A switchable dynamic-static tactile system for augmented haptic secret communication

Huiqi Zhao; Weiqi Qian; Chong Guo; Yaming Zhang; Jiabin Wang; Huiyu Dan; Yan Zhang; Chris R Bowen; Ya Yang

doi:10.1126/sciadv.adx6959

. 2025 Sep 12;11(37):eadx6959. doi: 10.1126/sciadv.adx6959

A switchable dynamic-static tactile system for augmented haptic secret communication

Huiqi Zhao ^1,^2,^†, Weiqi Qian ^1,^2,^†, Chong Guo ^1,², Yaming Zhang ³, Jiabin Wang ¹, Huiyu Dan ^1,², Yan Zhang ^1,^2,³, Chris R Bowen ⁴, Ya Yang ^1,^2,^*

PMCID: PMC12429048 PMID: 40938980

Abstract

Tactility bridges humans and the external world. Although human skin’s tactile receptors provide comprehensive perception, developing a biomimetic tactile system with both dynamic and static functions and rapid conversion remains a challenge. Here, we report a switchable dynamic-static tactile system. It features a rapid 1-millisecond transition between dynamic and static modes via light modulation, an all-in-one structure for simplicity and practicality, a remarkable balance of high sensitivity (198.45 per kilopascal) and wide pressure range (0.0137 to 207 kilopascals), a tunable sensitivity, and a sensing-feedback closed loop. The dynamic mode responds to vibrations, and the static mode responds to static pressure and superposition. In various real-world potential scenarios such as object detection and perception under vibration interference and human-computer interaction, it shows excellent performance. A closed-loop system with feedback and deep learning achieves user-encrypted Morse code haptic secret communication, paving the way for advancements in intelligence and virtual/augmented reality.

A switchable dynamic-static tactile system achieves augmented haptic secret communication.

INTRODUCTION

The tactile sense is a crucial medium for human interaction with the external world, having intricate perceptual mechanisms (1–7). Human reliance on the tactile sense enables a robust perception for the external environment, facilitated by various tactile receptors located under the skin (8, 9). These receptors consist of two primary types: fast-adapting (FA) mechanoreceptors and slow-adapting (SA) mechanoreceptors (10–15). FA receptors respond specifically to changes in stimuli, detecting dynamic touch such as vibration, while SA receptors continuously respond to stimuli, detecting static touch such as sustained pressure (12, 13, 16–18). These tactile receptors work in concert to provide humans with comprehensive and sensitive tactile perception. By emulating the functionalities of FA and SA receptors, tactile sensors have the potential to confer human-like tactile perception of both dynamic and static touch for robots and smart systems (19, 20).

Researchers have dedicated considerable efforts to developing tactile sensors to replicate the tactile perception capacities of human skin. However, the majority of current tactile sensors can only mimic either FA or SA receptors, thus achieving solely dynamic or static tactile perception (21–29). These single-mode tactile sensors fall notably short of replicating humans’ intricate tactile perception in terms of sensing performance. Among these, FA-like dynamic tactile sensors are typically based on piezoelectric (12, 30, 31) and triboelectric (32–35) effects, while SA-like static tactile sensors primarily rely on piezoresistive (26, 28, 36–38) and capacitive (30, 39, 40) effects. Although a few efforts have integrated dynamic and static tactile modes, they are typically based on the assembly of separate dynamic and static tactile modules (41–44). Such designs rely on distinct sensing units for different modes, leading to complex, large-scale systems with separate loops (16, 45–47). More critically, subregional sensing sacrifices full spatial resolution, failing to replicate the seamless integration of dynamic and static perception in human skin.

Previous tactile sensors scarcely compare to the comprehensive and sensitive tactile receptors of human skin. Consequently, it is urgent to develop advanced tactile sensors that integrate the functionalities of dynamic and static tactile modes within a single-structure single-loop device, permitting rapid switching between different tactile modes. Furthermore, the potential applications of tactile sensors are extensive, particularly when combined with intelligence technologies such as machine learning (16, 41, 42, 45), neural stimulation (10, 16), and human-computer interaction (11, 21, 43). Therefore, the realization of a combined dynamic-static tactile sensor with multimodal haptic feedback to form a complete haptic closed loop holds notable promise for the next generation of more realistic and comprehensive tactile systems.

In this work, we report a dynamic-static tactile system, an integrated platform comprising a switchable dynamic-static tactile sensor and a dynamic-static haptic feedback, unlike existing integrated dynamic-static tactile systems that typically rely on separate modules for different modes, resulting in complex, large-scale structures with separate loops and sacrificed full spatial resolution. Our switchable dynamic-static tactile sensor mimics FA and SA receptors of human skin, providing enhanced full-spatial tactile perception resolution within a single-structure single-loop device. Via light modulation of the dynamic-static tactile modes, rapid conversion between two modes within 1 ms is enabled. Besides, the sensor’s sensitivity is tunable through light modulation, currently achieving a high sensitivity of 198.45 kPa⁻¹, with potential for further improvement. Furthermore, the dynamic-static haptic feedback system with vibration and thermal modules is constructed, realizing augmented haptic secret communication based on Morse code.

RESULTS

Design of the dynamic-static tactile system

To replicate the characteristics of the FA and SA receptors in human skin, a dynamic-static tactile system is designed (Fig. 1A). This system incorporates a flexible bismuth ferrite (BiFeO₃, BFO)-based film to create a dynamic-static tactile sensor that features dynamic and static tactile modes, mimicking the behavior of FA and SA receptors. Notably, rapid conversion between the two tactile modes is achieved through light modulation within only 1 ms (Fig. 1B). In the dynamic tactile mode, the tactile sensor exhibits a piezoelectric effect, generating instantaneous spike signals produced upon the application or release of pressure, in response to dynamic touch or vibration. Conversely, in the static tactile mode, the tactile sensor demonstrates piezoresistive-like properties modulated by the laser light, generating a persistent signal that responds to the state of applied pressure, thus enabling it to respond to continuous touch or static pressure. Furthermore, the dynamic-static feedback system incorporates both dynamic and static haptic feedback, with a vibration motor and a heater providing vibration and thermal stimulation, respectively, to feedback dynamic and static tactile sensations.

In the realm of high-performance tactile sensors, achieving a balance between high sensitivity and a broad pressure range presents a formidable challenge. Our designed dynamic-static tactile sensor adequately considers this challenge, resulting in a remarkable sensitivity of 198.45 kPa⁻¹ and an extensive pressure range of 0.0137 to 207 kPa, exceeding the performance of most previous tactile sensors (Fig. 1C). The majority of currently reported tactile sensors are capable of either dynamic (21, 30, 33, 41, 48) or static (26, 28, 37, 38, 49–51) tactile sensing. While a few reports have demonstrated the ability to sense both dynamic and static tactile stimuli (16, 18, 42), our dynamic-static tactile sensor emerges as a pioneering achievement in terms of sensitivity tunability and rapid mode-switching capability. Notably, our sensor features a single-structure and single-loop design, setting it apart from other tactile sensors currently in existence. Additional detailed statistics regarding these tactile sensors in terms of sensing performance, sensing mechanism, and intelligent applications are listed in table S1.

The switchable dynamic-static tactile sensor consists primarily of a flexible BFO-based tactile film with a hierarchical structure (fig. S1, A and B, and note S4). The film is positioned on a customized platform to construct the switchable dynamic-static tactile sensor (Fig. 2A). In this setup, a press lever is connected to the end of a tensile tester to apply varying levels of pressure and velocity, while the tensile tester operates in tandem with the dynamometer to deliver programmable force and motion. A 405-nm laser light is used to modulate the conversion between dynamic and static tactile modes. For evaluation of dynamic-static tactile sensing performance, the press lever and the laser light are aligned within the same sensing unit; note that the laser light is only used in standardized tests, with the practical wearable system adopting the integrated light-emitting diode (LED).

Fig. 2. — (A) Illustration of performance test platform for the switchable dynamic-static tactile sensor. The sensor contains a flexible BFO-based film with a hierarchical structure. (B) Force curve of the dynamic and static force stimuli. (C to E) Performance of the sensor in the dynamic mode. Current signals (C), pressure sensitivity (D), and characteristics (E) of the sensor. (F to H) Performance of the sensor in the static mode. Current signals (F), pressure sensitivity (G), and characteristics (H) of the sensor. (I) Demonstrations of the switchable dynamic-static tactile sensor: (i) object detection under regular vibration interference, (ii) object detection under irregular vibration interference, and (iii) changing force detection under irregular vibration interference. Scale bar, 1 cm. (J) Signal-to-noise ratio enhancement of the sensor in the various demonstrated scenarios via the conversion between dynamic and static modes.

Performance of the switchable dynamic-static tactile sensor

According to the action time of external force stimuli, human tactile sensation can be categorized into dynamic touch (rapid stimulation) and static touch (continuous stimulation). The customized platform emulates both dynamic and static force stimuli (Fig. 2B and note S5), with the aim of comprehensively exploring the performances and differences between the dynamic and static modes for the switchable dynamic-static tactile sensor. When the laser light is deactivated, the sensor operates in the dynamic mode, whereas when the laser light is activated, the sensor switches to the static mode.

In the dynamic mode, dynamic signals are produced when the sensor is subjected to applied rapid and continuous forces (Fig. 2C and fig. S2A), where instantaneous spike signals occur only at the moment of force applied or released. Both the short-circuit current (I) and the open-circuit voltage (V) signals exhibit a dependence on pressure, with greater force leading to larger electrical signals (fig. S3, A and B). The minimum detection pressure can reach an extent of 0.6 kPa (fig. S3D). The sensor’s sensitivity (note S6), obtained from the slope of the linear fit, varies with pressure (Fig. 2D). Below 50 kPa, the normalized current (ΔI/I₀) rises sharply and the sensitivity is 2.07 kPa⁻¹. Above 50 kPa, the slope slows and the current saturates gradually with a sensitivity of 0.3 kPa⁻¹. In the dynamic mode, the sensor’s pressure sensing ranges 0.6 to 207 kPa (fig. S3). Notably, the sensor in the dynamic mode has unique sensing characteristics, being able to detect the speed of applied force (Fig. 2E). At the same applied forces of 0.1 N, the dynamic signals rise with the speed of force application, showing a linear relationship. In contrast, the sensor in the static mode can only detect force magnitude and not the speed of force movement (fig. S5A), showing no difference in signals at different speeds.

Upon activation of the 405-nm laser light, the switchable dynamic-static tactile sensor experiences a mode conversion from the dynamic mode to the static mode. In the static mode, the BFO film exhibits a piezoresistive-like behavior, which produces a sustained output signal linearly correlated with applied pressure. Real static signals present a prominent photoelectric signal platform (fig. S2, B and C), which consequently hampers the comparison of force responses. To tackle this issue, the signal variation quantities (ΔI and ΔV) are used for analyzing the signals in the static mode of the sensor.

Static signals are generated under the same rapid and continuous forces respectively (Fig. 2F and fig. S2C). The sensor in the static mode produces signals that are highly consistent with the force-time curve (Fig. 2B), indicating that the sensor in the static mode can sense both rapid and continuous forces. Notably, when subjected to a continuous force, a continuous static signal is presented (Fig. 2F), which is distinctly different from the instantaneous spike signal in the dynamic mode (Fig. 2C). Similarly, the static sensitivity exhibits a dependence on pressure (Fig. 2G and fig. S4). Below 85 kPa, the normalized current increases notably, with a sensitivity of 198.45 kPa⁻¹. Above 85 kPa, the normalized current rises slowly, and the sensitivity is 48.14 kPa⁻¹. The normalized minimum detection pressure reaches 0.3 kPa (fig. S4, E and F). However, this value could potentially be more precise, yet it is limited by the minimum range of the dynamometer. A series of ultralight objects are used to further investigate the minimum detection limit of the sensor in the static mode (fig. S6, A to C). Consequently, the sensor’s pressure sensing range in the static mode is 0.0137 to 152 kPa. Notably, compared with the dynamic mode, the sensor in the static mode presents distinct sensing characteristics. It can sensitively detect the magnitude of force, real-time force variations, and the state of continuous force superposition (Fig. 2H and fig. S5, B and C). In addition, the sensor also shows the sensitive detection of actual object superposition (fig. S6, D and E). Evidently, the sensor in the static mode compensates for the deficiency of the dynamic mode, as the latter lacks the sensing capabilities of continuous force and force superposition.

In brief, the switchable dynamic-static tactile sensor features both dynamic and static modes with complementary capabilities. The dynamic mode detects spike signals in response to dynamic stimuli, whereas the static mode senses continuous signals under continuous pressure. The dynamic mode is applicable to larger pressures and has enhanced robustness, while the static mode is suitable for smaller pressures and offers higher precision. The combination of these two modes extends the sensing range from 0.0137 to 207 kPa, which is superior to human tactile perception (16, 36, 52), and the static sensitivity can reach up to 198.45 kPa⁻¹. The dynamic mode responds to force velocities, and the static mode is capable of sensing real-time force variations and superposition.

Demonstration of superiority in real-world potentials

On the basis of the remarkable advantages and characteristics within its fundamental sensing capabilities, the switchable dynamic-static tactile sensor exhibits unique superiority in real-world potentials. Here, three typical demonstrations are presented (Fig. 2I): (i) object detection under regular vibration interference, (ii) object detection under irregular vibration interference, and (iii) changing force detection under irregular vibration interference. These further emphasize the superiority of the switchable dynamic-static tactile sensor for various real-world potential applications.

The first demonstration is object detection under regular vibration interference (movie S1). Initially, the sensor in dynamic mode remains stable while regular vibration causes background noise. When a small screw with an irregular surface is placed, a spike signal appears; a reversed spike signal occurs when it is removed. When the screw is on the sensor, only background noise is detected. When the light turns on and the sensor switches to static mode, the photoelectric signal platform is prominent and the background noise is negligible. The processes of placing, keeping, and removing the screw correspond to the sensor’s signals. In this case, an object applies continuous force to the sensor. The sensor can eliminate the influence of regular vibration-induced background noise to detect the force. In game controller scenarios with regular vibrations, the sensor can detect the force and measure grip force changes precisely, enabling more accurate input recognition and better gaming experiences by distinguishing player-intended grip adjustments from controller-generated vibrations.

The second demonstration centers on object detection under irregular vibration interference (movie S2). Hand-shaking is used to simulate these vibrations, thereby generating complex background noise. The same small screw is placed onto the sensor. In the dynamic mode, in contrast to the regular vibration case, the spike signals during the placement and removal of the screw are intertwined with variable noise. In the static mode, the sensor’s noise-filtering ability plays a crucial role. The photoelectric signal platform accentuates the relevant signals. Despite the challenges presented by the irregular vibration interference, the sensor can detect the object’s movement and stationary states. In this case, the sensor has the ability to eliminate the influence of irregular vibration interference for force detection. This is of great significance in scenarios such as industrial environments, where machinery vibrations are often irregular and accurate object detection is essential for quality control and process monitoring purposes.

The third demonstration focuses on changing force detection under irregular vibration interference (movie S3). The same hand-shaking generates complex interference, while a finger touch induces a changing force. In the dynamic mode, the sensor records a series of complex signals incorporating the effects of both the changing force and the irregular vibrations. Upon switching to the static mode, the sensor is capable of differentiating between the two. The static mode not only eliminates the chaotic background noise from the irregular vibrations but also precisely captures the details of the changing force. The sensor can accurately track force variations over time, thereby providing valuable information regarding the dynamic nature of the applied force. This has notable implications in medical rehabilitation. In patients with movement disorders like Parkinson’s disease, the sensor can monitor and analyze muscle force changes during rehabilitation, even with involuntary tremors causing vibrations. This enables health care professionals to better evaluate treatment progress and adjust rehabilitation programs accordingly.

To summarize, the switchable dynamic-static tactile sensor’s functionality under both regular and irregular vibration interferences confers substantial advantages in real-world applications. In particular, the conversion between dynamic and static modes of the sensor leads to two orders of magnitude enhancement of the signal-to-noise ratios (SNRs) within the three demonstrated scenarios (Fig. 2J). These advantages have the potential to improve user experience in entertainment devices such as game controllers, facilitate the sensor’s noise-filtering capacities in industrial settings, and provide support for patients with movement disorders like Parkinson’s disease. Through the accurate detection of forces and the perception of changing forces during various types of vibration, the sensor bridges the gap between human-object interaction and the challenges presented by external disturbances, thereby opening up possibilities for the improvement of human-machine interfaces and assistive technologies.

Modulation of the dynamic-static tactile modes

Light illumination induces a transition between dynamic and static tactile modes modulated by light intensity. Under the same pressure, varying light intensities (0 to 2179 mW cm⁻²) result in tactile signals with distinct amplitudes and features (Fig. 3A and fig. S7). Specifically, in the dynamic mode (light off or intensity ≤ 20.9 mW cm⁻²), force generates transient spike signals; in the static mode (light on with intensity ≥ 100 mW cm⁻²), persistent signals arise in response to persistent force. This clear threshold separation between 20.9 mW cm⁻² (dynamic) and 100 mW cm⁻² (static) ensures no overlap between dynamic and static signals.

Light-induced photoelectric signal platform increases with rising light intensities (Fig. 3B, fig. S10, and note S8). In addition, light enables tunability of tactile sensitivity, with higher light intensities further enhancing sensitivity. Notably, as light intensity increases, the tactile signal gradually transitions from dynamic to static mode, and this mode switching depends on light intensity rather than the specificity of the light source, enabling compatibility with LEDs for practical wearable applications. This conversion between the two modes is interesting and needs further analysis (note S9).

The current-voltage relation (I-V) curves of the sensor gradually change from a typical Schottky contact to an ohmic-like contact as the light intensity increases (Fig. 3C and note S9). Besides, a metal-semiconductor (MS) junction is used as a theoretical model to simulate the effect of light intensity on outputs (Fig. 3D). When subjected to low light intensity, photogenerated carriers enhance current outputs at fixed voltages. As light intensity increases further, negative current outputs emerge at zero voltage, indicating a reverse potential difference. The results show that the Schottky barrier decreases as light intensity increases, which further evidences the conversion from Schottky to ohmic-like contact.

In addition, the potential distribution and carrier concentration of BFO at different light intensities are studied under a vertical pressure of 0.25 N (Fig. 3E, fig. S12, and note S9). In the absence of light, as a P-type semiconductor, BFO has a low carrier concentration of 2.4 × 10¹⁴ cm⁻³ and a hole mobility of 796.68 cm² V⁻¹ S⁻¹ (53–55). The piezoelectric charge generates a potential difference of about 12.9 mV. In weak light, the carrier concentration rises to 2.4 × 10¹⁵ cm⁻³ and the potential difference reduces to 9.3 mV due to the partial shielding of photogenerated carriers on the piezoelectric charge. Under strong light, the carrier concentration further increases (1.2 × 10¹⁶ cm⁻³) and the piezoelectric charge is mostly shielded. Here, photogenerated carriers dominate the potential distribution; thus, in strong light, the sensor shows tactile sensing characteristics similar to piezoresistive pressure sensors.

Accordingly, working mechanisms in different states are suggested from the energy-band perspective (Fig. 3F and note S10). In the dynamic mode, BFO energy band bends at the two interfaces (56–58). Force redistributes charges at the interfaces due to the piezoelectric effect, generating dynamic tactile signals. The light illumination converts the sensor to the static mode. Light illumination creates electron-hole pairs to generate photoelectric output. The concentration of photogenerated carriers at the interfaces rises notably, acting as a shielding agent for piezoelectric charges, making the sensor piezoresistive-like. Force changes the internal resistance to generate static tactile signals.

Wearable dynamic-static tactile system for secret communication

A wearable dynamic-static tactile system for augmented haptic secret communication consists of a transmit end and a receive end (Fig. 4A). The transmit end incorporates a wearable sending band equipped with a compact acrylic-frame controller (28 mm by 32 mm by 20 mm, 14 g, Fig. 4B). This controller balances mechanical stability for precise force control and ergonomic wearability during user interaction, with its acrylic frame ergonomically shaped to fit the palm and facilitate natural finger placement for dynamic/static force inputs. Integration with a flexible Ecoflex sending band enhances comfort, conforming to the arm while isolating the rigid core from body motion artifacts.

The dynamic-static tactile film and an LED in a double-layer acrylic frame form the controller (Fig. 4B and fig. S13, A to C). Here, an upper press lever exerts force, supported by four springs, while the lower LED irradiates the film. The LED activates static mode, with ~180 mW cm⁻² at the BFO-based tactile film surface. This intensity exceeds the static threshold for reliable switching, yet staying within safe limits (IEC 60825-1) without protective gear. A sensing circuit board is integrated in the sending band; the wearable receiving band includes a motor, a heater, and a feedback circuit board (fig. S13, D and E, and note S11).

Operationally, the manually held controller senses transmission-designated information, which the sending band wirelessly transfers to the receiving band. Inspired by human tactile receptors’ functional dichotomy, our dual-actuator system emulates FA and SA roles: The motor mimics FA’s rapid response to transient stimuli (Morse “∙”), while the heater mirrors SA’s sustained response to static pressure (“−”). Rooted in biomimetic logic, it aligns with Morse standards (ITU-R M.1677–1), where “−” lasts three times “∙.” The heater’s inherent slow response (fig. S15) fits this, avoiding decoding ambiguity.

This biohybrid design ensures perceptual congruence, minimizing cognitive load during code interpretation. Functionally, the system’s two modes directly map to these actuators (Fig. 4A): mode 1 (dynamic): a rapid force on the controller triggers the transmit end to send a dynamic signal; the receive end responds with vibration-based feedback for “∙”; mode 2 (static): light irradiation combined with continuous force prompts the transmit end to emit a static signal, eliciting thermal-based feedback for “−” at the receive end.

Two volunteers, serving as sender and receiver, respectively, achieve augmented haptic secret communication via the wearable dynamic-static tactile system. The transfer of three distinct types of information is demonstrated, with the whole process being synchronously recorded (Fig. 4C and movies S4 to S6). In the initial scene, three random letters, Q, H, and U, are successfully transmitted and decoded. In the second scene, the distress signal “SOS” is successfully dispatched and decoded. In the final scene, a complete sentence carrying key location information is successfully sent and decoded.

In addition, user-encrypted augmented haptic secret communication is achieved through the application of multimodal deep learning algorithms. The multimodal deep learning algorithm is deployed to classify and identify the tactile information of six users. Each user procures both dynamic and static tactile information as datasets and inputs them into the algorithmic architecture, with a quota of 200 samples per user (Fig. 4D and figs. S16 and S17). The architecture of the multimodal deep learning algorithm encompasses two convolutional neural networks that are responsible for extracting feature information from the dynamic and static tactile signals, respectively. In addition, it includes a multilayer perception composed of fully connected neural networks, which undertakes the tasks of fusing the extracted information and performing the final classification.

Conventionally, 80% of the data are randomly chosen as training data, while the remaining 20% serve as testing data. During the training process, samples are drawn from the dataset and the network is updated in each training step. Over a sequence of 200 training steps, the training and testing accuracy escalates and the loss diminishes and attains a stable state (Fig. 4E). The confusion matrix reveals a final accuracy of 96.67% for the multimodal recognition of six users (Fig. 4G). Multimodal algorithms integrally combine the user’s dynamic and static tactile signal characteristics in a complementary manner, surpassing unimodal algorithms that solely rely on either dynamic or static tactile signal traits in terms of classification and recognition capabilities (Fig. 4F and fig. S18).

The multimodal deep learning results of the six users are input into the wearable dynamic-static tactile system, endowing the system with the ability to realize user-encrypted secret communication (movie S7). Six users, acting as senders, transmit orientation information to the receiver: front (F), back (B), left (L), right (R), up (U), and down (D). However, only one of them is the authentic user, conveying the correct orientation information. The other users dispatch false orientation information, thereby interfering with the delivery of accurate information. When identified as the true user, the green light on the receive band flashes; conversely, when recognized as a false user, the red light flashes (Fig. 4H). The receiver is enabled to access the authenticity of the user and the reliability of the information. Subsequently, the correct orientation information can be obtained. In the demonstration, red light does not flash only when the message from user 4 is received. The receiver records Morse code via haptic perception and decodes it to acquire the correct orientation information: R. Therefore, multimodal deep learning algorithms further actualize user-encrypted augmented haptic secret communication.

DISCUSSION

We have developed a switchable dynamic-static tactile system that integrates biomimetic sensing modalities with light-enabled mode-switching capabilities. This system offers five key advancements, establishing a technological paradigm: seamless dynamic-static integration in a single-structure, single-loop device with 1-ms light-modulated switching; all-in-one architecture simplifying configuration; high-performance metrics combining ultrahigh sensitivity (198.45 kPa⁻¹) and broad pressure range (0.0137–207 kPa); light-tunable sensitivity for adaptive sensing requirements; and closed-loop integration with vibration/thermal feedback for augmented haptic communication.

Unlike conventional tactile sensors limited to single modes, our system enables comprehensive tactile perception akin to human skin’s FA and SA receptors. Existing hybrid systems require separate sensing modules with complex architectures, compromising spatial resolution and response speed (16, 41–44). In contrast, our light-modulated single-film solution eliminates mechanical complexity while achieving two orders of magnitude SNR improvement in vibration-prone environments. This dual-mode synergy enables noise-immune operation where conventional single-mode sensors fail.

The dual-actuator system (motor/heater) mirrors the sensor’s dynamic/static logic, assigning transient “∙” to vibration (FA-like) and sustained “−” to thermal feedback (SA-like). This biomimetic design ensures intuitive Morse code interpretation, reducing cognitive load. It enhances decoding reliability by distinguishing transient/sustained signals, addressing single-actuator ambiguity in complex scenarios.

The system addresses human-machine interface limitations: For dynamic authentication systems relying on transient signals, our static mode provides continuous biometric verification through force superposition detection. Unlike fingerprint authentication vulnerable to replication, our closed-loop architecture combines dynamic haptic input with user-specific neural network recognition (96.67% accuracy, six users), establishing dual-layer security through physiological signals and behavioral patterns. This multimodal approach surpasses unimodal authentication in security applications where spoofing resistance is paramount.

Practical demonstrations validate transformative potential across domains. In industrial settings, the system suppresses vibration interference during precision tasks, as evidenced by effective background noise elimination in object detection scenarios. For medical rehabilitation, it achieves high sensitivity to micropressure variations while maintaining functionality under pathological tremors, as demonstrated in Parkinsonian patient simulations. Morse code communication achieves 100% decoding accuracy through synchronized vibration-thermal feedback, demonstrating viability for covert haptic messaging in augmented reality scenarios.

The acrylic frame provides structural stability for the tactile film, press lever, and springs, while integration with a flexible Ecoflex band addresses wearability. This hybrid design, which combines rigidity for precise sensing and compliance for comfort, not only demonstrates the coexistence of mechanical stability and ergonomics but also indicates the system’s potential for practical applications. However, when comparing our system with traditional tactile units like fingerprint buttons, it is evident that form factor and functional complexity present a trade-off. Traditional units offer compactness but lack the adaptive, bimodal sensing, and closed-loop feedback essential for nuanced communication, such as Morse code encoding, and robust operation in noisy environments. Our system deliberately prioritizes functional complexity to build a versatile platform for advanced haptic applications, including robotic perception and covert communication.

To further enhance performance and practicality, future work will focus on miniaturization via flexible electronics and integrated photonics. This approach will help balance size and performance while maintaining core functionalities. For instance, substituting lasers with compact LEDs in practical implementations improves scalability, and future efforts will concentrate on embedding micro-LEDs into the BFO substrate. In addition, the current transparent acrylic frame (used for visualization of mode switching) will be replaced with opaque materials to confine light within the device, eliminating potential light leakage and enhancing user safety in wearable scenarios.

In addition to miniaturization, future iterations may incorporate advanced materials (e.g., Joule-heating graphene films) to reduce heater-related thermal latency while maintaining vibration immunity. This improvement will further enhance the system’s response capabilities and expand its application scope.

Furthermore, optimizing the light beam’s size and shape can revolutionize the system’s sensing capabilities. By engineering the light beam, we can achieve spatial control of sensing modes in BFO-based devices. A focused beam, for example, could create localized piezoresistive-like regions in pixelated tactile arrays while preserving the piezoelectric response in adjacent areas. This strategy would enable multiplexed functionality, notably enhancing the system’s adaptability in various scenarios.

However, practical deployment faces challenges posed by environmental factors. Temperature variations can affect BFO’s piezoelectric response, necessitating thermal stabilization of the optical source. Humidity, on the other hand, can degrade light transmission and cause surface corrosion, although we have mitigated these effects through controlled experimental conditions and protective coatings. To overcome these challenges, future research will focus on developing temperature-compensated algorithms and moisture-robust thin-film architectures. These measures will enhance the system’s field reliability and ensure its stable operation in different environments.

Moreover, ambient light interference and mechanical fatigue under prolonged cycling require attention. Addressing these will improve resilience, facilitating adoption in intelligent robotics, neuroprosthetics, and immersive interfaces. This will not only expand the system’s application potential but also contribute to the development of these emerging fields.

MATERIALS AND METHODS

Preparation of the dynamic-static tactile film

A smooth and clean mica substrate was stripped and then placed on a spin coater. The lanthanum nickel oxide (LaNiO₃, LNO) precursor solution was spin coated on the mica substrate at 2000 rpm for 30 s and then sequentially annealed on a hot plate at 180°C (evaporation of the solvent), 400°C (preliminary crystallization), and 700°C (complete crystallization) for 3 min. The above steps were repeated six times to obtain LNO film. Then, the BFO precursor solution was spin coated on the LNO film at 4000 rpm for 20 s and then sequentially annealed on a hot plate at 200°C (solvent evaporation), 400°C (preliminary crystallization), and 550°C (complete crystallization) for 3 min. The above steps were repeated five times to obtain the BFO/LNO film. Subsequently, a 3 × 3 array of indium tin oxide (ITO) electrodes was deposited on the BFO/LNO film using magnetron sputtering equipment (sputtering time, 20 min; sputtering power, 150 W) and a designed mask plate, with individual sensing units of size 2 by 2 mm². As a result, the ITO (166 nm)/BFO (122 nm)/LNO (307 nm) thin film was obtained as the dynamic-static tactile film.

Construction of the customized platform for performance evaluation

The customized platform was assembled with a combination of several key components, namely, a tensile tester (IMADA, MX2-500N), a force gauge (IMADA, ZTA-5N), a self-fabricated press lever (a cylinder having a cross-sectional diameter of 2 mm and a height of 10 mm), a 405-nm laser light source (MLL-III-405), and the dynamic-static tactile film. The dynamic-static tactile film was securely fastened onto a support frame, with the electrode array side oriented toward the light source. Notably, the sensing unit of the film was suspended in a position above the light source. The mica substrate side of the film was positioned to face the press lever, thereby being configured to endure the applied force. The support frame was designed to be horizontally movable, enabling the precise alignment of both the laser light and the press lever to the center of the sensing unit under examination. The light source was positioned at a distance of 1 cm from the dynamic-static tactile film. The experiments were conducted on a Zolix optical platform, providing a rigid foundation to align the 405-nm laser perpendicular to the BFO film via mechanical fixtures (Zolix BP-12 base plate). Lateral positioning used a Zolix DSM50S-6590L manual stage, optimized by maximizing the photoelectric baseline signal to center the laser spot. In addition, films composed of the same material were attached to the surface of the mica substrate and the surface of the press lever, with the intention of mitigating the influence of triboelectric signals. The tensile tester was capable of generating programmable movements, while the force gauge was responsible for controlling the applied force. The pairing of the tensile tester and the force gauge enabled the manipulation of the movement speed of the press lever, as well as the magnitude and duration of the applied force. Consequently, this customized platform was proficient in providing diverse tactile states, such as rapid force (corresponding to the dynamic mode) and continuous force (associated with the static mode), which were essential for comprehensively assessing the dynamic-static tactile sensing characteristics of the dynamic-static tactile film.

Fabrication of the wearable dynamic-static tactile sensor

An LED-based device further enhanced integration and reduced power requirements. An LED was used to supply a 405-nm light source within the wearable apparatus. The press lever, which was equipped with a flat plate on its upper surface, the dynamic-static tactile film, and the LED were incorporated into a self-fabricated miniature acrylic frame. The lower compartment of the frame was designated for housing the LED and furnishing support to the dynamic-static tactile film, with the LED positioned ~2 mm below the tactile film and secured via a miniature bracket to ensure stable light alignment, with the sensing unit in an overhanging configuration and the electrode array side directed toward the LED. Four springs were integrated into the upper portion of the frame to uphold the press lever and safeguard the dynamic-static tactile film from excessive pressure loads. They were rigidly anchored on both top and bottom within the acrylic frame, restricting motion to pure longitudinal displacement (vertical axis) to eliminate lateral sliding or bending. Eventually, the wearable dynamic-static tactile sensor was achieved. Through pressing the flat plate situated atop the press lever, the user is enabled to apply a suitable force onto the dynamic-static tactile sensor.

Assembly of the wearable dynamic-static tactile system

For the transmit end, the wearable dynamic-static tactile sensor served as a controller and was interconnected to a sending band. For the receive end, the receiving band integrated a motor and a heater. The motor provided vibration stimulation upon receiving dynamic signals, and the heater supplied a thermal stimulation of around 45°C when static signals were received. For both the transmit and receive ends, a key step was the band encapsulation. A 1:1 ratio of Ecoflex elastomer parts A and B was added to a beaker and thoroughly mixed to eliminate air bubbles. The forming molds, which were designed based on the shape and size of the respective control circuit boards and fabricated using an acrylic plate and a laser cutter, were then filled with the well-mixed Ecoflex. The filled molds were cured in an oven at 80°C for 2 hours, and after cooling and demolding, the sending band for the transmit end and the receiving band for the receive end were obtained.

Acknowledgments

Funding: Y.Y. acknowledges the Beijing Natural Science Foundation (grant no. JQ21007), the National Natural Science Foundation of China (grant no. 52072041), and the University of Chinese Academy of Sciences (grant no. Y8540XX2D2).

Author contributions: Conceptualization: Y.Y. Methodology: H.Z., W.Q., and Yaming Zhang. Software: H.Z., W.Q., and Yan Zhang. Validation: H.Z., W.Q., C.G., and Yaming Zhang. Formal analysis: H.Z., W.Q., C.G., Yaming Zhang, J.W., and H.D. Investigation: H.Z., W.Q., C.G., Yaming Zhang, J.W., and H.D. Resources: Y.Y. Data curation: H.Z., W.Q., and Y.Y. Writing—original draft: H.Z., W.Q., Yaming Zhang, and Y.Y. Writing—review and editing: H.Z., W.Q., C.G., Yaming Zhang, J.W., H.D., Yan Zhang, C.R.B., and Y.Y. Visualization: H.Z., W.Q., Yaming Zhang, Yan Zhang, and Y.Y. Supervision: Y.Y. Project administration: Y.Y. Funding acquisition: Y.Y.

Competing interests: The authors declare that they have no competing interests.

Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials.

Supplementary Materials

The PDF file includes:

Notes S1 to S12

Figs. S1 to S18

Table S1

Legends for movies S1 to S7

sciadv.adx6959_sm.pdf^{(12MB, pdf)}

Other Supplementary Material for this manuscript includes the following:

Movies S1 to S7

sciadv.adx6959_movies_s1_to_s7.zip^{(58.8MB, zip)}

REFERENCES AND NOTES

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Notes S1 to S12

Figs. S1 to S18

Table S1

Legends for movies S1 to S7

sciadv.adx6959_sm.pdf^{(12MB, pdf)}

Movies S1 to S7

sciadv.adx6959_movies_s1_to_s7.zip^{(58.8MB, zip)}

[R1] 1.Yu X., Xie Z., Yu Y., Lee J., Vazquez-Guardado A., Luan H., Ruban J., Ning X., Akhtar A., Li D., Ji B., Liu Y., Sun R., Cao J., Huo Q., Zhong Y., Lee C., Kim S. Y., Gutruf P., Zhang C., Xue Y., Guo Q., Chempakasseril A., Tian P., Lu W., Jeong J., Yu Y. J., Cornman J., Tan C. S., Kim B. H., Lee K. H., Feng X., Huang Y., Rogers J. A., Skin-integrated wireless haptic interfaces for virtual and augmented reality. Nature 575, 473–479 (2019). [DOI] [PubMed] [Google Scholar]

[R2] 2.You I., Mackanic D. G., Matsuhisa N., Kang J., Kwon J., Beker L., Mun J., Suh W., Kim T. Y., Tok J. B. H., Bao Z., Jeong U., Artificial multimodal receptors based on ion relaxation dynamics. Science 370, 961–965 (2020). [DOI] [PubMed] [Google Scholar]

[R3] 3.Lee S., Franklin S., Hassani F. A., Yokota T., Nayeem M. O. G., Wang Y., Leib R., Cheng G., Franklin D. W., Someya T., Nanomesh pressure sensor for monitoring finger manipulation without sensory interference. Science 370, 966–970 (2020). [DOI] [PubMed] [Google Scholar]

[R4] 4.Tee B. C. K., Chortos A., Berndt A., Nguyen A. K., Tom A., McGuire A., Lin Z. C., Tien K., Bae W. G., Wang H., Mei P., Chou H. H., Cui B., Deisseroth K., Ng T. N., Bao Z., A skin-inspired organic digital mechanoreceptor. Science 350, 313–316 (2015). [DOI] [PubMed] [Google Scholar]

[R5] 5.Jung Y. H., Yoo J. Y., Vázquez-Guardado A., Kim J. H., Kim J. T., Luan H., Park M., Lim J., Shin H. S., Su C. J., Schloen R., Trueb J., Avila R., Chang J. K., Yang D. S., Park Y., Ryu H., Yoon H. J., Lee G., Jeong H., Kim J. U., Akhtar A., Cornman J., Kim T., Huang Y., Rogers J. A., A wireless haptic interface for programmable patterns of touch across large areas of the skin. Nat. Electron. 5, 374–385 (2022). [Google Scholar]

[R6] 6.Lee W. W., Tan Y. J., Yao H., Li S., See H. H., Hon M., Ng K. A., Xiong B., Ho J. S., Tee B. C. K., A neuro-inspired artificial peripheral nervous system for scalable electronic skins. Sci. Robot. 4, eaax2198 (2019). [DOI] [PubMed] [Google Scholar]

[R7] 7.Yao K., Zhou J., Huang Q., Wu M., Yiu C. K., Li J., Huang X., Li D., Su J., Hou S., Liu Y., Huang Y., Tian Z., Li J., Li H., Shi R., Zhang B., Zhu J., Hung Wong T., Jia H., Gao Z., Gao Y., Zhou Y., Park W., Song E., Han M., Zhang H., Yu J., Wang L., Li W. J., Yu X., Encoding of tactile information in hand via skin-integrated wireless haptic interface. Nat. Mach. Intell. 4, 893–903 (2022). [Google Scholar]

[R8] 8.Fleck J. J., Zook Z. A., Clark J. P., Preston D. J., Lipomi D. J., Pacchierotti C., O’Malley M. K., Wearable multi-sensory haptic devices. Nat. Rev. Bioeng. 3, 288–302 (2025). [Google Scholar]

[R9] 9.Bag A., Ghosh G., Sultan M. J., Chouhdry H. H., Hong S. J., Trung T. Q., Kang G.-Y., Lee N.-E., Bio-inspired sensory receptors for artificial-intelligence perception. Adv. Mater. 37, e2403150 (2024). [DOI] [PubMed] [Google Scholar]

[R10] 10.Wang W., Jiang Y., Zhong D., Zhang Z., Choudhury S., Lai J. C., Gong H., Niu S., Yan X., Zheng Y., Shih C. C., Ning R., Lin Q., Li D., Kim Y. H., Kim J., Wang Y. X., Zhao C., Xu C., Ji X., Nishio Y., Lyu H., Tok J. B. H., Bao Z., Neuromorphic sensorimotor loop embodied by monolithically integrated, low-voltage, soft e-skin. Science 380, 735–742 (2023). [DOI] [PubMed] [Google Scholar]

[R11] 11.Qiu Y., Sun S., Wang X., Shi K., Wang Z., Ma X., Zhang W., Bao G., Tian Y., Zhang Z., Ding H., Chai H., Liu A., Wu H., Nondestructive identification of softness via bioinspired multisensory electronic skins integrated on a robotic hand. NPJ Flex. Electron. 6, 45 (2022). [Google Scholar]

[R12] 12.Park J., Kim M., Lee Y., Lee H. S., Ko H., Fingertip skin-inspired microstructured ferroelectric skins discriminate static/dynamic pressure and temperature stimuli. Sci. Adv. 1, e1500661 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R13] 13.Delmas P., Hao J., Rodat-Despoix L., Molecular mechanisms of mechanotransduction in mammalian sensory neurons. Nat. Rev. Neurosci. 12, 139–153 (2011). [DOI] [PubMed] [Google Scholar]

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PERMALINK

A switchable dynamic-static tactile system for augmented haptic secret communication

Huiqi Zhao

Weiqi Qian

Chong Guo

Yaming Zhang

Jiabin Wang

Huiyu Dan

Yan Zhang

Chris R Bowen

Ya Yang

Roles

Abstract

INTRODUCTION

RESULTS

Design of the dynamic-static tactile system

Fig. 1. Concept of the dynamic-static tactile system.

Fig. 2. Performance and potentials of the switchable dynamic-static tactile sensor.

Performance of the switchable dynamic-static tactile sensor

Demonstration of superiority in real-world potentials

Modulation of the dynamic-static tactile modes

Fig. 3. Conversion of the dynamic-static tactile modes and the corresponding mechanism.

Wearable dynamic-static tactile system for secret communication

Fig. 4. Wearable dynamic-static tactile system for augmented haptic secret communication.

DISCUSSION

MATERIALS AND METHODS

Preparation of the dynamic-static tactile film

Construction of the customized platform for performance evaluation

Fabrication of the wearable dynamic-static tactile sensor

Assembly of the wearable dynamic-static tactile system

Acknowledgments

Supplementary Materials

The PDF file includes:

Other Supplementary Material for this manuscript includes the following:

REFERENCES AND NOTES

Associated Data

Supplementary Materials

ACTIONS

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