prepared straightforwardly by random copolymerization of acrylic acid, acetyl-protected/unprotected glucose, and methacrylic anhydride in dimethyl sulfoxide (DMSO). By employing a DMSO-water solvent exchange strategy, the underwater adhesion was skillfully induced by the synergetic effects of hydrophobic aggregation and hydrogen bonding, leading to excellent adhesion behaviors on various surfaces, including pig skins, glasses, plastics, and metals, even after 5 days of storage in water. In addition, the underwater adhesive hydrogels with simple and low-cost protected/unprotected carbohydrate compositions showed good mechanical and rheological properties, together with cytocompatibility and antiswelling behavior in water, all of which are beneficial for underwater adhesions. In application as a flexible strain sensor, the adhesive hydrogel exhibited stable and reliable sensing ability for monitoring human motion in real time, suggesting great potential for intelligent equipment design.

A Spatio-Temporal Prediction and Planning Framework for Proactive Human–Robot Collaboration

AbstractA significant challenge in human–robot collaboration (HRC) is coordinating robot and human motions. Discoordination can lead to production delays and human discomfort. Prior works seek coordination by planning robot paths that consider humans or their anticipated occupancy as static obstacles, making them nearsighted and prone to entrapment by human motion. This work presents the spatio-temporal avoidance of predictions-prediction and planning framework (STAP-PPF) to improve robot–human coordination in HRC. STAP-PPF predicts multi-step human motion sequences based on the locations of objects the human manipulates. STAP-PPF then proactively determines time-optimal robot paths considering predicted human motion and robot speed restrictions anticipated according to the ISO15066 speed and separation monitoring (SSM) mode. When executing robot paths, STAP-PPF continuously updates human motion predictions. In real-time, STAP-PPF warps the robot’s path to account for continuously updated human motion predictions and updated SSM effects to mitigate delays and human discomfort. Results show the STAP-PPF generates robot trajectories of shorter duration. STAP-PPF robot trajectories also adapted better to real-time human motion deviation. STAP-PPF robot trajectories also maintain greater robot/human separation throughout tasks requiring close human–robot interaction. Tests with an assembly sequence demonstrate STAP-PPF’s ability to predict multi-step human tasks and plan robot motions for the sequence. STAP-PPF also most accurately estimates robot trajectory durations, within 30% of actual, which can be used to adapt the robot sequencing to minimize disruption.

An IoT and machine learning enhanced framework for real-time digital human modeling and motion simulation

The Digital Human Model (DHM) is a computational technique employed for simulating human motion and behavior, finding applications across sectors such as manufacturing, healthcare, and education. Despite its potential, current DHM simulations grapple with challenges like high computational costs, significant latency, low precision, and limited realism. Addressing these challenges, this paper introduces a novel DHM simulation approach that synergizes the Kinect motion capture system, Internet of Things (IoT) devices, and advanced machine learning techniques. Distinctively, our method captures real-time human motion data through IoT devices and the Kinect system, followed by data preprocessing and feature extraction. In contrast to traditional methodologies, we harness deep learning to establish time-series models, integrating convolutional neural networks and dilated convolutional kernels to amplify processing capabilities and cater to multi-scale data. Furthermore, we employ virtual reality technology to craft a digital human structural model, streamlining motion simulation and optimization. Experimental evaluations underscore that our approach consistently outperforms conventional methods across all metrics, demonstrating robust real-time simulation capabilities. In essence, this paper pioneers an innovative technological paradigm for DHM simulation, heralding new avenues for research and applications in related domains.

Titania Nanoparticle-Stimulated Ultralow Frequency Detection and High-Pass Filter Behavior of a Flexible Piezoelectric Nanogenerator: A Self-Sustaining Energy Harvester for Active Motion Tracking.

A significant driving force for the fabrication of IoT-compatible smart health gear integrated with multifunctional sensors is the growing trend in fitness and the overall wellness of the human body. In this work, we present an autonomous motion and activity-sensing device based on the efficacious nucleation of the polar β-phase in an electroactive polymer. Representatively, we investigate the nucleating effect of TiO2 nanoparticles on weight-modulated PVDF-HFP films (PT-5, PT-10, and PT-15) and subsequently prototype a sensing device with the film that demonstrates superior β-phase nucleation. The PT-10 film, with an optimal polar β-phase, shows the highest remnant polarization (2Pr) and energy density of 0.36 μC/cm2 and 22.3 mJ/cm3, respectively, at 60 kV/cm. The films mimic a high pass filter at frequencies above 10 KHz with very low impedance and high ac conductivity values. The frequency-dependent impedance studies reveal an effective interfacial polarization between TiO2 nanoparticles and PVDF-HFP, explicitly observed in the low-frequency region. Consequently, the sensor fabricated with PT-10 as the sensing layer exhibits ultralow frequency detection (25 Hz) resulting from the blood flow muscle oxygenation. The device successfully senses voluntary joint movements of the human body and actively tracks a range of motions, from brisk walking to running. Additionally, through repetitive human finger-tapping motion, the nanogenerator lights up multiple light-emitting diodes in series and charges capacitors of varying magnitudes under 50 s. The real-time human motion sensing and movement tracking modalities of the sensor hold promise in the arena of smart wearables, sports biomechanics, and contact-based medical devices.

Fabrication of amphoteric gelatin nanospheres-doped self-healing nanocomposite hydrogels and the application in flexible sensors

The nanomaterials with two distinct properties or components have outstanding advantages in the synthesis of smart hydrogels. In this work, gelatin nanospheres (GNPs) asymmetrically coated with poly(diallyldimethylammonium chloride) (PDDA) and tannic acid (TA) were synthesized by Pickering emulsion template method. The nanocomposite hydrogel with amphoteric gelatin nanospheres (GPTNPs)-doped was successfully prepared, which has excellent mechanical properties (strain = 616%, stress = 2.85 MPa) and self-healing properties (efficiency = 90.9%) under the synergistic effects of hydrogen bonding and metal coordination. The developed GPTNPs/polyacrylic acid (GPTNPs/PAA) hydrogel sensor exhibits high sensitivity (gauge factor (GF) = 3.69), quick response time (96.4 ms) and superior stability. Therefore, the GPTNPs/PAA hydrogels are excellent materials for application in wearable flexible sensors to monitor real-time human motion.

A transparent, self-adhesive and fully recyclable conductive PVA based hydrogel for wearable strain sensor

Hydrogel-based wearable sensors have attracted tremendous attention in the sensing electronics field due to their unique properties. However, integrating hydrogel sensors with transparency, biosafety, self-adhesiveness, recyclability and conductivity remains a great challenge. Here, we report an ionic conductive hydrogel prepared using poly (vinyl alcohol) (PVA) grafted with the 2,3-epoxypropyltrimethylammonium chloride (TMAC) through a facile freezing-thawing (F-T) process. The obtained PVA grafted with TMAC hydrogel (PVATH) exhibited relatively high transparency (∼87% transmittance) and strong self-adhesion to different substrates with high stretchability (544%) and good conductivity (0.038 S m−1). Most notably, the hydrogel can be fully recycled by a simple and green process on account of a fully physical cross-linking network. Even after three recycling cycles, the hydrogel sensor exhibited almost identical electromechanical performance with respect to the original sensor. The hydrogel was further assembled as a strain sensor for monitoring real-time human motions, including finger, wrist, elbow, knee joint and neck movement. This work provides an innovative design strategy to fabricate ionic conductive hydrogels and reduce electronic waste, which will broaden the applications of hydrogels in wearable devices.

Self-Healable and Reprocessable Silicon Elastomers Based on Imine-Boroxine Bonds for Flexible Strain Sensor.

Silicon elastomers with excellent self-healing and reprocessing abilities are highly desirable for the advancement of next-generation energy, electronic, and robotic applications. In this study, a dual cross-linked self-healing polysiloxane elastomer was facilely fabricated by introducing an exchangeable imine bond and boroxine into polydimethylsiloxane (PDMS) networks. The PDMS elastomers exhibited excellent self-healing properties due to the synergistic effect of dynamic reversible imine bonds and boroxine. After healing for 2 h, the mechanical strength of the damaged elastomers completely and rapidly recovered at room temperature. Furthermore, the prepared PDMS elastomers could be repeatedly reprocessed multiple times under milder conditions without significant degradation in mechanical performance. In addition, a stretchable and self-healable electrical sensor was developed by integrating carbon nanotubes (CNTs) with the PDMS elastomer, which can be employed to monitor multifarious human motions in real time. Therefore, this work provides a new inspiration for preparing self-healable and reprocessable silicone elastomers for future flexible electronics.

Thermogalvanic gel patch for self-powered human motion recognition enabled by photo-thermal-electric conversion

Quasi-solid-state electrolytes offer a cheap, leak-free, and scalable way to convert heat directly into electricity for wearable electronics. However, the limited temperature difference with the environment poses a challenge for the extensive application of thermoelectric gels. Currently, photo-thermal-electric (PTE) conversion is an environmentally-friendly technology to exploit the thermogalvanic effect of gels. Here, we report a smart light-driven flexible PTE gel for self-powered human motion monitoring. The whole structure takes advantage of the low thermal conductivity and high output power of poly(vinyl alcohol)/polydimethylsiloxane (PVA-PDMS) gels with Fe2+/3+ as a redox pair, together with the local surface plasmon resonance (LSPR) of Au nanoparticles (AuNPs)@SiO2-PDMS composite film. This enables simultaneous high thermogalvanic and photothermal performance, with an open circuit voltage of up to 12 mV and a short circuit current exceeding 1.25 A m−2 at a temperature difference of up to 9 °C under one sun. The PTE gel is fabricated into a proof-of-concept self-powered patch, which can actively distinguish human motions in real time by recognizing the switching of solar radiation stimulated by arm swing. This work provides thermogalvanic gels with a new opportunity in self-powered wearable sensing that may greatly promote the progress of intelligent medical electronics.

Super‐Stretchable, Anti‐Freezing, Anti‐Drying Organogel Ionic Conductor for Multi‐Mode Flexible Electronics

AbstractDue to their intrinsic flexibility, tunable conductivity, multiple stimulus‐response, and self‐healing ability, ionic conductive hydrogels have drawn significant attention in flexible/wearable electronics. However, challenges remain because traditional hydrogels inevitably faced the problems of losing flexibility and conductivity because of the inner water loss when exposed to the ambient environment. Besides, the water inside the hydrogel will freeze at the water icing temperatures, making the device hard and fragile. As a promising alternative, organogels have attracted wide attention because they can, to some extent, overcome the above drawbacks. Herein, a kind of organogel ionic conductor (MOIC) by a self‐polymerization reaction is involved, which is super stretchable, anti‐drying, and anti‐freezing. Meanwhile, it can still maintain high mechanical stability after alternately loading/unloading at the strain of 600% for 600 s (1800 cycles). Using this MOIC, high‐performance triboelectric nanogenerator (TENG) is constructed (MOIC‐TENG) to harvest small mechanical energy even the MOIC electrode underwent an extremely low temperature. In addition, multifunctional flexible/wearable sensors (strain sensor, piezoresistive sensor, and tactile sensor) are realized to monitor human motions in real time, and recognize different materials by triboelectric effect. This study demonstrates a promising candidate material for flexible/wearable electronics such as electronic skin, flexible sensors, and human‐machine interfaces.