1 Introduction
Advances in technology have enabled the widespread application of intelligent flexible wearable devices in areas such as health monitoring， human-machine interfaces， and soft robotics
[1-2]. These devices must be bendable， foldable， twistable， and stretchable to satisfy demanding mechanical deformation matching requirements， posing significant development challenges
[3]. Among them， flexible mechanical sensors （FMSs）， which convert mechanical deformation into measurable electrical signals， offer a promising alternative by virtue of their flexibility， lightweight， and biocompatibility. Most traditional FMSs are commonly fabricated by integrating conductive materials （e.g.， carbon nanomaterials， metal nanomaterials， or conductive polymers） into elastic substrates， such as polydimethylsiloxane （PDMS）， Ecoflex， natural rubber， or thermoplastic elastomers
[4]. Despite their simple preparation and good conductivity， practical applications of FMSs often face challenges of limited sensitivity， poor biocompatibility， restricted detection range， and filler-matrix delamination
[5]. Therefore， there is a growing demand for softer， more sensitive， and highly stretchable mechanical sensors in next-generation wearable and portable electronics.
Advances in materials science have significantly accelerated the development of FMSs
[6-7]. These novel materials enable the realization of key device characteristics， such as high sensitivity， broad detection range， robust mechanical stability， and fast response times
[8]. Consequently， they facilitate cost-effective， comfortable， and multifunctional flexible electronic systems， which meet comprehensive demands for long-term biomonitoring.
Hydrogels are soft and moist materials with high water content and a three-dimensional （3D） elastic cross-linked polymer network
[9]. This structure allows effective permeation by various ions and molecules. Key properties of hydrogels include swellability， flexibility， high biocompatibility， and porosity
[4]. These properties can be adjusted during synthesis， making hydrogels suitable for use in wearable flexible electronic devices
[10]. Among these， conductive hydrogels （CHs） represent a highly promising class of functional materials， characterized by properties such as electrical conductivity， mechanical strength， self-healing， and freeze resistance
[9,11]. These features have facilitated notable progress in hydrogel-based mechanical sensors， which can detect external stimuli and monitor human biomechanical signals， such as real-time tracking of body movements and subtle physiological activities
[12-13].
To outline the structure and logic of this review， we present a framework diagram illustrating recent advances in CH-based FMSs （CHFMSs） in
Fig. 1. First， we introduce the representative synthesis materials of CHs， which determine the intrinsic properties of the hydrogel. Next， CHFMSs are classified according to different conduction mechanisms and are analyzed for their comprehensive performance， including key electromechanical properties， environmental stability， and other extended features. These parameters form the core metrics for evaluating sensor performance. Furthermore， we summarize the applications of CHFMSs across various practical scenarios. Finally， building on current advances， we outline the existing challenges in the field and propose potential directions for future development.
2 Types of CHs
2.1 Nanocomposite-based CHs
As a key advancement in flexible sensors， incorporating conductive nanofillers into a hydrogel matrix has emerged as one of the mainstream strategies for fabricating CHs
[14-15]. This approach allows straightforward tuning of the mechanical properties of the composite by varying the filler-to-matrix ratio， making it suitable for diverse applications. Building upon this foundation， the sensitivity of hydrogel-based FMSs can be further enhanced and optimized by leveraging physical mechanisms such as contact resistance， tunnelling effects， and capacitance variations. Depending on the fundamental nature of the nanofillers used， CHs can be classified into three main categories： metal nanomaterial
[14,16-17]， carbon nanomaterial
[18-20]， and emerging MXene （e.g.， 2D transition metal carbides/nitrides， such as Ti
3C
2T
x） hydrogels
[21].
2.1.1 Metal nanomaterial-based CHs
Metal nanomaterials possess high electrical conductivity， high surface energy， ease of fabrication， and favorable modification properties
[17]. Incorporating metal nanomaterials into hydrogels improves electrical conductivity， flexibility， and stretchability， thus representing one of the most promising strategies for developing CHs. Common metal nanostructures include nanoparticles （NPs）
[16]， nanorods （NRs）
[22]， and nanowires （NWs）
[23-24]， among others.
Owing to their exceptional electrical conductivity， favourable biocompatibility， high chemical stability and facile functionalization， gold nanoparticles （AuNPs） can effectively enhance the sensitivity of pressure sensors
[25-26]. Consequently， they have been widely adopted as one of the key materials for constructing high-performance sensors
[27-29]. For example， Yan et al.
[30] prepared a nanocomposite hydrogel by adding allyl mercaptan （ALM） functionalized Au nanoparticles into poly（
N-isopropylacrylamide-co-hydroxyethylmethacrylate）/poly（
N-isopropylacrylamide） semi-interpenetrating hydrogel network， as shown in
Fig.2a. Functionalized AuNPs can be dispersed more uniformly within the polymer network， thereby synergistically enhancing both the mechanical and electrical properties of the semi-interpenetrating hydrogel. A wearable strain sensor was developed using the composite hydrogel， exhibiting excellent stability and repeatability in the strain range of 0%~150%. This case proves the viability of a strategy that combines metallic nanomaterial functionalization with microstructure design of hydrogel to enhance the overall performance of sensors.
Once good dispersion is achieved， further optimizing the morphology of nano-fillers serves as an effective strategy to directionally enhance conductivity. As shown in
Fig.2b， Kiyotake et al.
[31] developed CHs by incorporating high aspect ratio citrate-gold nanorods （GNRs） into the matrix of pentenoate-functionalized hyaluronic acid （PHA） and gelatin （PGel）. The elongated shape of GNRs promotes an effective contact within a polymer matrix， forming an efficient percolation network. This enables a linear enhancement in the electrical conductivity of the hydrogel with increasing GNR content， effectively overcoming the issue of conductivity caused by the random aggregation of metal nanoparticles.
Furthermore， to achieve long-term anti-aggregation， Wang et al.
[32] designed a pre-anchoring strategy， immobilizing silver nanoparticles （AgNPs） onto cellulose nanocrystals （CNCs） to form CNC@Ag composites， and then integrating the CNC@Ag composite into a dual-network hydrogel （
Fig. 2c）. This strategy fundamentally physically restricts the migration of nanoparticles.
Moreover， a high aspect ratio of metal nanowires can form effective conductive pathways at lower concentrations， while minimizing adverse effects on the mechanical properties of the hydrogel
[33-35]. As illustrated in
Fig.2d， using
in-situ free-radical polymerization， Lv et al.
[36] fabricated a conductive nanocomposite hydrogel by incorporating silver nanowires （AgNWs） and carboxylated multi-walled carbon nanotubes （c-MWCNTs） into a chitosan （CS） and polyacrylamide （PAM） network. AgNWs serve as the primary conductive network， while c-MWCNTs substantially improve mechanical properties （e.g.， ultra-high extensibility of 16000%） through reinforcing interfacial hydrogen bonding. This combination thus enables synergistic optimization of electrical and mechanical performance.
Although metallic nanomaterials can provide hydrogels with high electrical conductivity， their inherent rigidity may lead to stress concentration within the hydrogel matrix， potentially compromising the uniformity of the resulting mechanical and electrical properties. Therefore， liquid metals （LMs）， with unique room-temperature fluidity and deformability， emerge as one of the ideal candidate materials for CHs
[37-39].
As shown in
Fig. 2e， Chen et al.
[40] developed a dual-network composite hydrogel by incorporating LM and nickel nanowires （NiNWs）. The LM fills the gaps between the NiNWs， creating highly efficient conductive pathways that significantly enhance the overall electrical conductivity of the hydrogel. Zhong et al.
[41] developed a composite hydrogel with an electrical conductivity of 1800 S/cm and a tensile strain of 1400%， which was achieved by a strategy of physically crosslinking a matrix of gallium-based LM nanodroplets with conductive micro-fillers. Moreover， a self-sintered structure with LM and polyvinyl alcohol （PVA） hydrogels was formed through gravity-driven phase separation， thereby enhancing the mechanical strength and electrical conductivity of the composite hydrogel
[42]. FMSs based on LM composite hydrogels demonstrate excellent stretchability and signal stability while achieving high conductivity， opening new avenues for wearable sensing and human-machine interaction.
2.1.2 Carbon nanomaterial-based CHs
Carbon nanomaterials， such as carbon nanotubes （CNTs）， graphene， carbon nanofibers （CNFs）， and carbon black （CB）， are ideal fillers for constructing high-performance CHs due to their outstanding intrinsic electrical and mechanical properties
[20,43-44]. Incorporating carbon nanomaterials into polymer matrices enables the construction of three-dimensional CH networks with large specific surface areas and abundant functional groups. This not only provides efficient pathways for electron transport but also enhances the mechanical properties of the composite materials.
While a high loading of conductive components increases the mechanical stiffness and brittleness of hydrogels， an insufficient concentration often leads to poor dispersion and inefficient network formation， thereby limiting the overall conductivity
[45-46]. By physically guiding carbon materials to form spatially ordered and highly efficient conductive pathways within the hydrogel matrix， excellent conductivity is achieved even at low filler content. For example， as illustrated in
Fig.3a， Huang et al.
[47] incorporated high-aspect-ratio CNTs into a PVA hydrogel and aligned them along the stretching direction via cyclic stretching， thereby constructing an anisotropic conductive network. This strategy cleverly exploits the reorganization behavior of nanomaterials， substantially improving conductivity along the tensile direction （
x‑axis） and reducing impedance by 17.4%. The material also retains excellent mechanical stability over 20000 stretching cycles， achieving reliable electrical and mechanical performance.
In addition， further optimization of the carbon materials can enhance their ability to form stable conductive networks. For example， Park et al.
[48] achieved
in-situ formation of a reduced graphene oxide （rGO） network with a 3D channel structure by thermally annealing agarose hydrogels containing small amounts of GO. The formation of interconnected internal channels allows the material to achieve high conductivity and tissue-like elasticity at a very low filler loading.
Furthermore， the introduction of hydrophilic compounds into carbon-based CHs can effectively address the aggregation of inherently hydrophobic carbon materials in aqueous media. This approach facilitates the formation of a hydrogel network with a uniform and stable conductive system
[49-50]. As shown in
Fig.3b， Huang et al.
[51] created a stable and uniform hydration environment by precisely controlling the growth of polymer crystalline domains within cross-linked PVA hydrogels. This micro-structured hydrogel matrix effectively promotes the uniform dispersion of CNTs， enabling the fabrication of composites that exhibit high extensibility （>139%）， low modulus， and high uniformity. However， achieving uniform dispersion of carbon-based nanomaterials within the system still remains challenging， significantly limiting their potential applications in CHFMSs.
2.1.3 MXene-based CHs
MXene represents a class of 2D layered materials
[52-53]. Owing to the distinctive layered nanostructure， high electrical conductivity， large specific surface area， and tunable surface chemical properties， MXene shows promising potential for application in flexible mechanical sensing
[54-55]. For example， as shown in
Fig.4， Lu et al.
[56] rapidly synthesized an LS‑MXene/PAA hydrogel by co‑assembling sodium lignosulfonate （LS） as a stabilizer with MXene and initiating polymerization using the moisture‑retaining effect of glycerol. LS improved the dispersion of MXene and formed a protective layer on the surface of MXene. This layer， in synergy with the PAA network， endowed the hydrogel with high conductivity （0.23 S/m）， a broad sensing range （10%~700%）， and anti‑freezing capability.
However， MXene tends to aggregate during hydrogel formation because of the abundance of polar groups on the surfaces. Through controlled chemical pretreatment， the surface chemistry and nanostructure of MXene can be pre‑regulated， endowing it with enhanced intrinsic dispersibility and oxidation resistance. For instance， Zhang et al.
[57] employed the oxidized treatment to MXene in an alkaline environment to customize the nanostructure. This moderate oxidation modifies the surface chemistry of the nanosheets， significantly improving the dispersibility of MXene in PAM. The PAM/oxidized‑MXene composite exhibits comprehensive enhancements in conductivity， transparency， sensitivity， and mechanical strength.
Although promising， the aggregation and oxidation tendency of MXene nanosheets in water environments severely restrict their applications
[55]. Therefore， future work needs to focus on the simultaneous optimization of both performance and long-term stability in MXene-based hydrogel sensors.
2.2 Ionic-based CHs
Ionic CHs combine the elasticity of hydrogels with the conductivity of ions， presenting excellent mechanical and electrical properties， and are widely used in wearable electronic devices
[58-60]. The 3D hydrophilic polymer network of ion-conducting hydrogels usually contains large numbers of free ions， which can be obtained by dissolving ionic salts （metallic salts （e.g.， NaCl or KCl， etc.）， acids （e.g.， HCl or H
2SO
4） and ionic liquids （ILs）
[61-64]） in the hydrogels.
Owing to the strong ionization property and low cost， metallic salts and acids are fundamental choices for constructing highly conductive hydrogels. As shown in
Fig. 5a， Wang et al.
[65] achieved efficient ion transport in a cellulose-bentonite network by introducing LiCl. The coordination interactions between Li
+ and the cellulose-bentonite network enhanced the mechanical properties， resulting in a hydrogel with both high strength （0.76 MPa） and high ionic conductivity （8.99 S/m）. Similarly， organic acids， such as phytic acid （PA）， not only dissociate to provide high H
+ concentrations， but also act as physical crosslinkers due to the multifunctional structures， thereby improving conductivity （4.23 S/m） while significantly enhancing adhesion （87.74 kPa）
[66]. Thus， inorganic or organic ionic conductors primarily improve hydrogel conductivity and mechanical properties synergistically through ion-polymer interactions.
In addition to metallic salts and acids， ionic liquids （ILs） are also one of the promising ionic conductors due to their high ionic conductivity and excellent stability. Beyond serving as free ion donors， ILs also actively participate as functional building blocks in hydrogel network formation. As illustrated in
Fig. 5b， Ji et al.
[67] synthesized an ionic CH by copolymerizing a functionalized IL （a pyridazolium ammonium salt with a ureido backbone） with sulfobetaine methacrylate （SBMA） and acrylamide （AM）. In this system， ILs not only enhance the conductivity of hydrogel but also stabilize the polymer network via dynamic bonds （e.g.， hydrogen bonds）， thereby promoting structural integrity and functionality. However， since excess ions may cause salt precipitation and leakage， the selection of ionic conductor type and concentration is critical for balancing conductivity， stability， and biocompatibility in ionic CHs.
2.3 Conductive polymer-based CHs
Conductive polymers， such as polyaniline （PANi）， polypyrrole （PPy）， and PEDOT：PSS， constitute a class of macromolecular chain materials formed by C atom skeletons and extended π-conjugated electron systems
[68-69]. This unique architecture allows them to retain the favourable processability and mechanical flexibility of traditional polymers， while simultaneously exhibiting electrical conductivity akin to metallic or inorganic semiconductor materials
[70]. However， the application of conductive polymers in hydrogels is often limited by problems such as aggregation tendency， swelling， and insufficient efficiency of the conductive network. To address the issue， as illustrated in
Fig.6a， Sun et al.
[71] introduced rigid PPy into a polyacrylamide/alginate （PAM‑ALG） matrix to construct a PAM‑ALG‑PPy composite hydrogel. The multiple non‑covalent bonds formed between the PPy chains and the gel network not only effectively suppress hydrogel swelling， but also synergistically enhance the mechanical strength （1.63 MPa） and electrical conductivity （2.16 S/m） of composite hydrogel. To meet the demand of rapid electrical signal transmission， Li et al.
[72] designed a PEDOT：PSS hydrogel structure called hierarchical inhomogeneous network （HIN）， which composed of hydrated PSS networks formed by the entanglement of PSS polymer chains and a PEDOT：PSS polycrystalline phase with interlaced conductive networks， as shown in
Fig. 6b. The HIN serves as an internal electronic highway of material， breaking the traditional understanding that ions dominate the charge transport mechanism within CHs.
Furthermore， to balance the trade-offs among the mechanical， electronic， and self-healing properties， Su et al.
[73] designed a supramolecular double-network （DN） CHs by pre-infiltrating PANI into the hydrophobic association poly（acrylic acid） （HAPAA） hydrogel matrix. The interconnected PANi network endows the PANi hydrogel with high conductivity and excellent sensory properties. However， the hydrophobicity and easy aggregation of PANi often lead to a decline in the mechanical and electrochemical properties of hydrogels. To solve the issue， Zhao et al.
[74] prepared a homogeneous PANi CH by introducing a bifunctional glycyrrhizic acid （GL）. This is because GL functions both as a biocompatible matrix and as a dopant for aniline monomers， promoting the in‑situ polymerization of well‑dispersed PANi.
Despite the favourable biocompatibility and tunability of conductive polymers， the inherent tendency towards aggregation and poor ability to form permeable networks remain practical challenges constraining the enhancement of conductive hydrogel performance. These issues require an urgent solution through subsequent research.
2.4 Hybrid CHs
To overcome the limitations of single conductive fillers， researchers have combined different types of conductive fillers to construct composite hydrogel systems. Within this framework， synergistic effects between distinct components enable complementary performance characteristics， thereby significantly enhancing electronic or ionic conductivity and endowing the material with additional functional properties. This strategy not only improves the structural stability of CH-based FMSs but also further optimizes their sensing capabilities.
For example， combining conductive nanoparticles， poly（ionic liquid）， and polymers into a composite material ensures the flexibility of the hydrogel， while also imparting electrical conductivity and temperature resistance. Combining freeze-thawing and ionizing radiation methods， a poly（ionic-liquid）/MXene/poly（vinyl-alcohol） （PIL/MXene/PVA）-based DN ionic CH was prepared by Zhao and collaborators
[75]， as shown in
Fig. 7. Physical cross-linking network of the PVA was achieved through freeze-thawing cycles， followed by ionizing radiation to form a chemically cross-linked PIL-PVA polymer network. PIL enhanced the temperature tolerance of PVA hydrogels while imparting excellent conductivity. Furthermore， owing to the abundant surface hydrophilic groups （e.g.， —F， —OH， and
$\stackrel{\mathrm{ }\mathrm{ }\mathrm{ }\mathrm{ }}{＝}$O）， MXene， as a physical crosslinking agent， formed noncovalent interactions with the PIL-PVA polymer network. This strategy further strengthens the mechanical properties of the composite hydrogel.
In view of the limited conductivity of CHs， Wang et al.
[76] developed a carbon composite hydrogel with a highly conductive network， which was prepared by encapsulating a liquid metal （LM）-based conductive hydrogel formed by
in-situ free radical polymerization of cellulose fiber-derived carbon aerogel and PAA. A synergistic combination of LM （eutectic gallium-indium， EGaIn） and the cellulose fiber-derived carbon aerogel provided the conductivity for the carbon composite hydrogel. In addition， Ga
3+ from EGaIn induced reversible ionic crosslinking in PAA， thereby conferring self-healing ability to the hydrogel. Meanwhile， the ductile PAA network and the abundant hydrophilic groups in the conductive hydrogel enable the carbon composite hydrogel to show good stretchability and adhesion. This multi-component design thus enhances not only the electrical but also the mechanical performance of the hydrogel. Furthermore， the prepared strain sensor based on the carbon composite hydrogel exhibited high sensitivity， ultra-low detection limit and good durability.
2.5 Analysis of different types of CHs
A comprehensive comparison of the four categories of CHs is summarized in
Table 1. Each strategy offers a distinct pathway to conductivity， defined by inherent performance trade-offs that define the application. Nanocomposite-based CHs utilize nano-conductive fillers， such as metals， carbon nanomaterials， and MXenes， to achieve the highest conductivities and significant mechanical strength
[77-78]. However， the performance of CHs is determined by the intrinsic properties of the specific filler. For example， metal nanomaterials （e.g.， AgNWs， AuNPs） provide exceptional conductivity but are prone to oxidation and aggregation. In contrast， carbon nanomaterials （e.g.， CNTs， graphene） offer superior long-term stability， tunable electrical properties， and mechanical reinforcement， though the hydrophobic nature necessitates complex dispersion processes. MXenes present a different profile， combining high conductivity with inherent hydrophilicity for easier processing， yet they may suffer from rapid oxidative degradation in ambient conditions. Consequently， selecting an appropriate filler requires a direct compromise between peak conductivity， long-term stability， and processability.
Ionic CHs utilize mobile ions for charge transport， resulting in materials with inherent biocompatibility， transparency， and facile synthesis
[59-60]. However， poor mechanical strength， susceptibility to dehydration， and unstable conductivity resulting from ion diffusion or leakage restrict the wider application. Consequently， ionic CHs are less suitable for applications that require mechanical durability or stable operation over extended periods.
Conductive polymer-based CHs integrate charge transport directly into the polymer matrix via conjugated systems （e.g.， PEDOT：PSS， PANi）. This approach bypasses filler dispersion issues and yields good electrical conductivity with inherent flexibility
[79-80]. However， the environmental sensitivity of the conjugated backbone often leads to performance decay under operational stresses such as humidity or cyclic loading. Hybrid CHs represent an integrative approach designed to overcome the above limitations by combining multiple conductive components. This strategy aims for synergistic performance， often achieving enhanced functionality （e.g.， self-healing， extreme toughness） that exceeds the sum of individual parts
[81-82]. But the development of hybrid hydrogels may face challenges posed by more complex manufacturing processes and material compatibility.
3 Classification and performance of CHFMSs
3.1 Classification of CHFMSs
CHFMSs are flexible electronic devices capable of converting external mechanical stimuli into electrical signal outputs such as vibration， impact， tension， compression， and torque
[83-84]. The operating principles of CHFMSs rely on external forces inducing structural deformation within the sensor， thereby altering the electrical properties of the sensing layer. Based on the type of mechanical signal measured， these sensors are primarily categorized into pressure sensors and strain sensors. Furthermore， depending on the signal conversion mechanism， they may be classified as piezoelectric， triboelectric， resistive， or capacitive types
[85-87].
Resistive mechanical sensors use CHs as the sensing element. Under external mechanical stimuli， the internal conductive network of the hydrogel undergoes changes， resulting in measurable and systematic variations in resistance （
Fig. 8a）. Zhang et al.
[88] developed an entirely physically crosslinked DN hydrogel based on electrostatic interactions and hydrogen bonding between polymer chains. The hydrogel consists of a chitosan （CS） network and a poly（sulfobetaine-
co-acrylic acid）/chitosan-citrate （P（SBMA-
co-AAc）） copolymer network. In this system， citrate ions serve as charge carriers， migrating within the polymer matrix to enable conductivity. When the hydrogel is stretched， some of the original conductive pathways are discontinuous or broken， leading to an increase in resistance and thereby enabling strain sensing.
In addition to the fundamental operating mechanisms described above， the electromechanical response of hydrogel-based resistive sensors can be more rigorously interpreted through percolation theory and tunneling-based charge-transport models
[89-90]， which together govern conductivity in heterogeneous hydrogel networks. Many nanocomposite hydrogels described in Section 2 form conductive pathways only after surpassing a critical percolation threshold， as demonstrated by systems incorporating high-aspect-ratio fillers， such as gold nanorods， CNTs， MXene sheets， or Ag nanowires， which establish interconnected conductive clusters within the hydrogel matrix
[31,33-35,47,56]. Above this threshold， the conductive domains form a macroscopically continuous network， and mechanical deformation perturbs the connectivity of this fragile network by altering particle contact， orientation， or spacing， resulting in significant and often nonlinear resistance variations. When the network lies close to the percolation threshold， its sparse connectivity makes it highly sensitive to structural perturbations， thereby enhancing the gauge factor but reducing linearity. Conversely， hydrogels far above the percolation threshold， such as those containing dense MXene or CNT networks， possess redundant pathways that mitigate abrupt resistance changes and thus yield more linear responses
[47,56]. In hydrogels where conductive fillers remain separated by nanoscale polymer layers， electron transport is further governed by quantum tunneling， whose resistance depends exponentially on the interparticle distance； such tunneling-dominated microstructures are common in soft matrices where metallic nanoparticles， carbon nanomaterials， or hybrid fillers do not form direct physical contact
[30,45,51]. As a result， even subtle deformation can significantly enlarge or reduce nanoscale interparticle gaps， producing large resistance changes and accounting for the exceptionally high gauge factors observed in several nanocomposite hydrogel systems. This theoretical framework， therefore， provides the mechanistic bridge connecting materials design and electromechanical performance in CHFMSs. To complement the fundamental transduction mechanisms introduced above， the electrical responses of CHFMSs can be further rationalized through established theoretical models that quantitatively describe the relationship between mechanical deformation and electrical output.
For resistive sensors， the electromechanical behavior is governed by deformation-induced changes in the conductive pathways within the hydrogel network. The resistance variation can be expressed as
R=ρL/A， where changes in length （
L） and cross-sectional area （
A） under strain contribute to overall resistance
[91]. However， the dominant contribution originates from microstructural rearrangements of the conductive fillers， which can be described by percolation theory and tunneling conduction models. Near the percolation threshold （
pc）， conductivity （
σ） follows
σ∝（p-pc）t， and deformation modifies the filler volume fraction
p， altering cluster connectivity
[90]. In networks containing nanoscale gaps， electron transport follows the Simmons tunneling model
[92-93]，
Rt=R0eβd， where
d is the interparticle distance. These models explain the high sensitivity and nonlinear behavior commonly observed in nanocomposite-based resistive hydrogels.
Capacitive mechanical sensors utilize CHs as the dielectric layer or electrodes， as shown in
Fig.8b. The operation of capacitive FMSs relies on changes in the electrode spacing or contact area under force， which in turn induces a capacitance variation. For instance， Fan et al.
[94] developed an iontronic sensor by sandwiching an alginate/PAM/FeCl
3/LiBr hydrogel， which was used as a dielectric layer containing a high concentration of free ions， between two copper electrodes. Upon the application of pressure， the contact area at the electrode-hydrogel interface rapidly increases， thereby enhancing the electrical double-layer （EDL） capacitance and effectively transducing the pressure into a measurable capacitive signal.
For capacitive sensors， the capacitance output follows the classical parallel-plate capacitor relationship
[95]：
C=εrε0d/A， where
εr is the relative permittivity，
A the effective contact area， and
d the electrode separation. Mechanical loading alters
A or
d， leading to capacitance variation. In iontronic hydrogels， an EDL is formed at the hydrogel-electrode interface， significantly amplifying capacitance. The behavior of EDL capacitive sensors is described by the Gouy-Chapman-Stern model
[96]， where the effective capacitance is
CEDL-1=
CHelmholtz-1+
Cdiffuse-1. This framework explains the ultrahigh sensitivity achieved by ion-rich hydrogels in contact-area-based capacitive sensors.
The piezoelectric effect refers to the generation of a potential in piezoelectric materials under mechanical stress. Upon application of an external force， the relative displacement of positive and negative charges within the material induces polarization， leading to the formation of polarized charges on the materials surface and voltage. Once the force is removed， the material recovers its original state and the charges dissipate （
Fig.8c）. Hydrogels containing piezoelectric ceramics or polar polymers can function as sensing elements in piezoelectric hydrogel-based FMSs. For example， Wang et al.
[97] developed a P（AM-
co-AN） piezoelectric hydrogel by incorporating polyacrylonitrile （PAN） into an AM copolymer. The PAN not only improves the mechanical strength of the hydrogel but also imparts piezoelectricity. Under pressure， the dipoles in P（AM-
co-AN） hydrogel undergo aligned polarization， resulting in charge accumulation and separation that generates voltage signals. In the absence of external force， the dipoles oscillate randomly， maintaining a state of spontaneous polarization equilibrium. This mechanism enables effective conversion of mechanical stimuli into electrical signals.
For piezoelectric sensors， the charge generation under mechanical stress is captured by the direct piezoelectric effect
[98]：
D = emiS + εmnE， where
D is the electric displacement，
emi is the piezoelectric stress coefficient， and
εmn is the piezoelectric coefficient.
S and
E refer to the applied strain and the electric field， respectively. Hydrogels containing piezoelectric polymers （e.g.， PAN-based systems） produce polarization upon deformation， generating voltage signals that scale proportionally with the applied dynamic strain.
Triboelectric sensors operate based on the effects of contact electrification and electrostatic coupling， which convert mechanical energy into electrical signals without an external power source. The sensors offer high signal output but may suffer from unstable performance. There are four specific working modes， such as vertical contact-separation mode， single electrode mode， lateral sliding mode， and independent mode， with the vertical contact-separation mode being the most prevalent. A typical vertical contact-separation triboelectric pressure sensor consists of two friction layers that generate charges and two electrodes that transfer electrons （
Fig. 8d）. CHs are usually used as friction layers or electrodes. For example， Tao et al.
[99] used a PAM/calcium alginate （CA） DN hydrogel with microcone structure as an electrode and electrification layer of a triboelectric device to constitute a self-powered pressure sensing module. When an external force is applied， the upper polyimide （PI） film and the hydrogel form a contact pair. Due to their difference in electron affinity， the PI acquires electrons and becomes negatively charged， while the hydrogel loses electrons and becomes positively charged. This charge transfer generates a voltage output， enabling precise pressure sensing.
For triboelectric sensors， the contact and separation of two materials with different electron affinities generate induced charges， which in turn induce a potential difference. The open-circuit voltage can be approximated by
VOC =σx/ε0， where σ is the tribo-charge density and
x the separation distance
[100-101]. The short-circuit current is given by
ISC=
dQ/
dt， where
Q is the induced charge
[100-101]. These relations explain the strong dynamic sensitivity and self-powered nature of triboelectric hydrogel sensors.
Owing to their different working principles， resistive， capacitive， triboelectric， and piezoelectric FMSs exhibit distinct characteristics
[85,102-106]， which are clearly outlined in
Table 2.
Resistive and capacitive mechanical sensors offer the advantages of simple operating mechanisms， device structures， and signal acquisition modes， while being capable of responding to both static and dynamic mechanical signals
[87]. Piezoelectric and triboelectric mechanical sensors feature high-speed response and self-powering functionality， but exhibit limited responsiveness to static loads and are susceptible to environmental interference
[106]. Therefore， the mechanism choice of the sensor should be guided by the specific demands of the application.
3.2 Multimodal sensing based on CHFMSs
Due to the structural tunability， high electrical sensitivity， broad environmental adaptability， and excellent mechanical properties， flexible multimodal sensors based on CHs have garnered significant attention
[107-108]. By integrating multiple sensing mechanisms， the multimodal sensors can respond to diverse external stimuli， including physical parameters （e.g.， tensile force， pressure， temperature） and chemical biomarkers （e.g.， pH， glucose， lactate）， which enables multidimensional force monitoring and physical/chemical signal cross-domain detection， as shown in
Fig. 9. For example， Mi et al.
[109] developed robust CNC-LiCl CHs through a one-pot crosslinking method， which exhibits multisensory responsiveness to mechanical and thermal stimuli. By integrating resistive， triboelectric and capacitive mechanisms， the resulting sensor based on the CNC-LiCl CHs achieved non-contact proximity sensing and judgment in curling referee systems. In this system， temperature variation was correlated with resistance， the landing impact of stones was reflected in the triboelectric output， and proximity events were indicated by capacitance signals. Furthermore， Wang et al.
[110] constructed a flexible multimodal sensing patch consisting of a bimodal hydrogel layer for pressure-temperature sensing and a nanofiber-based non-contact detection layer. Pressure and temperature detection were achieved through the capacitive and resistive effects of the hydrogel sensing layer， respectively. Meanwhile， the top nanofiber-based triboelectric layer enables non-contact sensing.
However， signal crosstalk remains a critical challenge that limits the detection accuracy of multimodal sensors. To improve signal independence， current research mainly adopts two strategies： physical isolation of sensing units to mitigate crosstalk， and the use of machine learning algorithms or customized circuits to extract target parameters from hybrid signals
[111-112]. Overall， CHs show great potential in multimodal mechanical sensing. Future advances will rely on the deep integration of novel material architectures and intelligent decoupling techniques， which enable high-fidelity and synchronized perception of multiple parameters in complex environments.
3.3 Performance of CHFMSs
Recent advancements in synthesis methods and materials have yielded a range of CHs possessing unique advantages such as excellent stretchability， stable conductivity， self-healing， anti-freezing， and moisture retention properties
[88,113-120]. These multifunctional hydrogels are now widely investigated and utilized in domains like motion detection， medical diagnostics， electronic skin， and human-machine interactions， as shown in
Table 3. Stretchability is a key characteristic of CHFMSs， ensuring the functional stability of the hydrogel under mechanical deformations， such as bending， twisting， or stretching. Common strategies for enhancing the stretchability of hydrogels at present include constructing dual-network structures， introducing nanocomposites， and utilizing supramolecular interactions within the materials
[15,95]. For example， Wang et al.
[114] prepared PVA/PAA/Zr⁴
+ hydrogels via a one-step radical polymerization method， demonstrating excellent stretchability and high electrical conductivity. The abundant hydroxyl groups in the PVA chains form intramolecular and intermolecular hydrogen bonds between the PVA network and PAA， significantly enhancing mechanical properties and enabling a maximum stretchability of 2053%.
Furthermore， to enhance environmental adaptability， hydrogels often require multiple properties such as anti-freezing， self-adhesion， anti-swelling， and self-healing to maintain stable performance under extreme conditions. It should be noted that these performances are not simply superimposed but rather involve interdependence and multi-objective trade-offs. For instance， although the dense cross-linked structure adopted to achieve anti-swelling can stabilize the size， it will restrict the movement of polymer chains， thereby weakening elasticity and increasing the difficulty of self-healing. Consequently， to guarantee reliability in complex and dynamic operating conditions， the practical design of these sensors must balance the various potentially conflicting performance requirements.
3.4 Interfacial engineering for CHFMSs
The interface between hydrogels and external components， such as electrodes， conductive wires， substrates， and biological tissues， plays a decisive role in determining the stability， fidelity， and long-term reliability of CHFMSs
[116-117]. Although hydrogel compositions and sensing mechanisms are often emphasized， interfacial failure remains one of the most common and critical bottlenecks in practical applications. Weak adhesion， dehydration-induced shrinkage， mechanical mismatch， and unstable charge transfer at the interface can lead to increased contact impedance， signal drift， noise， and even complete device failure.
Hydrogel-electrode interfaces suffer from repeated strain-induced delamination， particularly when the hydrogel has a low modulus or high water content
[117-118]. Interfacial charge transfer is further hindered by microgaps or insufficient wetting. To address this， several strategies have been developed， including conductive adhesive interlayers （e.g.， polyacrylamide-PEDOT：PSS pastes）， metal-ligand coordination （e.g.， Zr
3+/polymer coordination for liquid metal composites）， surface roughening or microtexturing of electrodes to increase effective contact area， and the use of liquid-metal interconnects that maintain conformal contact under deformation
[121,124-126]. Liquid metal-based EGaIn networks， in particular， have demonstrated excellent deformability and interfacial compliance， providing highly stable contact resistance during large strains.
Hydrogel-skin interfaces present additional challenges due to the dynamic， curved， and moisture-rich nature of human skin
[127-128]. Interfacial engineering approaches inspired by biological adhesion， such as catechol chemistry， hydrogen-bonding-rich polymer networks， zwitterionic adhesive motifs， and topologically entangled interfacial networks， have significantly improved conformal contact and reduced motion artifacts
[123,129-131]. Microstructured adhesive surfaces， such as pillar arrays and suction-cup-like architectures， further enhance mechanical interlocking， enabling reliable sensing even under vigorous motion
[132]. Ionic bridging using divalent cations or polyelectrolyte complexes has also been employed to strengthen hydrogel-tissue interfaces without compromising biocompatibility
[128].
Encapsulation is another effective strategy to maintain interfacial stability by preventing dehydration and suppressing hydrogel contraction
[127,134]. Stretchable elastomer encapsulants （e.g.， Ecoflex， PDMS） or organohydrogel coatings enhance water retention and provide a secondary mechanical buffer layer that harmonizes modulus mismatch between the hydrogel and the external environment.
These interfacial engineering strategies are essential for translating high-performance hydrogel materials into fully functional and reliable sensing systems. As the field moves toward wearable， implantable， and long-term monitoring applications， the integration of robust interfacial design principles will remain a key determinant of device success.
4 Application of CHFMSs
In recent years， CHFMSs have attracted growing interest owing to their promising applications across multiple fields. As flexible electronic devices， CHFMSs can be attached to the skin for monitoring subtle physiological signals such as pulse waves and breathing patterns
[135-136]， as well as for detecting large-scale deformations caused by body movements， including joint flexion and motion pressure
[137-138]. Furthermore， the outstanding conductivity and stretchability of CHFMSs make them suitable for use in wearable human-machine interfaces and soft robotics
[139-140]. Monitoring different physiological or motion signals needs distinct requirements on sensors in terms of frequency， amplitude， and effective modulus
[15,103].
（1）Frequency response determines whether a sensor can track signal variations. Physiological signals， such as heart rate， blood pressure， and respiration， typically exhibit low-frequency ranges （0.1~10 Hz）. However， human motion signals， such as joint flexion and walking speed， generally require a frequency response of 1~100 Hz.
（2）A high signal-to-noise ratio （SNR） allows sensors to accurately extract weak signals amid environmental interference. Therefore， a stable and large signal amplification can be achieved from sensors with stable electrical properties and high sensitivity.
（3） Effective modulus governs the quality of mechanical coupling between the sensor and contact interface. A stiff sensor with a mismatched modulus may impede signal acquisition and cause discomfort. For example， to ensure conformal contact with the skin， a low modulus （~10 kPa） is generally required.
After summarizing the specific parameters of the sensor， the following sections will focus on the practical application of CHFMSs in various scenarios.
4.1 Healthcare monitoring
The frequency， rhythm， and intensity of the pulse provide critical indicators for evaluating cardiac function. Continuous monitoring of these parameters allows for early detection of abnormalities， such as tachycardia and bradycardia， which is of considerable significance for cardiovascular disease prevention and health management. Conventional techniques of pulse monitoring， however， largely depend on manual measurements performed by healthcare professionals， and their accuracy can be compromised by variations in operator skill and environmental conditions， making long-term， continuous signal acquisition difficult to achieve. Recent progress in flexible electronics has led to the development of electronic skin devices based on conductive hydrogel sensors， which offer a promising alternative by enabling stable and real-time tracking of dynamic pulse waveforms.
For instance， Yan et al.
[141] developed a resistive-type sensor based on silk fibroin hydrogels （SFHs） for monitoring pulse waveforms. The SFH was fabricated from a silk fibroin solution derived from Bombyx mori silkworm cocoons， which contains α-helices， β-sheets， and random coils. These molecular structures form chain networks with PAM through multiple hydrogen bonds in the presence of choline chloride （ChCl）. The resulting hydrogel exhibited high sensitivity （GF：5.2397）， a frequency of 0.25~5 Hz， and adhesive strength to skin （30 kPa）， which attributed that fulfill the requirements for wearable continuous monitoring sensors. In practical validation， similar pulse waveform patterns with only variations in signal intensity were detected by placing the SFH resistive sensor at different locations on the wrist （
Fig.10a）. This consistency confirms the utility of the SFH sensor for reliable pulse detection.
Moreover， the precise monitoring of respiratory function is equally vital for health assessment and early warning of disease. Pressure sensors fabricated by CHs can be affixed near the nasal cavity or on the thoracic and abdominal regions. By detecting changes in airflow or surface body movements， they enable real-time， non-intrusive monitoring of breathing patterns， thereby providing crucial information for the early diagnosis of respiratory-related diseases.
Liu et al.
[142] have developed a high-performance multimodal hydrogel sensor based on a dual-crosslinked cellulose hydrogel with a sandwich structure， as illustrated in
Fig.10b. By introducing a tannic acid-glycerol-NaCl system into the chemical network， an additional physically crosslinked network is formed， resulting in a dual-crosslinking architecture. The hydrogel demonstrates remarkable mechanical properties and extreme temperature tolerance， owing to the synergistic effect between chemical crosslinking and multiple hydrogen bonding. Capitalizing on the dual sensitivity to mechanical stimuli， the sensor can be attached to the chest and abdomen for real-time monitoring of respiratory activity.
4.2 Human motion monitoring
Monitoring human movement is crucial for sports training， rehabilitation of persons with disabilities， and personalized medicine. Recently， various electronic devices for health monitoring based on CHFMSs have emerged
[143-144]. The application scenarios of CHFMSs range from macro-level joint movements to micro-level physiological signals， imposing differentiated performance demands on the sensor.
For monitoring large-scale and periodic joint movements （e.g.， elbow and knee bending）， sensors require high elasticity， strong interfacial adhesion， and reliable cyclic stability to maintain signal integrity under substantial deformation. Zeng et al.
[145] developed a PEDOT：PSS-based nanocomposite hydrogel with a tensile strain of ~450%， tissue adhesion strength of ~0.5 N/cm²， and self-healing efficiency of ~95%. The PEDOT：PSS-based hydrogel provides the necessary elasticity and adhesion， enabling stable and repeatable tracking of joint bending angles.
For the subtle movements， such as swallowing， vocalization， and micro-expressions， signals are weak and susceptible to interference. Therefore， sensors must possess high sensitivity， rapid response， and excellent SNR. Simultaneously， the devices must be exceptionally flexible to prevent signal suppression. As shown in
Fig. 11， Mohamadnia et al.
[146] dissolved a mixture of acrylic acid and glycidyl methacrylate-modified gelatin in a deep eutectic solvent （DES） composed of choline chloride and allyldimethylammonium chloride， thereby fabricating a DES‑based strain sensor with high elasticity and strong adhesion. In practical wearable tests on volunteers， the sensor not only reliably captured signals from major joints such as fingers， wrists， elbows， and knees （
Fig.11a and b） but also effectively detected subtle physiological activities， including swallowing and articulated speech with varying syllables （
Fig.11c and d）， demonstrating robust and reproducible signal acquisition. This case indicates that constructing highly ion-conductive， highly elastic gel networks is an effective approach to meeting the demands of such high-precision biological signal capture.
Furthermore， for high-level applications （e.g.， outdoor sports and rehabilitation training）， CHFMSs must achieve comprehensive breakthroughs in wide pressure/strain ranges， high sensitivity， long-term stability， and motion-pattern recognition capabilities. For example， Gou et al.
[147] integrated electrospun nanofibers with an ionogel to fabricate a pressure sensor that exhibited an ultra‑wide sensing range （~1000 kPa）， ultra‑high sensitivity （>10000 kPa
-1）， and stable performance （>5000 cycles）. Notably， by combining the sensor array with deep learning algorithms， different trick movements in skateboarding were successfully distinguished. Therefore， for sports monitoring applications， CHFMSs should maintain mechanical robustness under large deformations and optimize electrical sensitivity and SNR under micro‑strains， ultimately integrating sensing with intelligent algorithms to enable advanced perception in complex environments.
4.3 Human-machine interaction
Human-machine interaction （HMI） conveys human intent for machine control while simultaneously gathering real-time operational data to reveal the operational status of the machine. Gesture recognition and robotic remote control are both regarded as forms of human-machine interaction
[148-149].
Gestures serve as an effective medium of communication， particularly in scenarios requiring silent information transmission. For gesture recognition， flexible sensors detect minute deformations caused by human joint movements or muscular activity. Following signal processing， these changes are converted into electrical signals detectable by instruments， which can be interpreted as specific sign language meanings. Therefore， the signal discrimination， repeatability， and a large amplitude of CHFMSs are crucial to accurately distinguish between different gestures. For example， as shown in
Fig.12a， Shi et al.
[150] realized gesture recognition by attaching PAM/BA-Ag@PDA hydrogel sensors to the back surfaces of robotic fingers， thereby measuring the relative resistance changes in each finger over time. The PAM/BA-Ag@PDA hydrogel sensor demonstrated a GF of 1.86 within a strain range of 0%~200% and a fast response time of 138 ms. Crucially， it exhibited highly consistent and noise-free responses to different strains （10% to 40%）， enabling the precise recognition of human motion signals. This excellent signal consistency allows the sensor array output to be clearly translated into distinct gesture commands， satisfying the demands of high‑precision pattern recognition.
Remote control， as an important mode of human-machine interaction， relies on the continuous detection of sensors for human movements， such as finger bending， wrist rotation and fingertip pressure. Therefore， this mode requires that the sensor be capable of reproducing continuous human movements in real time and accurately converting motion signals into commands for remote devices. As shown in
Fig.12b， Zhuo et al.
[151] integrated conductive organic hydrogel sensors featured kirigami-inspired structures into a smart glove to enable remote control of a robotic arm for underwater grasping. This application requires high sensitivity to detect motions ranging from subtle flexion to gripping， along with ultra-low latency and high cyclic stability， to ensure real-time and precise operation. Through cooperative optimization of MXene nanosheets and kirigami structure， the sensor achieved continuous and reliable mapping of complex finger and wrist movements into control signals， thereby realizing fine remote manipulation of the robotic arm.
5 Challenges and prospects
Although CHFMSs are continually advancing， significant challenges persist. As illustrated in
Fig.13， these challenges primarily encompass material innovation， performance enhancement， and functional expansion.
Firstly， a conductive hydrogel system must be constructed with synergistically optimized electrical and mechanical properties. Since hydrogels are inherently non-conductive， exogenous conductive components are often incorporated to improve electrical conductivity. However， poor compatibility between conductive fillers and the hydrogel network often compromises mechanical performance. The effects of different conductive materials vary： nano-conductive fillers can significantly enhance conductivity， but their electrical behavior is limited by conduction mechanisms and often reduces tensile properties. Conductive polymers can improve both strength and conductivity， yet frequently reduce extensibility. Ionically， CHs offer high water retention and ionic conductivity but may leak liquid under large strains. Therefore， future research should prioritize developing novel conductive polymers and composite fillers that enhance electrical properties without compromising mechanical integrity.
Moreover， CHFMSs still face challenges in reliability and stability for practical applications. Their long-term performance in biological environments critically influences their practicality. These materials are prone to dehydration in air and freezing at low temperatures， resulting in deteriorated electrical and mechanical properties， which leads to sensor failure and reduced sensitivity. Incorporating high-boiling-point and low-freezing-point organic solvents （e.g.， glycerol or ethylene glycol） or establishing high-concentration salt systems can improve moisture retention and freeze resistance. However， such additives may introduce biocompatibility issues and disrupt polymerization， thereby impairing sensing performance. Thus， a careful balance among these properties is essential.
Finally， existing hydrogel-based mechanical sensors exhibit relatively limited functionality， highlighting the need for multimodal mechanical sensing systems. Piezoresistive and capacitive sensors are suitable for static pressure detection but generally exhibit limited sensitivity. Piezoelectric and triboelectric sensors perform well in dynamic signal detection but are unsuitable for static measurements. Although integrating multiple sensing mechanisms can provide complementary functions， achieving functional integration and intelligence remains challenging due to issues such as signal crosstalk and low transmission reliability. Improving sensor sensitivity and SNR requires advances in structural design and signal decoupling.
Although the CHFMSs are still in early stages with many critical challenges unresolved， the promising sensing properties and mechanical adaptability of CHFMSs are still expected to drive the widespread application in healthcare， human-machine interfaces， and other areas as artificial intelligence technologies advance.
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