Soft and Strong: Elastic Conductors with Bio‐Inspired Self‐Protection

Abstract

Skin is soft yet strong – a combination achieved by integrating compliant elastin with stiff but wavy collagen, producing non‐linear mechanical properties. Inspired by this structure, stiff conductive wires are engineered into sinusoidal patterns and mechanically interlocked them with highly elastic fibers using a reimagined woven fabric approach. The result is a highly conducting and stretchable yarn that also has high tensile strength – a combination that is attractive for wearable applications where comfort and durability are valued. With a diameter of ≈1 mm—comparable to many commercial yarns—the fabric‐based yarn exhibits low stiffness across a broad strain range (up to 270% under 2 N of force) while demonstrating a self‐protective transition to high stiffness and strength (up to 30 MPa) as it nears failure. Additionally, this yarn offers excellent flexibility, high strain tolerance (exceeding 500%), inherent breathability, and superior weavability. By tuning the number of elastic fibers and electrode fibers, it can further tailor these stretchable conductive yarns into strain‐insensitive connecting yarns (low impedance at MHz frequencies, GF = 0.0003) and mechanical sensing yarns with dual strain and proximity sensing capabilities. The integration of these functional yarns enables system‐level smart textile applications, such as wristband controllers.

A general reverse‐engineering approach is demonstrated for designing functional yarns that uses woven fabric architecture as a structural framework. The fabric‐based stretchable conductive yarns combine flexibility, high elasticity, low stiffness, self‐protection, and weavability with conventional textile processes. By fine‐tuning the number of elastic fibers and electrode fibers, this fabric‐based approach enables customized tuning of strain sensitivity in stretchable conductive yarns.

1. Introduction

Wearable devices capable of sensitively detecting physiological signals and efficiently transmitting user commands hold significant promise for applications in digital health and the Internet of Things (IoT).^{[ 1} , ² , ³ , ^{4 ]} However, their widespread adoption is impeded by limitations in wearability and durability. While mainstream wearable devices, such as smartwatches and wristbands, can collect physiological data and serve as human‐computer interfaces, their rigidity, weight, and bulkiness can result in discomfort, thereby compromising long‐term wearability and user experience.^{[ 5 ]} Thin‐film‐based flexible electronics, such as electronic skin, are garnering increasing attention as they enable unobtrusive devices to be created.^{[ 6} , ^{7 ]} These advanced technologies offer reduced thickness, enhanced flexibility, and stretchability, enabling them to conform more naturally to the human body and accommodate a wide range of movements. Nevertheless, the use of films often results in limited breathability, posing a challenge to long‐term wearability and large‐area applications. Furthermore, like traditional wearable devices, they are typically deployed at single‐point locations, capturing only localized data. This restricts their ability to monitor multiple body areas, thereby limiting comprehensive and multidimensional data acquisition.

Building a large‐area distributed sensing network can enable coverage of the entire body or critical regions, allowing comprehensive and multidimensional physiological data collection. Through collaborative analysis of these signals, a more holistic view of health status and more precise disease diagnosis can be achieved.^{[ 8} , ⁹ , ^{10 ]} For human‐computer interaction, a distributed sensing network provides an enhanced interaction interface, delivering a more immersive and convenient experience. Smart textiles seamlessly integrate electrical functions with clothing, presenting a promising trajectory for the development of distributed sensing networks.^{[ 11} , ¹² , ^{13 ]} However, in contrast to wearable devices that operate at a single‐point location, smart textiles engineered for full‐body coverage encounter more complex stress conditions during daily wear.^{[ 14 ]} On one hand, the entire circuit system must possess excellent stretchability to ensure adequate wearing comfort and adaptability to human movements.^{[ 15 ]} On the other hand, the large‐area coverage of smart textiles increases the risk of tear damage, necessitating mechanically robust conductive pathways to enable effective self‐protection.^{[ 16 ]} Serpentine‐configured stretchable circuits present a promising strategy for constructing stretchable electronics.^{[ 17} , ^{18 ]} Nonetheless, products fabricated via conventional printing or cutting techniques often suffer from mechanical limitations, such as cracking susceptibility and weak interfacial adhesion.^{[ 19 ]} Moreover, insufficient breathability and poor weavability further restrict their integration with smart textiles. Spinning processes offer an additional alternative for producing stretchable conductive fibers; however, achieving a balance between high strength, high stretchability, and reliable electrical performance continues to be a significant challenge.^{[ 20} , ^{21 ]}

Compared to fiber fabrication, fabric manufacturing offers substantially enhanced design flexibility. Traditional fabric manufacturing techniques interweave one‐dimensional fibers to create a diverse range of two‐dimensional or three‐dimensional fabrics, a process that can be conceptualized as dimensional enhancement. Conversely, we propose a dimensionality reduction strategy that derives one‐dimensional yarns from fabric architectures. This reverse process harnesses the sophisticated structural design capabilities inherent in fabric manufacturing for the fabrication of fibers or yarns, substantially broadening the design space for stretchable conductive yarns. This approach is theoretically viable: by diminishing the width and thickness of fabrics, it is possible to attain a one‐dimensional structure that exhibits flexibility and weavability akin to conventional fibers or yarns.^{[ 22 ]} The resultant fabric‐based yarns can subsequently be subjected to secondary textile manufacturing processes, enabling the production of a diverse array of functional fabrics. Conductive yarns developed via this strategy can achieve the necessary functionalities while retaining the adaptability essential for system integration.^{[ 23 ]}

Mimicking the structure of natural skin, we employed a commonly used plain‐woven structure to interweave stiff electrode fibers with elastic fibers, where the elastic fibers serve as the warp yarns and the electrode fibers as the weft yarns. This hybrid fiber configuration imparts the classic fabric structure with enhanced mechanical properties, enabling our fabric‐based stretchable conductive yarns to exhibit a low‐stiffness, comfortable state during normal wear, while transitioning to a high‐stiffness, high‐strength self‐protective mode under destructive stress. Moreover, our fabric‐based stretchable conductive yarns possess excellent flexibility, large strain tolerance, dimensions and weavability comparable to commercial‐grade yarns, as well as inherent breathability. By fine‐tuning the number of elastic fibers and electrode fibers, we can engineer the strain‐sensing ability of the stretchable conductive yarns. The stretchable connecting yarn (SCY) configured at a 2:1 demonstrates superior electrical conductivity (1897.39 S cm⁻¹) and strain insensitivity (relative impedance change ΔZ/Z = 0.09%, including both resistive and inductive components). The mechanical sensing yarn (MSY) configured at a 3:2 exhibits dual‐mode sensing capabilities, including both strain sensitivity and proximity sensitivity (sensing distance: 8 mm). We developed a system‐level smart wristband fabric by integrating MSYs and SCYs. This wristband is capable of monitoring and recognizing muscle movements while also functioning as a human–computer interface for applications such as remote control, gaming interactions, and music playback. As one of the most widely adopted fabric structures, the woven fabric structure ensures the broad applicability and accessibility of this approach.^{[ 24 ]} The high degree of automation and cost‐effectiveness inherent in textile manufacturing further enhance the efficiency and scalability of the strategy, making it well‐suited for the large‐scale production of smart garments.

2. Results and Discussion

2.1. Design Strategy of Fabric‐Based Stretchable Conductive Yarn

Our design was inspired by the structure–mechanics relationship observed in natural skin (Figure 1a).^{[ 25} , ^{26 ]} Skin achieves a combination of low stiffness, high stiffness, and enhanced strength through the coordinated interaction of collagen fibers, elastin fibers, and their characteristic wavy configurations. In the unstressed state, collagen fibers adopt a curved morphology, allowing the skin to exhibit low initial stiffness. Upon stretching, these fibers gradually straighten, resulting in a transition to a high‐stiffness and high‐strength regime. Simultaneously, elastin fibers provide the skin with elasticity, enabling it to recover its original shape after deformation. This hybrid and adaptive structural organization forms the basis of our mechanical design strategy. In Figure 1b, we provide a schematic illustration of the framework for reverse‐designing stretchable conductive yarns based on woven fabric. We initially adopted the plain‐woven structure to assemble the elastic fibers (warp) and electrode fibers (weft). By adjusting the fiber number, we fabricated MSY (3 warp: 2 weft) and SCY (2 warp: 1 weft) (Figure S1, Supporting Information). Leveraging the interlaced architecture of the woven fabric, the electrode fibers were securely fixed into sinusoidal configurations. Combined with the excellent shape‐recovery capabilities of the elastic fibers, both MSY and SCY achieved highly repeatable stretchability and self‐protective functionality, effectively mimicking the mechanical properties of natural skin. Notably, they retained a one‐dimensional yarn form, making them well‐suited for secondary weaving processes. When integrated with microcontroller units (MCU) and machine learning algorithms, these yarns can serve as building blocks for system‐level smart textiles (Supplementary Note 1, Supporting Information).

Figure 1.

a) The structure and mechanical behavior of skin inspired the design of our stretchable conductive yarn with self‐protective capability. The collagen is stiff while the elastin is soft and elastic. The waviness of the collagen enables the structure to maintain low stiffness (regions I and II of the stress/strain curve at right) until strain is sufficient that collagen is straightened, leading to high stiffness (region III). The elastin fibers enable elastic recovery. b) Design process of smart textiles based on fabric‐based stretchable conductive yarns. c) Image of the microcontroller unit (size: 2.3 cm * 1.7 cm). d) Microscopic images of MSY and SCY. e) An image demonstrating the strength of MSY: MSY can lift a 710 mL bottle of water. f) Comparison of mechanical performance with state‐of‐the‐art wearables.^{[ 27} , ²⁸ , ²⁹ , ³⁰ , ³¹ , ³² , ³³ , ³⁴ , ³⁵ , ³⁶ , ³⁷ , ³⁸ , ³⁹ , ⁴⁰ , ⁴¹ , ^{42 ]} g) Comparison of electrical conductivity with state‐of‐the‐art stretchable conductors.^{[ 37} , ³⁸ , ³⁹ , ⁴³ , ⁴⁴ , ⁴⁵ , ⁴⁶ , ⁴⁷ , ⁴⁸ , ⁴⁹ , ^{50 ]}

In Figure S2 (Supporting Information), we illustrate the fabrication processes of MSY and SCY. Initially, a copper layer was deposited onto the surface of carbon nanotube (CNT) fibers via electrochemical deposition, using copper foil as the anode and a CuSO₄ solution as the electrolyte. Subsequently, cotton fibers were wrapped around the Cu/CNT fibers through a yarn‐winding technique. The resulting fiber was then encapsulated with an Ecoflex GEL coating to form the electrode fiber (Figure S3, Supporting Information). Finally, the electrode fiber was integrated with elastic rubber fibers using a weaving process to fabricate the fabric‐based stretchable conductive yarn. The purpose of copper deposition is to improve the electrical conductivity of the CNT fibers. The Ecoflex coating acts as a protective layer, shielding the conductive fibers from damage and sweat‐induced interference. Additionally, the cotton fiber layer facilitates the efficient and uniform coating of Ecoflex gel on the fiber surface.

A miniaturized printed circuit board (PCB) was developed to serve as the MCU (Figure 1c), enabling signal acquisition and wireless transmission from MSY and SCY. With a compact form factor of 2.3 cm × 1.7 cm, this design greatly reduces potential discomfort to the human body, thereby enhancing wearability. By reducing the dimensions, our fabric‐based yarns successfully achieve fineness comparable to commercial yarns (Figure S4, Supporting Information), demonstrating large elongation relative to nylon, acrylic, polyester and cotton yarns, and high strength compared to spandex. The fabricated MSY (2 mm width × 0.5 mm thickness) and SCY (1.2 mm width × 0.8 mm thickness) successfully achieved a one‐dimensional configuration (Figure 1d), maintaining high flexibility while retaining the intrinsic advantages of both CNT fibers and rubber fibers. These conductive yarns exhibit excellent deformability and load‐bearing capacity. The MSY, with a fineness of only 805 tex, is capable of supporting the weight of a 710 mL water bottle (Figure 1e; Figure S5, Supporting Information). It demonstrates a tensile strength of ≈30 MPa and a strain exceeding 100% (Figure 1f). In comparison, the SCY, with a fineness of 966.15 tex, exhibits a slightly lower tensile strength (>15 MPa) but an exceptional strain capacity exceeding 550%. These properties, particularly the mechanical strength, offer significant advantages over those of wearable devices reported in recent literature (Table S1, Supporting Information),^{[ 27} , ²⁸ , ²⁹ , ³⁰ , ³¹ , ³² , ³³ , ³⁴ , ³⁵ , ³⁶ , ³⁷ , ³⁸ , ³⁹ , ⁴⁰ , ⁴¹ , ^{42 ]} ensuring the wearing comfort, durability, and robustness of the fabric‐based conductive yarns in practical applications. We also compared the strain insensitivity of SCY with other reported stretchable conductors (Table S2, Supporting Information).^{[ 37} , ³⁸ , ³⁹ , ⁴³ , ⁴⁴ , ⁴⁵ , ⁴⁶ , ⁴⁷ , ⁴⁸ , ⁴⁹ , ^{50 ]} SCY exhibits excellent signal transmission capability, with a conductivity approaching 2000 S cm⁻¹ (Figure 1g). Within a strain range of up to 300%, the relative change in impedance is as low as 0.09%. Notably, the measured impedance includes both resistance and inductance components, and both remain remarkably stable under large deformations. Human motion typically induces large deformations at joints (e.g., the skin can sustain strains up to 150%^{[ 25 ]}), demanding wearable devices with exceptional strechability and robust electrical performance. The impedance stability not only eliminates signal interference from SCY to MSY during wearing but also ensures efficient and reliable circuit performance. In particular, the stable inductance overcomes the inherent limitations of helical stretchable conductive yarns.^{[ 51} , ⁵² , ^{53 ]}

2.2. Performance of MSY and SCY

We first investigated the electrical properties of SCY. As illustrated in Figure 2a, the electrode fibers in MSY and SCY exhibit a serpentine morphology and are mechanically interlocked with the axially aligned elastic rubber fibers, forming a stable fabric architecture. The electrode fibers feature a multilayered core–shell structure. As shown in Figure 2b, the deposition of copper onto CNT fiber surfaces results in a slight increase in fiber diameter and a transition in surface texture from smooth to rough. At the yarn scale, bridging structures are observed between adjacent fibers (highlighted by yellow arrows), which, in conjunction with copper's superior intrinsic conductivity, significantly enhance the electrical performance of the Cu/CNT fibers. Compared to pristine CNT fibers, the line resistance of Cu/CNT fibers is reduced by nearly 95%, thereby ensuring the excellent electrical conductivity of SCY. We systematically investigated the effects of deposition time, current level, and electrolyte concentration on the electrical resistance of Cu/CNT fibers. Our results show that even a short deposition time can markedly improve fiber conductivity. With respect to current and electrolyte concentration, increasing either parameter enhances copper deposition efficiency under low‐current or dilute‐electrolyte conditions. However, once a certain threshold is reached, further increases become cost‐inefficient and may even induce adverse effects, thereby limiting the overall benefits of excessive current or electrolyte concentration (Figure 2c; Figure S6, and Supplementary Note 2, Supporting Information). Figure 2d demonstrates the relationship between the impedance of SCY and applied strain. Within a broad strain range of 0% to 300%, SCY exhibits only a slight increase in relative impedance during the initial stage of stretching (less than 0.1%), after which the impedance remains essentially constant. This indicates that the influence of strain on the impedance of SCY is negligible. The phase angle of SCY is measured at 0.2°, suggesting a minimal inductive component. Furthermore, the inductance remains stable throughout the stretching process, ensuring the reliable operation of MCU under high‐frequency conditions.^{[ 54} , ^{55 ]}

Figure 2.

Performance characterization of MSY and SCY. a) Schematic illustration of the electrode fiber structure. b) SEM images of pristine CNT yarn and Cu/CNT yarn. c) Influence of copper deposition time on electrical conductivity of Cu/CNT yarns. d) Strain insensitivity characterization of SCY impedance at MHz. e) Strain response characteristics of MSY. f) Proximity response capability of MSY. g) Resistance variation of SCY during 10 000 stretching cycles. h) Capacitance response of MSY during 10 000 stretching cycles. i–k) COMSOL simulation of electric potential i), surface charge density j), and electric field intensity k) variation during the stretching process of MSY.

We then investigated the electrical properties of MSY. In contrast to SCY, MSY incorporates two serpentine‐configured electrode fibers arranged to form a capacitive sensing structure (Figure 2a). During stretching, the serpentine fibers progressively straighten, resulting in a change in the inter‐electrode distance, which enables effective and sensitive strain detection. Figure 2e presents the strain‐responsive behavior of MSY under tensile deformation. The double‐serpentine configuration endows MSY with a unique dual‐phase strain response pattern. At the initial stage, increasing strain leads to a decrease in relative capacitance. However, beyond a strain threshold of ≈40%, further stretching results in an increase in relative capacitance. This dual‐phase response enables MSY to operate under multiple application modes. For time‐series tasks—such as those involving machine learning—this non‐monotonic response provides a richer set of features for training datasets.^{[ 56} , ^{57 ]} For sensing applications that prioritize monotonicity and linearity, MSY can be pre‐stretched to 40% for effective calibration. Owing to the low stiffness of MSY within the 90% strain range (Figure S7, Supporting Information), applying a 40% pre‐strain introduces only minimal tension (≈0.35 N), which does not compromise comfort in wearable scenarios. We also introduced a “fitting‐derivative” curve transformation method to linearize the relationship between the measured capacitance response value (y) and strain (x), as shown in Figure 2e. First, we applied quadratic polynomial fitting to the raw data to derive a quadratic function describing the y − x relationship (y = ax ² + bx + c). Next, we computed the first derivative of this function, yielding a linear relationship between the derivative y′ and x (y′ = 2ax + b). Finally, by calculating y′, we could determine the real‐time strain value x, enabling more efficient measurements and analysis (Supplementary Note 3, Supporting Information). By locating the capacitance “valley”, we can determine whether the strain occurs during the stretching process or the recovery process (Supplementary Note 4 and Figure S8, Supporting Information).

In addition to strain sensitivity, MSY also possesses proximity sensing capabilities (Figure 2f). When a human palm approaches MSY, a significant decrease in capacitance is observed. The sensing range of MSY reaches ≈9 mm, and from the onset of detection to full contact with the palm, the relative capacitance change reaches nearly 20%. We evaluated the proximity sensing performance of MSY with various materials (Figure S9, Supporting Information) and observed that only conductive materials induced a notable change in capacitance. This finding suggests that the proximity sensing capability of MSY primarily stems from capacitive coupling between the conductive object and the two electrode fibers, rather than from variations in the dielectric constant of the surrounding medium.^{[ 58 ]} Figure S10 (Supporting Information) demonstrates the capability of MSY to discriminate between strain and proximity signals. In this experiment, a human palm was brought near MSY while it was concurrently subjected to tensile deformation. Owing to the distinct response profiles of MSY to strain and proximity stimuli, the two signal components can be readily distinguished based on their characteristic waveform features.

Finally, we investigated the durability and robustness of MSY and SCY. We first assessed the sweat‐adaptive capability of MSY (Figure S11a, Supporting Information) and found that the Ecoflex coating effectively protects the electrode fibers, preventing sweat ions from inducing short circuits. Owing to the high sensitivity of capacitive sensors to dielectric properties, sweat infiltration markedly increases the capacitance of MSY—by several tens of times—since the dielectric constant of saline solution (≈60–70) is far greater than that of air (≈1). As the sweat evaporates, air gradually refills the inter‐fiber gaps, allowing the capacitance to return to its initial baseline. This dynamic process enables MSY to not only resist sweat‐induced failure but also monitor sweat secretion and evaporation in real time. Importantly, despite the pronounced baseline shift caused by sweat, MSY consistently retains its capacitive sensing functionality for mechanical stimuli such as touch. The resulting multimodal sensing capability—encompassing touch, proximity, stretching, and sweat—enriches the signal features and thereby facilitates feature extraction and prediction by machine learning models. Leveraging this advantage, we employed a long short‐term memory (LSTM) deep learning model to classify muscle movement signals acquired by MSY under both dry and sweaty conditions (Figure S11b, Supporting Information). Benefiting from the richness of the multimodal features, MSY not only distinguishes different muscle motions with high accuracy but also reliably identifies the presence of sweat.

We then conducted 10 000‐cycle cyclic tensile tests on MSY and SCY to evaluate their mechanical robustness.^{[ 59} , ⁶⁰ , ^{61 ]} After cycling, SCY exhibited a relative resistance change of less than 8% (Figure 2g), whereas MSY maintained a stable capacitance response throughout the first 9 000 cycles (Figure 2h), underscoring their electromechanical durability. Notably, SCY showed a resistance increase during the initial stretching stage, which then stabilized—likely arising from partial filament fracture within the CNT fibers and damage to the copper coating and warming caused by wear. The increased sensitivity observed in MSY during the final stage of testing is attributed to structural damage within the fibers. To further elucidate the failure mechanisms, we compared the microscopic morphologies of MSY and SCY before and after cycling (Figure S12, Supporting Information). Consistent with the electromechanical results, after 10 000 stretching cycles, wear is observed in the elastic fibers of MSY, and one elastic fiber is broken, while SCY exhibited pronounced wear of both the elastomer and the copper layer (Figure S12c,f, Supporting Information). The wear of the insulating layer on the carbon conductors explains the increase in capacitance change with cycling, as seen in Figure 2h. The sudden increase in capacitance sensitivity after 35 h of cycling is likely due to increased compliance resulting from the failure of one of the three elastomer fibers (as seen in Figure S12f, Supporting Information). Increase in resistance of the SCY fibers with time may be due to wearing off of the conducting copper coating, and possibly results from temperature increases during cycling. Nevertheless, owing to the outstanding mechanical properties of CNT fibers and the interlocking nature of the woven structure, both MSY and SCY largely preserved their structural integrity as well as stable load‐ and strain‐bearing capabilities (Figure S12a,b,d,e, Supporting Information). The weaknesses of elastic fiber or elastomer coatings could be mitigated by employing alternative materials with enhanced durability, such as abrasion‐resistant thermoplastic polyurethane.

We performed 12‐h creep tests on MSY and SCY to assess their long‐term load‐bearing capability (Figure S13, Supporting Information). After 12 h under sustained loading, SCY exhibited nearly 45% creep strain—equivalent to ≈10% of its total strain capacity—whereas MSY showed less than 2% creep strain, accounting for only ≈1.5% of its total strain. Most of the creep occurred during the initial loading stage, after which both yarns gradually adapted and their strain responses stabilized. The markedly superior creep resistance of MSY can be attributed to its dual CNT electrode fibers (compared with the single electrode fiber in SCY) and its reduced deformation capacity. The dual electrode fibers engage at an earlier stage of loading, thereby restricting creep deformation within the overall structure. We also evaluated the washability of MSY and SCY (Figure S14, Supporting Information). To simulate a washing environment, we immersed the yarns in water with detergent and applied vigorous stirring using a magnetic stirrer. After 30 washing cycles, the capacitance of MSY remained essentially unchanged, while the resistance of SCY increased rapidly during the first few cycles remaining within 8% change before gradually stabilizing. This degradation is primarily caused by the exposed ends of the electrode fibers, which are required for connection to the MCU. Without elastomer coating protection, the copper layer detaches during washing, leading to increased resistance of the electrode fibers.

Furthermore, we compared the mechanical and electrical properties of fabric‐based stretchable yarns with different configurations. In addition to MSY (3 elastic:2 electrode plain‐woven structure), we fabricated a 3:2 twill‐woven yarn (3 elastic:2 electrode) and a 2:2 plain‐woven yarn (2 elastic:2 electrode) (Figure S15, Supporting Information). The electric responses, mechanical performance, and morphological features of these yarns are shown in Figure 2e and Figures S7 and S15 (Supporting Information). Similar to MSY, both the 3:2 twill‐woven and 2:2 plain‐woven yarns exhibit good stretchability, self‐protection capability, and dual‐phase capacitive response characteristics. However, configuration adjustments significantly modified their ultimate strain and sensing response ranges. As shown in Figure S15b,e (Supporting Information), the 3:2 twill‐woven yarn achieved an elongation at break of ≈140%, while the 2:2 plain‐woven yarn exceeded 200%—both representing clear improvements over MSY (≈110%). In terms of sensing behavior, while all yarns displayed dual‐phase capacitive responses, their relative capacitance ranges and transition strains varied notably (Figure S15c,f, Supporting Information). MSY showed a relative capacitance range of −6 to 6 with a transition strain near 40%. The 3:2 twill‐woven yarn exhibited a range of −4 to 8 with a transition strain around 50%, while the 2:2 plain‐woven yarn demonstrated a range of −12 to 0 with a transition strain of ≈80% (Table S3, Supporting Information). These differences arise from variations in electrode fiber waviness, which directly influences both stretchability and electric field distribution. A greater effective electrode fiber length stored per unit yarn length (corresponding to higher waviness) enhances yarn stretchability while also concentrating the electric field between electrodes, thereby shifting the sensing performance.

In Figure 2e, the capacitance–strain curve of MSY exhibits a distinctive dual‐phase behavior. To elucidate the underlying mechanism, we conducted an in‐depth analysis of the system's electrical response. As illustrated in Figure S16 (Supporting Information), the double‐serpentine electrode configuration of MSY can be divided into two regions: the crossover region and the non‐crossover region. This structural segmentation gives rise to two distinct electric field distributions, referred to as Field I (crossover) and Field II (non‐crossover). MSY can be regarded as a parallel connection of multiple capacitors of these two types (Figure S16a,b, Supporting Information). At the initial stage of stretching, the two electrode fibers remain relatively close in the crossover regions, resulting in Field I dominating the overall electric field distribution. As the strain increases, the electrode fibers in the non‐crossover regions progressively move closer together, leading to a gradual enhancement of Field II. Simultaneously, the deformation of the electrode fibers weakens the influence of Field I. This shift in the dominant electric field causes a decrease in capacitance, corresponding to the first stage of MSY's capacitance‐strain response. With continued stretching, Field II eventually becomes the prevailing electric field, marking the onset of the second stage, during which the capacitance begins to increase (Figure S16c, Supporting Information). To validate our hypothesis, we developed a finite element model using COMSOL Multiphysics. A three‐dimensional model of the double‐serpentine configuration of MSY was constructed, with a sufficiently large surrounding air domain to accurately replicate real‐world conditions (Figure S17, Supporting Information). Consistent with our analysis, in the early stage of stretching, the electric field distribution (Figure S18, Supporting Information) and intensity (Figure 2k) of Field I dominate. As the stretching progresses, Field II gradually becomes the dominant electric field. The constant potential of the electrode fibers (Figure 2i), along with the uneven distribution of surface charge density (Figure 2j), further corroborates our analysis (See Supplementary Note 5, Supporting Information, for more details).

2.3. Mechanical Analysis of Fabric‐Based Stretchable Structure

The fabric‐based stretchable structure leverages mechanical interlocking between fibers, which not only preserves excellent stretchability but also imparts outstanding mechanical strength and breathability. More importantly, this architecture inherently eliminates interfacial vulnerabilities, thereby addressing common concerns related to conductive trace damage. In contrast to conventional printed serpentine structures on elastic substrates^{[ 17} , ^{62 ]}—which are prone to delamination, cracking, and performance degradation under repeated deformation—the fiber‐interlocked system ensures superior mechanical integrity and stable electrical performance. Moreover, the intrinsic breathability and softness of textile‐based platforms greatly enhance wearer comfort, rendering them highly suitable for long‐term wearable applications. We employed a sine wave‐based model (y = asinbx) to represent the fabric‐based stretchable structure and analyzed the relationship between its structural configuration and mechanical performance (Figure 3a; Figure S19a, Supporting Information). In this structure, high‐modulus and low‐modulus fibers are interlaced in an alternating over–under pattern, forming a mechanically interlocked and structurally stable system. The high‐modulus fibers are essentially inextensible, whereas the low‐modulus fibers exhibit excellent elasticity. The difference between the arc length (L) and the period length (T) constitutes the primary source of stretchability in the fabric‐based structure. Simultaneously, the incorporation of low‐modulus fibers facilitates superior shape recovery, enabling the structure to return to its original configuration following deformation (see Supplementary Note 6, Supporting Information, for more details). In practical weaving processes, T is typically difficult to modify, as it is primarily determined by the diameters of the two types of fibers and is constrained by the requirement to maintain a tightly interlaced structure. In contrast, the yarn width (a), which is directly related to L, emerges as the key parameter for tuning the ultimate strain of the stretchable fabric structure (Figure S19b,c, Supporting Information). In Figure S19d (Supporting Information), we show the structural evolution of MSY during stretching. As strain increases, the electrode fibers gradually transition from a curved to a straightened state, while the elastic fibers conversely shift from straight to bent. A key determinant of yarn stretchability is the effective length of electrode fiber stored per unit yarn length. This stored length is indirectly governed by yarn width and the waviness of the electrode fibers, both of which modulate the degree of stretchability in the overall structure.

Figure 3.

Mechanical analysis of fabric‐based stretchable structure based on finite element analysis. a) A schematic model based on the sine function. b) Comparison of load‐bearing mechanisms between truss‐element and solid‐element yarn models with consideration of bending stiffness. c) Comparison of loading behavior between fabric‐based stretchable structure and thin‐film stretchable structure (unit: MPa). d,e) Effect of fiber modulus on the mechanical performance of fabric‐based stretchable structure. d) shows the effects of modulus variation in high‐modulus fiber, while e) represents the effects of modulus variation in low‐modulus fibers. f) Effect of yarn width on the mechanical performance of fabric‐based stretchable structure.

In practical applications, the serpentine structure alone does not solely govern stretchability. A typical example can be observed by contrasting a bent yarn with a spring: straightening the bent yarn requires minimal force, whereas stretching a spring demands significantly more effort. We conducted finite element analysis using ABAQUS to investigate the mechanical response mechanisms of the fabric‐based stretchable structure. The geometric model is shown in Figure S20, Supporting Information. Considering the high nonlinearity of the model, the explicit dynamics solver in ABAQUS was employed for the simulation. We first examined the effect of the analysis step time on the results and found that when the time exceeds 30 seconds, the model transitions from dynamic to quasi‐static stretching (Figure S21a,b, Supporting Information).^{[ 63 ]} Subsequently, under quasi‐static conditions, truss elements and solid elements were used to simulate fibers with negligible and finite bending stiffness, respectively.^{[ 64 ]} By comparing the two models, we evaluated the influence of fiber bending stiffness on the mechanical response of fabric‐based stretchable structure. As shown in Figure 3b, regardless of whether a truss or solid element model is used, the mechanical load under strong tensile deformation is primarily borne by the high‐modulus fibers in the fabric‐based stretchable structure. In the truss model, due to the absence of bending stiffness, the high‐modulus fibers exhibit a uniform stress distribution. In contrast, the presence of bending stiffness in the solid model leads to stress inhomogeneity within the high‐modulus fibers, as the internal stress is also influenced by inter‐yarn compression. This difference results in a corresponding divergence in mechanical behavior: the absence of bending stiffness enables the stretchable structure to exhibit a broader low‐stiffness regime (Figure S21c, Supporting Information).

We also compared the mechanical response behaviors of the fabric‐based structure and the printed serpentine structure during stretching (Figure 3c). Owing to the presence of air gaps between fibers, the fabric‐based stretchable structure allows individual fibers to straighten and move closer to each other under tension, forming a unified rope‐like structure. This behavior originates from the woven architecture and is independent of the bending stiffness of the fibers (Figure S22, Supporting Information). In contrast, during the elongation of printed serpentine patterns, the shear‐induced deformation energy cannot be effectively released within the plane due to spatial constraints, leading to out‐of‐plane distortion of the elastic substrate. Such deformation compromises conformability in wearable applications and negatively affects the intimate contact between the electronic skin and the human body. Moreover, to ensure a fair comparison between the two structures, the simulation models were based on idealized assumptions for the printed serpentine configuration, including sufficient mechanical strength and robust interfacial adhesion. In real‐world applications, however, planar printed structures are highly susceptible to conductive trace cracking and interfacial delamination, which further limit their applicability and long‐term reliability in wearable scenarios. To mitigate these issues, elastomer‐based electronic skins typically require additional interface engineering or the incorporation of kirigami‐inspired designs, both of which help alleviate stress concentration and enhance structural durability.

Finally, we investigated the influence of material parameters on the mechanical performance of the fabric‐based stretchable structure. During stretching, the structural rearrangement between the elastic and electrode fibers gives rise to three distinct stages in the tensile response of the yarn (Figure 3d,e). In the first stage, neither the high‐modulus nor low‐modulus fibers are fully straightened, resulting in a low‐stiffness region. In the second stage, the low‐modulus fibers become fully extended and dominate the mechanical response. Owing to their intrinsic low modulus, this stage also exhibits low stiffness, similar to the first. In the third stage, the high‐modulus fibers are fully straightened and begin to bear the load, leading to a sharp increase in structural stiffness. Overall, the high‐modulus fibers primarily influence the third stage (Figure 3d), while the low‐modulus fibers play a dominant role in the second stage (Figure 3e). We further simulated the effect of yarn width on the mechanical behavior of the fabric‐based stretchable structure (Figure 3f). Consistent with our previous analysis (Figure S19, Supporting Information), increasing the yarn width significantly enhances the ultimate strain of the structure. Notably, the improvement is primarily observed in the extension of the low‐stiffness region, which is particularly advantageous for maintaining comfort in wearable applications (for more details, see Supplementary Note 7, Supporting Information).

2.4. Wearable Applications

Conductive yarns based on fabric‐based stretchable structure can be used for secondary weaving, retaining the system integration capability. We fabricated a smart wristband fabric using the prepared fabric‐based stretchable conductive yarns and integrated four MSYs onto it, enabling multi‐channel sensing and human‐computer interaction (Figure 4a; Figure S23, Supporting Information). Figure 4b,c display the microscopic morphology of MSY and SCY when integrated into smart fabric systems. It can be observed that both MSY and SCY have comparable dimensions and flexibility to commercially available yarns, allowing them to conform well to the shape of the human wrist. As shown in Figure 4d, we designed a breathability testing protocol to assess the air permeability of the smart fabric. The fabric and a sheet of white paper were placed sequentially on the testing apparatus, and air was pumped from beneath the fabric using an air tube. The air easily passed through the fabric, lifting the white paper above it, thereby demonstrating the fabric's exceptional breathability (Figure 4e; Movie S1, Supporting Information). This characteristic ensures long‐term wearing comfort during use. We evaluated the sensing performance of MSY after its integration into the wristband fabric. Benefiting from the high sensitivity of its capacitive field, MSY is capable of detecting stretching along multiple directions across the wristband (Figure S24, Supporting Information). In addition, we assessed the mechanical robustness of the connection between the electrode fibers and the MCU (Figure S25, Supporting Information). Since CNT yarns cannot be directly soldered, we first crimped the yarns to establish a reliable mechanical interface, followed by soldering the crimped terminal to the MCU. To further enhance stability, the joint was reinforced with conductive adhesive (Silicone Solutions SS‐26), ensuring both mechanical durability and electrical reliability. The resulting connection can sustain a load of ≈2 N and withstand more than 100 cycles of cyclic loading under 0.5 N.

Figure 4.

Smart wristband incorporating fabric‐based stretchable conductive yarns and its application in muscle motion monitoring. a) Smart wristband features four integrated MSYs. b,c) Morphological images of MSY b) and SCY c) incorporated into the fabric. d) Schematic diagram of the fabric breathability test. e) When air is pumped through the air tube beneath the fabric, it easily passes through the fabric and lifts the white paper placed on top, demonstrating the excellent breathability of the smart wristband fabric. f) Signals elicited from 11 distinct arm movements. The horizontal axis represents time (s), while the vertical axis represents the output signal. g) Schematic diagram of the hybrid CNN‐LSTM deep learning model for arm movement classification. h) Confusion matrix of arm movement classification results.

The wrist serves as a bridge connecting the palm and the forearm. The smart wristband embedded with MSY integrates both strain and proximity sensing capabilities. It not only enables direct monitoring of wrist movements but also captures the muscle activities of the entire arm by detecting the morphological changes transmitted through the wrist ligaments (Figure S26, Supporting Information).^{[ 65 ]} We evaluated the smart wristband by testing 11 distinct movements (using Channel 4). The system accurately distinguished between different motions by generating unique signal waveforms, regardless of whether the action involved pronounced joint movement or subtle muscle activation (Figure 4f; Figure S27, Supporting Information). For instance, Movements 9 and 10 both involve clenching and then opening the fist. Although they appear identical, the differences in muscular exertion result in distinguishable signal patterns. This fine‐grained motion detection ability enables differentiation of arm movements and, when combined with deep learning classification algorithms, offers promising potential for assisting in scientific training and reducing the risk of sports injuries.

We classified 11 distinct motion categories (as labeled in Figure 4f) using sensor data collected from the smart wristband. An integrated data classification pipeline was developed, combining advanced preprocessing, continuous wavelet transforms, and a hybrid CNN–LSTM architecture. Raw time‐series signals were segmented into 250‐sample windows, with each file contributing up to 500 segments. The segments were first smoothed using a Savitzky–Golay filter (window length 11, polynomial order 2), then normalized via z‐score. To enhance model robustness, data augmentation techniques were applied, including random amplitude scaling (scaling factors between 0.9 and 1.1), Gaussian noise addition (with a standard deviation proportional to 1% of the segment's standard deviation), and random circular shifts (±5 samples). Segments exhibiting a single dominant peak (minimum prominence 0.3 within the central 20% margin) were selected for further processing. These filtered segments were transformed into scalograms using a continuous wavelet transform (scales 1 to 50 with a complex Morlet wavelet, with an example result in Figure S28, Supporting Information). The resulting time–frequency representations were input into a hybrid CNN–LSTM model, consisting of two convolutional layers (32 and 64 filters with 3 × 3 kerners) with max pooling, followed by an LSTM layer with 128 units. A dense classification head with 128 neurons, dropout (rate = 0.4), and a final softmax output layer completed the architecture (Figure 4g; Figure S29, Supporting Information). The model was trained using the Adam optimizer with early stopping (patience = 5 epochs) and a learning rate scheduler (reduction factor = 0.5, patience = 3 epochs). This pipeline achieved test accuracies exceeding 90% (Figure 4h; Figure S30, Supporting Information).

To gain deeper insights, we applied t‐distributed Stochastic Neighbor Embedding (t‐SNE) to the features extracted from the penultimate dense layer, enabling visualization of the high‐dimensional feature space in two dimensions. As shown in the t‐SNE plot (Figure S31, Supporting Information), the embeddings formed well‐defined clusters corresponding to different motion classes, highlighting the model's ability to capture discriminative features. Notably, along dimension 1, class 9 (make a strong fist and open it) and class 10 (make a gentle fist and open it) were widely separated, suggesting that the degree of muscular exertion during fist formation results in distinct neuromuscular activation patterns. Along dimension 2, class 1 (forward pushing) and class 4 (arm muscle movement) were also clearly separated, likely reflecting the different biomechanical mechanisms between generating linear force through proximal muscles versus isolated or localized arm muscle contractions. In contrast, classes 2 (forward hitting), 3 (finger bending), 5 (waving arms), 6 (wrist bending), and 7 (hand trembling) clustered together, indicating similar transient, multi‐joint dynamics typically associated with arm muscle activities. Class 0 (wrist twisting/turning) appeared relatively isolated from other categories, likely due to its unique rotational motion and muscle engagement. These clustering patterns not only validate the high discriminative capability of the model but also provide biophysical insights into the underlying neuromuscular strategies associated with different movement types. When two or more muscle movements occur simultaneously, the signals detected by MSY exhibit superposition, which introduces challenges in accurately recognizing individual motion patterns. In Supplementary Note 8 and Figure S32 (Supporting Information), we provide a signal recognition strategy based on Convolutional Neural Networks (CNNs) and Continuous Wavelet Transforms (CWTs), which can distinguish different muscle activities from continuous composite signals. This limitation can also be addressed by expanding the training categories. For example, finger movements, arm movements, and combined finger–arm movements can be defined as three fundamental classes for machine learning model training and testing. Owing to the high sensitivity of MSY to muscle activity, machine learning models can effectively distinguish not only discrete muscle movements but also their composite signals (Figure S32g, Supporting Information).

In the final part of this study, we explored the potential of the smart wristband for applications in flexible human–computer interfaces. Owing to the integration of multiple MSY sensing channels on the wristband, various command signals can be generated through different channel combinations, enabling a wide range of human–computer interaction functionalities, including remote control, gaming operation, and music playback (Figure 5a). Within the entire system, MSY‐based sensing channels serve as the primary interface for user input, capable of transmitting commands through multiple interaction modes, including strain, proximity, and touch. Meanwhile, SCY functions as a robust conductive interconnect, reliably delivering electrical signals generated by MSY to the MCU. The seamless coordination between MSY and SCY ensures accurate signal acquisition and stable transmission, forming a fully integrated fabric‐based platform that supports complex interactive functions in a highly wearable and user‐friendly format.

Figure 5.

Application schemes of smart wristbands as human‐computer interfaces. a) Smart wristband can be utilized for remote control and computer interaction, such as gaming controls and music playback functionalities. b) Schematic diagram of the circuit design for smart wristband in wireless human‐computer interaction. c) Flowchart illustrating the algorithmic principle for transforming signals from the smart wristband into human‐computer interaction commands. d) Controlling the movement of a wireless vehicle via smart wristband. e) Different action commands can be programmed by combining signals from multiple channels of smart wristband.

For accurate capacitance measurement, we employed the CapSense module integrated within an Infineon PSoC 6 series microcontroller. This was supported by a custom‐designed PCB incorporating the MCU, antenna, and power supply components. The system is powered by a 3.7 V nominal voltage LiPo battery, with a low‐dropout (LDO) regulator converting the voltage to 3.3 V, suitable for the MCU. The entire measurement and wireless transmission system operates with ultra‐low power consumption, requiring less than 20 mW. Figure 5b illustrates the working principle of the CapSense module. The module features an analog multiplexer that enables sequential scanning and measurement of multiple sensing channels. During operation, a transmit (Tx) signal initially charges each sensing capacitor. A capacitance‐to‐digital converter (CDC), composed of a constant current source and a timer, is then used to determine the capacitance. Specifically, the current source discharges the capacitor at a constant rate, and the timer records the discharge duration. The capacitance value is subsequently calculated based on the measured discharge time. After measurements are completed across all sensing channels, the collected data are packaged and transmitted via Bluetooth Low Energy (BLE) to the receiver. The receiver processes the incoming data to generate real‐time plots of sensor outputs and execute application‐specific functions.

Figure 5c presents the software flowchart on the receiver side, detailing the process of converting collected sensor data into actionable commands. Upon initialization, the program sets up the data visualization interface and establishes the Bluetooth Low Energy (BLE) communication protocol. A dedicated readout thread is launched to continuously receive incoming BLE data from the transmitter. Meanwhile, the main thread enters an animation loop responsible for real‐time data visualization and application‐level command execution. Within this loop, sensor data received by the readout thread are continuously accessed, processed, and used to update dynamic plots and initiate specific actions. To identify sensor activation events, a rising‐edge detection algorithm is employed across all sensing channels. This algorithm enables precise identification of signal transitions indicative of user interaction or mechanical input. Additionally, a cooldown mechanism is implemented to prevent excessive or unintended triggering by enforcing a minimum time interval between successive activations.

Using a remote‐controlled car as a demonstrative example, we successfully employed the smart wristband to command various actions, including forward/backward movement, left/right turning, and twisting, as illustrated in Figure 5d,e and Movie S2 (Supporting Information). Figure S33 (Supporting Information) presents the remote‐controlled car circuit, built on a brown PCB where each pushbutton corresponds to a unique command. For each pushbutton, two wires are soldered onto opposite terminals and connected to the output side of an optoisolator. The input side of the optoisolator is wired to an Arduino output pin. Upon receiving and interpreting sensor signals from the smart wristband, the Arduino triggers the corresponding pin, activating the LED within the optoisolator. This illumination switches on the transistor on the output side, short‐circuiting the two wires to simulate a physical button press. The same control strategy is extended to other human–computer interface applications, such as game controllers and music players (Figure S34, Supporting Information). In the game controller demonstration (Movie S3, Supporting Information), the smart wristband is used to play Flappy Bird, where activation of channel 2 (middle sensor) emulates a ″spacebar' keypress on a Windows device, triggering a jump action. For music playback (Movie S4, Supporting Information), coordinated wrist movements activate channels 1, 2, and 3 simultaneously, simulating the ″Ctrl+Alt+P' hotkey to pause or resume music. This multi‐channel logic allows intuitive, gesture‐based control over digital systems. The entire interface is built on a textile‐based structure, endowing the wristband with excellent flexibility, breathability, and conformability to the skin. These characteristics enable long‐term, comfortable wear, ensuring that users can interact with the system naturally and continuously without sacrificing mobility or comfort. The user‐friendly nature and real‐world applicability of this wearable interface highlight its strong potential in next‐generation flexible electronics and human–computer interactions.

3. Conclusion

We present a strategy for developing stretchable conductive yarns with self‐protection based on woven fabric structures. By leveraging the interlacing mechanism of woven textiles, we achieve a mechanical interlock between elastic fibers and inextensible electrode fibers. The resulting conductive yarns not only exhibit structural stability but also combine flexibility, high stretchability, low stiffness, high strength, excellent mechanical repeatability, and inherent breathability and weavability. In these fabric‐based stretchable conductive yarns, the electrode fibers provide outstanding strength, while the elastic fibers contribute low modulus and shape recovery capabilities. The electrode fibers present a serpentine configuration, where the difference between the arc length and the period length determines the ultimate stretchability of the conductive yarns. By adjusting the number of elastic fibers and electrode fibers, we can tailor the design to create either strain‐insensitive stretchable connecting yarns or strain‐sensitive mechanical sensing yarns. The one‐dimensional yarn format enables seamless integration into secondary weaving processes, facilitating the fabrication of system‐level smart textiles. As a proof of concept, we developed a smart wristband fabric capable of monitoring and recognizing muscle movements while also supporting human‐computer interaction applications, including remote control, gaming, and music playback. This approach provides a universal and accessible fabrication method for stretchable yarn structures. Looking ahead, this strategy can be extended to develop novel electronic components in a stretchable yarn format, such as capacitors, inductors, and diodes, further expanding the capabilities of next‐generation electronic textiles.

4. Experimental Section

Materials

High‐strength CNT fibers (10 tow) were purchased from DexMat, USA. Elastic rubber fibers (280 D) were obtained from Changming Garment Co., Ltd., China. Copper (II) sulfate powder (CAS 7758‐98‐7) with a purity exceeding 99% was purchased from Sigma‐Aldrich, UK. Copper foil (0.1 mm thickness) was purchased from Jiangsu Gefang New Material Co., Ltd., China. Pure cotton yarn (60 s) was purchased from Changzhou Liwan Textile Co., Ltd., China. Ecoflex Gel 2 was purchased from Smooth‐on, Inc., USA. Commercial yarns (polyester, spandex, acrylic, nylon, and cotton yarn) are purchased from Amazon.

Fabrication of the Electrode Fiber

As shown in Figure S2 (Supporting Information), copper electroplating was first carried out on CNT fibers. The electroplating solution was prepared by dissolving 10 g of copper (II) sulfate in 100 mL of deionized water. Electrochemical deposition was performed using a copper foil as the anode and CNT fibers as the cathode, with a constant current of 0.05 A applied for 3.5 h to yield Cu/CNT fibers. Subsequently, a layer of cotton fibers was helically wrapped around the Cu/CNT fibers via a yarn winding process using a braiding loom. During this process, the cotton yarn was mounted on a yarn carrier attached to a rotating base, while the Cu/CNT fiber served as the core yarn. Upon initiation, the collector and rotating base operated simultaneously, enabling the cotton yarn to wrap tightly around the Cu/CNT core to form a composite yarn, which was then continuously collected. Finally, the surface of the cotton/Cu/CNT fibers was coated with an Ecoflex gel (prepared by mixing components A and B in a 1:1 ratio) through a yarn coating process. The coated yarns were cured at room temperature for 24 h to ensure full crosslinking and structural stabilization.

Fabrication of MSY, SCY

A weaving loom was used to fabricate both MSY, SCY, 3:2 twill yarn, 2:2 plain yarn, and the smart wristband. For MSY, three elastic rubber fibers were employed as warp yarns, and two electrode fibers served as weft yarns. Guided by the harness frames, the three warp yarns moved alternately up and down to create a shed. A weft yarn was inserted into each shed alternately—one in the first shed and the other in the next—repeating this process to construct the MSY structure. For SCY, two elastic rubber fibers were used as warp yarns, and a single electrode fiber acted as the weft yarn. The two elastic fibers alternated between upward and downward motion to form a shed, into which the electrode fiber was inserted. As the weaving process continued, the formed structure was simultaneously wound onto a collector, enabling continuous fabrication. The preparation of 3:2 twill yarn and 2:2 plain yarn is similar to that of MSY. The preparation of the smart wristband follows the same process as traditional 2D woven fabrics, with MSY and SCY integrated during the manufacturing process.

Characterization

A Leica DM2700 M zoom‐stereo microscope was employed to observe the structures of MSY, SCY, and the wristband fabric. SEM images of the CNT and Cu/CNT fibers were captured using a CHTP Helios Nanolab 650 system. The mechanical properties of MSY and SCY were characterized using an Instron 5969 microcomputer‐controlled universal testing machine. Electrical properties of MSY, SCY, 3:2 twill yarn, 2:2 plain yarn, and the electrode fibers were measured with an HP4275A LCR meter. Unidirectional tensile tests were performed on MSY, SCY, and other commercial yarns with a gauge length of 50 mm and a stretching speed of 100 mm/min, following the JIS L1096 standard for textile tensile testing. For cyclic tensile tests, the gauge length remained at 20 mm, with both stretching and releasing speeds set to 400 mm/min. The samples were stretched from their original length until the applied force reached 2 N, and this loading–unloading process was repeated for 10 000 cycles. During the cyclic tests, the real‐time capacitance of MSY and real‐time resistance of SCY were concurrently recorded to assess their durability. For the creep test, sustained loads of 8 N and 4 N were applied to MSY and SCY specimens, respectively, each with a 50 mm gauge length, using the Instron testing machine. The real‐time displacements were continuously recorded over a 12‐h period. To characterize the strain insensitivity of SCY, the impedance of the electrode fiber was measured under stretching at a frequency of 1 MHz using the HP4275A LCR meter. For the capacitance–strain response characterization of MSY, the real‐time capacitance between the two electrode fibers was recorded during uniaxial stretching at 20 mm min⁻¹, also measured at 100 kHz. For proximity sensing evaluation, MSY was fixed to the upper fixture of the Instron machine, while a hand was positioned palm‐up on the lower fixture at an initial distance of 20 mm. The upper fixture moved downward until a contact force of 0.1 N was reached, indicating full contact between MSY and the hand. The real‐time capacitance was recorded throughout this process to analyze proximity sensitivity. To assess material recognition capabilities, MSY was placed flat on different materials—including copper foil, human palm, fingertip, cotton fabric, ABS plastic sheet, PET fabric, and silk fabric—and the corresponding capacitance values were recorded. For each material, 20 independent measurements were conducted. For the washability test, 200 mL of clean water and an appropriate amount of detergent were added to a beaker containing MSY and SCY samples. The solution was stirred at 50 °C and 700 rpm using a magnetic stirrer to simulate a washing machine environment. The washing process was repeated 30 times, and after each cycle, the capacitance of MSY and the resistance of SCY were recorded. All experiments were conducted at a temperature of 20 °C and a relative humidity of 60%. For strength calculations of MSY and SCY, the cross‐sections are approximated as rectangular. The cross‐sectional area is determined from measurements of yarn width and thickness in the unstretched state, enabling the calculation of nominal stress. In the application section, the volunteer agreed to participate in the wearable monitoring experiments and signed the consent forms. Based on completion of UBC's Ethics Screening Tool, under Article 2.5 of Canada's Tri‐Council Policy Statement governing research involving human subjects this project has been deemed an ethics‐exempt program evaluation activity and therefore not subject to formal institutional ethical review. The MCU was custom‐designed and fabricated by an electronic prototyping manufacturer. A 3.7 V rated battery served as the power supply.

Conflict of Interest

The authors declare no conflict of interest.

Supporting information

Supporting Information

Supplemental Movie 1

Supplemental Movie 2

Supplemental Movie 3

Supplemental Movie 4

Zhang C., Yin X., Zhou C., et al. “Soft and Strong: Elastic Conductors with Bio‐Inspired Self‐Protection.” Adv. Mater. 38, no. 4 (2026): e12471. 10.1002/adma.202512471

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.