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Extremely durable electrical impedance tomography–based soft and ultrathin wearable e-skin for three-dimensional tactile interfaces
Extremely durable EIT-based wearable tactile interface
https://orcid.org/0000-0002-7425-2871
Kim Kyubeen Conceptualization Formal analysis Investigation Methodology Project administration Validation Visualization Writing - original draft 1 †
https://orcid.org/0009-0002-5215-3642
Hong Jung-Hoon Conceptualization Data curation Formal analysis Investigation Methodology Project administration Software Validation Visualization Writing - original draft Writing - review & editing 1 †
https://orcid.org/0009-0001-3832-8103
Bae Kyubin Conceptualization Investigation Methodology Resources Supervision Validation Visualization Writing - review & editing 2 †
https://orcid.org/0000-0002-8520-8999
Lee Kyounghun Formal analysis Methodology Resources Software Validation 3
https://orcid.org/0009-0003-9998-9882
Lee Doohyun J. Investigation Resources Writing - original draft Writing - review & editing 1
https://orcid.org/0000-0002-4593-4793
Park Junsu Software Validation 4
https://orcid.org/0000-0002-9741-5098
Zhang Haozhe Data curation Formal analysis Investigation Methodology Resources Software Validation Writing - original draft Writing - review & editing 5
https://orcid.org/0000-0003-0298-3250
Sang Mingyu Formal analysis Methodology Resources Validation Visualization Writing - review & editing 1
https://orcid.org/0009-0007-2158-047X
Ju Jeong Eun Formal analysis Methodology Resources 1
https://orcid.org/0000-0003-2578-5177
Cho Young Uk Conceptualization Methodology Visualization 6
https://orcid.org/0000-0002-6892-3069
Kang Kyowon Formal analysis Methodology Validation 1
https://orcid.org/0000-0001-9355-8270
Park Wonkeun Resources Writing - original draft Writing - review & editing 2
Jung Suah Investigation Validation 1
https://orcid.org/0000-0003-4705-9248
Lee Jung Woo Resources Visualization 7
https://orcid.org/0000-0002-2591-8737
Xu Baoxing Data curation Formal analysis Investigation Methodology Project administration Resources Software Validation Visualization Writing - original draft Writing - review & editing 5
https://orcid.org/0000-0003-4434-5871
Kim Jongbaeg Funding acquisition Project administration Supervision 2 *
https://orcid.org/0000-0002-2922-2702
Yu Ki Jun Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Project administration Resources Supervision Validation Visualization Writing - original draft Writing - review & editing 1 8 9 *
1 Functional Bio-integrated Electronics and Energy Management Lab, School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea.
2 School of Mechanical Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea.
3 Sciospec GmbH, Leipziger Str. 43b, Bennewitz 04828, Germany.
4 Robotics Lab, Woowa Brothers Corp., Seoul 05544, Republic of Korea.
5 Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA 22904, USA.
6 Center for Emergent Matter Science (CEMS), RIKEN, The Institute of Physical and Chemical Research, Saitama 351-0198, Japan.
7 Department of Biomedical & Robotics Engineering, Incheon National University, Yeonsu-gu, Incheon 22012, South Korea.
8 Department of Electrical and Electronic Engineering, YU-Korea Institute of Science and Technology (KIST) Institute, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea.
9 The Biotech Center, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea.
* Corresponding author. Email: kimjb@yonsei.ac.kr (J.K.); kijunyu@yonsei.ac.kr (K.J.Y.)
† These authors contributed equally to this work.

20 9 2024
20 9 2024
10 38 eadr109915 6 2024
15 8 2024
Copyright © 2024 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).
2024
The Authors
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

In the rapidly evolving field of human-machine interfaces (HMIs), high-resolution wearable electronic skin (e-skin) is essential for user interaction. However, traditional array-structured tactile interfaces require increased number of interconnects, while soft material–based computational methods have limited functionalities. Here, we introduce a thin and soft e-skin for tactile interfaces, offering high mapping capabilities through electrical impedance tomography (EIT). We employed an organic/inorganic hybrid structure with simple, cost-effective fabrication processes, ensuring flexibility and stability. The conductive and stretchable sensing domain includes a micropatterned multiwall carbon nanotube and elastomer composite. The skin-like tactile interface effectively detects pressure-induced conductivity changes, offering superior spatiotemporal resolution with fewer interconnects (pixel/interconnects >57). This EIT-based tactile interface discerns external pressures to a submillimeter degree and vertical deformations of a few hundred micrometers. It sustains stable functions under external damage or environmental changes, confirming its suitability for persistent wearable use. We demonstrate practical applications in real-time HMIs: handwriting recognition and drone control.

A soft, ultrathin e-skin with computational tactile mapping offers high-resolution pressure detection and exceptional durability.

http://dx.doi.org/10.13039/100004358 Samsung SRFC-IT1901-08 http://dx.doi.org/10.13039/501100014188 Ministry of Science and ICT, South Korea RS-2024-00353768 http://dx.doi.org/10.13039/501100014188 Ministry of Science and ICT, South Korea RS-2023-00222166 The Yonsei Fellowship
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pmcINTRODUCTION

Wearable electronic skin (e-skin) for tactile interfaces is essential in modern human-machine interface (HMI) applications, such as medical, e-skin, and robotics domains (1–3). These interfaces are engineered to mimic human touch by converting various physical stimuli into digital formats for real-time feedback, which enhances user immersion and control in diverse HMI applications (4, 5).

As HMI applications advance, achieving high spatiotemporal mapping quality and reliability has become crucial. To meet these demands, many tactile sensors are designed with a crossbar array structure, connecting each sensor cell with interconnect lines (6–9). However, interconnects in array structure introduce two critical drawbacks (Fig. 1A) (10, 11). First, a spatial resolution of mapping is closely tied to the number of interconnects, resulting in an increase of the number of interconnects to get high resolution. The increased number of interconnects leads to potential issues such as larger electrical noise, greater susceptibility to interferences among sensor cells, and the cross-talk effects (12, 13). Second, when considering tactile sensor applications, interconnects are inevitably exposed to external force and subsequent mechanical deformation, such as compression, bending, or even stretching. This physical deformation can lead to a low signal-to-noise ratio (SNR). Moreover, single interconnect breakage in an array architecture can lead to the failure of a row or column (14, 15).

Fig. 1. Overall schematic of electrical impedance tomography (EIT)–based soft e-skin for tactile mapping interface.

(A) Conceptual illustration of a crossbar array structure tactile sensor showing limited spatial resolution and interconnect break under mechanical deformation. (B) Illustration of conventional device form factor of an EIT-based tactile sensor. (C) Intrinsically stretchable, interconnect-free soft e-skin for high-resolution 3D tactile mapping via EIT. (D) Photograph of the soft tactile sensor bent over a rod. Scale bar, 5 mm. (E) Photograph of the device pressed by a swab, demonstrating the intrinsically stretchable property of the sensing area. Scale bar, 5 mm. (F) Flowchart schematic of tactile mapping and human-machine interface (HMI) applications, handwriting, and drone controller. The blue box shows the impedance change mechanism of the multiwall carbon nanotube (MWCNT)–embedded elastomer under applied pressure.

To address these inherent challenges, researchers have focused on developing tactile interfaces based on soft materials and computational methods (16–19). This approach leverages mechanical softness and a reduced number of interconnects while maintaining sufficient mapping capability, making it advantageous for wearable tactile mapping applications. However, these interfaces often lack functionalities such as multi-touch capabilities or suffer from bulkiness of external components, making seamless integration difficult.

Among various computational methods for indirect tactile measurements, electrical impedance tomography (EIT) stands out with advantages such as an interconnect-free sensing area, multipoint detection, and damage robustness (20–22). However, like other computational methods, accurate reconstruction in EIT requires stable electrical contact and homogeneity of the conductive sensing domain (23, 24). For these reasons, conventional EIT tactile mapping systems are often designed in a bulky form utilizing rigid electrodes to ensure these properties (Fig. 1B). Even when using liquid or soft conductive materials, the thickness or rigidity of the sensing system has limited the practical use of EIT as a seamless e-skin. To overcome these limitations, it is essential to develop skin-like sensing interface that can achieve stable image reconstruction while possessing mechanical softness.

Here, we report a soft and ultrathin e-skin for a tactile interface that addressed the intrinsic constraints of the conventional array structured tactile sensors and EIT-based tactile devices (Fig. 1C). EIT-based 50-μm-thick ultrathin e-skin was fabricated based on photolithography, enabling simple and scalable fabrication for organic/inorganic hybrid structures [including polydimethylsiloxane (PDMS), Ecoflex 0030, multiwall carbon nanotube (MWCNT), metal (Cu), and SiO2]. By using micropatterning of MWCNTs compatible with the soft substrate, we ensured the stability and homogeneity of the mapping interface, thereby confirming wearable mapping capability with high spatiotemporal resolution through EIT measurement. This soft tactile interface has four distinct features:

Multipoint detection

With thin and soft device mechanical properties and the EIT reconstruction method, our tactile interface can monitor multiple local pressure points in a single sensing area. Although the detection ability of several points is highly dependent on the position and distance of the points, still, multipoint detection ability broadens the utility of the device application.

Intrinsic stretchability

The ultrathin device’s internal active sensing area comprises robust but soft materials, allowing it to withstand external forces and deformations. The elastomeric substrate and encapsulation layer ensure conformal fit on the human body. In addition, the inner sensing area is free from interconnects, and instead, it is only composed of single homogeneous piezoresistive material, MWCNT-embedded elastomer.

Real-time 3D dynamic tracking

One of the strengths of using EIT in a tactile interface is relatively high spatiotemporal resolution with a limited number of interconnects. We have validated the device’s spatial resolution characteristics at the level of human tactile sensation, by detecting the location and the strength of the stimuli.

Extremely robust functionality

Wearable devices are always exposed to environmental damage or condition changes, such as temperature or bending. Unlike the conventional systems, the EIT-based soft tactile sensor can normally operate under severe damage, physical cutting inside the sensing region, or in extreme temperature or mechanical bending.

From these outstanding properties, our soft wearable tactile interface works similarly to the human skin with low incompatibility and high functionality. Owing to the softness of the sensor and the EIT method, we have achieved the implementation of the soft tactile interface characterized by high reliability and the capability for sustained usage. Notably, through the deep learning process of reconstructed mapping data, we further demonstrated key applications for unconventional human-machine interactions.

RESULTS

Soft tactile mapping e-skin for HMI

One of the most important features of e-skin is to have conformal contact and low mechanical mismatch with human skin (25). The soft tactile sensor is mostly composed of soft organic materials in ultrathin (~50 μm) form, resulting in conformal contact on a curvy surface (Fig. 1D and fig. S1). Our soft, thin e-skin has double-sided elastomeric layers of a mixture of PDMS and Ecoflex 0030 (PECO). PDMS and Ecoflex are widely used as binder materials in substrates and nanocomposites due to their high biocompatibility, a crucial property for wearable devices (26–31). PDMS holds superior mechanical and chemical strength, making it suitable for various bio-electronic engineering. Ecoflex, on the other hand, shows great mechanical softness for wearable bioelectronics but has lower strength. By strategically blending these two elastomers, a more optimized material, referred to as PECO, can be synthesized, boasting enhanced repeatability and reduced hysteresis, ideal for performance-sensitive applications (32). The active sensing area, consisting of an MWCNT and PECO nanocomposite, shows tough and stretchable mechanical properties (Fig. 1E). This elastic nature of the inside sensing area enhances the conformal contact on the human body by being deformed along the skin (33). Using this soft tactile sensor, we demonstrated high–spatiotemporal resolution three-dimensional (3D) pressure mapping based on EIT method along with two wearable HMI applications: handwriting recognition and drone control (Fig. 1F). Wearable tactile human-machine interaction begins with mechanical deformation of the sensing area under an external press. When pressure is applied on the PECO/MWCNT/PECO structure, the near-pressure area is locally stretched. Consequently, interconnections between MWCNTs, which form a contact resistance–free network, switch to tunneling and disconnection as strain increases (34). This transition leads to an increase in impedance (35, 36). Our sensor can measure both the presence and magnitude of applied forces due to the incorporated MWCNT that changes in resistance depending on the level of applied pressure. Through the surrounding electrodes, we continuously measure voltage information and reconstruct the impedance distribution of the sensing area by simple vector calculations. On the basis of the reconstructed impedance distribution, we perform signal processing of noise reduction, maximum impedance point detection, and signal classification via neural networks. Then, the classified dynamic pressure information is converted into HMI information for handwriting recognition and drone control.

Development and analysis of a soft and ultrathin e-skin

The softness of the e-skin is one of the most important mechanical properties for wearable devices, as it helps prevent skin irritation and ensures a good mechanical match with human skin. Moreover, the inner active sensing area should be homogeneous and aligned with the surrounding electrodes to acquire accurate EIT measurements. However, normally used patterning methods for elastomers, such as 3D printing or screen printing, are barely compatible with the conventional microscale device fabrications (37, 38). Here, we adopted the micro-electromechanical systems (MEMS) manufacturing process for device fabrication, demonstrating a homogeneous coating of MWCNT and fine alignment with the underlying flexible electronics (Fig. 2A). On top of the soft PECO substrate, SiO2 was deposited, serving dual functions: a temporary passivation layer and an adhesion enhancement layer with polyimide (fig. S2) (39). After the surrounding interconnect patterning, the inner sensing area was defined by dry etching nonstretchable polyimide, followed by SU-8 passivation of the metal interconnect. Then, active sensing material, MWCNT, was micropatterned by a lift-off process (fig. S3), followed by PECO spin-coating forming an MWCNT/PECO matrix and an encapsulation layer (fig. S4). The detailed processes of the simple and scalable fabrication of the soft tactile sensor is described in Materials and Methods and the Supplementary Materials. The fabricated ultrathin tactile sensor can be released from the glass substrate and transferred onto a soft mold or human skin for usage. The exploded view shows the device structure, an intrinsically stretchable central sensing region with a flexible periphery like a trampoline (Fig. 2B). By utilizing MEMS manufacturing, the soft tactile sensor can be fabricated on a single substrate with good scalability and reproducibility.

Fig. 2. Mechanical and electrical characteristics of the soft tactile sensor.

(A) Fabrication process of the soft e-skin device. (B) Conformal attachment of transferred e-skin on soft human skin (forearm). The red boxed inset depicts aligned MWCNT and Cu electrodes from the micropatterning process. The black boxed inset shows the MWCNT network. (C) Stress/strain response of the PECO/MWCNT/PECO structure and force/depth relation of the fabricated soft tactile sensor. (D) Stress distribution simulation of single- and multiple-point compression on a soft tactile sensor on the skin-like substrate. (E) Illustration of measurement setup for average impedance measurement. Average impedance from 16 adjacent channels is used as an electrical impedance parameter, and the press is applied to the center of the sensing area with an indentation rod. (F) Relative impedance changes of the tactile sensor according to the depth of press. (G) Relative electrical impedance changes for 10,000 cycles of press, with a depth of 2 mm. The red and blue insets show an instant response at the beginning and the end of the cycles.

To verify the softness of the device, stress-strain responses of the active PECO/MWCNT/PECO layer were measured. The measured mechanical stress was below 0.6 MPa under the applied 40% strain. Then, an external vertical pressure was applied onto the floating device using a rod to measure the depth-force response of the soft tactile sensor (Fig. 2C). A pressure of 0.2 N could lead to as high as 5 mm compression depth, showing the softness of the device. Further investigation of mechanical properties and comparison with simulation data were conducted (fig. S5). Given that this soft tactile sensor is designed for wearable touch interfaces, strain changes by local vertical pressure within the sensing area were primarily monitored. The softness of the active sensing area originates from the material properties of MWCNT and PECO. Another critical aspect of wearable tactile mapping systems is managing local stress distribution and the cross-talk effect (40). Proper stress distribution is vital for accurately locating pressure points. Conventional array-type force distribution sensors face challenges from both electrical cross-talk due to parasitic currents in electrical lines and mechanical cross-talk stemming from pressure spreading (41, 42). Our sensor, utilizing EIT technology, features electrodes positioned away from the pressure application area, thereby averting mechanical deformation of the electrodes caused by pressure and thus diminishing cross-talk effects. If the stress is not localized around the applied pressure, it becomes challenging to ascertain the intensity and distinguish between multiple pressure points. Further finite element analysis (FEA) was conducted to investigate the response of the tactile device when subjected to pressure conditions that simulate human finger touch (Fig. 2D). Upon pressure on a single sensing region, the stress distribution shows a gradual decrease extending radially from the point of pressure application. This behavior elucidates the tactile sensing mechanism of the EIT-based tactile sensor in response to external pressures. Notably, the reduction in stress magnitude resulting from a single-point pressure is found to be consistent across various locations. This consistency aligns with the stress distribution observed in a flat punch indentation scenario on a thin film/substrate system (43). By contrast, when the sensing region is subjected to multiple compressive pressures with a gap distance, depending on the exact location and magnitude of the pressure, the resultant stress distributions show a clear difference, suggesting that the applied multiple pressures can be sensed with both magnitude and location. For instance, in the case of eccentric adjacent pressure application, the stress magnitude near and between the compression points is notably elevated, while stress levels distant from the two pressure points remain low. Conversely, when the positions of these two applied pressures are altered in opposite directions, the stress distribution transforms into two distinct peaks at the diametric ends, accompanied by lower stress levels elsewhere. In addition, compared with those from one single pressure, overlaps in strain distribution are observed, and the overlap areas depend on the magnitudes of the applied pressure and their gap distance (fig. S6).

The mechanical-electrical properties of the material and the device were characterized. MWCNT, the major sensing material of the device, is basically piezoresistive material with high sensitivity (44, 45). When embedded or mixed with various elastomers, it shows good usability in strain or pressure-sensing applications. Through the direct patterning process of MWCNT on an elastomer substrate, followed by elastomer coating, a highly stretchable strain device in microscale design was achievable. The partially embedded MWCNTs in the elastomer maintain interconnections under strain and return to their initial state due to anchoring in the matrix. The mechanical property of the device under mechanical deformation was validated by testing the uniaxial stretching response, confirming the stretchability up to ~110% (fig. S7). For electrical characteristics of the device, the fabricated device was transferred onto a soft skin-like mold made of Ecoflex 0030 and the center of the inner sensing region was pressed, while measuring the pressing force and the depth. Before measuring the electromechanical characteristics of our soft e-skin device, we evaluated the electrical contact property at the Cu-MWCNT interface and assessed the channel variation, both crucial for EIT measurement. We found that the interface shows consistent ohmic contact and low variation among the 16 surrounding adjacent channels (fig. S8).

The device, featuring an unconventional design with a single cell surrounded by 16 interconnects, assesses its characteristics through averaged impedance changes of adjacent impedances from the 16 surrounding electrodes, providing a representative value of the pressure response (Fig. 2E). As the depth of the press increased up to 3 mm, the average impedance from surrounding electrodes increased exponentially by 85% (Fig. 2F). The measurements, conducted on a soft, skin-like mold, demonstrate the device’s satisfactory sensitivity, averaging approximately 27.48%/mm, which validates its potential as a wearable tactile interface. To verify the functionality and stability, a repetitive test was performed, involving the application of pressure stimuli more than 10,000 times (Fig. 2G). Because of the intrinsic hysteresis property of soft conductive elastomer, there is a slight increase in the average impedance change; however, it demonstrates stable functionality for further wearable application. Furthermore, under the static pressure test, the average impedance of the 16 adjacent electrodes slowly decreased but remained relatively stable during the 10-hour application. Upon removal of the pressure, the average impedance returned to near its original value, showing a slight decrease of less than 0.5% (fig. S9). For the detailed analysis of impedance pressure response, we further evaluated the forward/backward impedance press response loop test (fig. S10). Along with the stretching response of the device, bending is another critical issue for wearable devices. As found in the stretching experiment, the PECO/MWCNTPECO structure shows high piezo-resistivity along the applied strain, and this property can also be applied to the bending-induced strain. To minimize the bending-induced strain, the MWCNT active layer was located at the neutral mechanical plane of the device to minimize the effects of bending (46, 47).

EIT-based tactile mapping interface

The pressure-induced change in an electrical property of our tactile interface motivates us to extract information on the external pressure by utilizing electrical measurements. To achieve this, we leverage the principles of EIT to reconstruct tactile information, which is detailed in the Supplementary Materials. The fundamental premise of EIT is that the distribution of electrical properties, such as electrical conductivity and permittivity within a material, can be inferred from electrical measurements on the material’s boundary (48). Here, we exploit the neighboring data collection scheme for our data acquisition protocol (49). The data collection process begins by injecting an alternating current, denoted as i12, into two adjacent pairs of the device (Fig. 3A). As the current flows through the device, voltage measurements are taken from the surrounding 16 adjacent electrode pairs. Following this, the current is injected into the next adjacent pair on the device, i23, and the surrounding 16 voltages are measured. This procedure is repeated for all 16 pairs of electrodes. These voltage measurements are responsive to changes in the internal impedance of the sensor, which, in turn, is affected by external pressure or touch.

Fig. 3. 3D mapping performance of the EIT-assisted tactile interface.

(A) Procedure of electrical impedance tomography for the tactile sensor; 208 measured voltage data are converted into a 2D 40 × 40 image by vector calculation with sensitivity matrix. (B) 3D reconstructed relative impedance plot and projected 2D graphical plot for single-point pressure. The maximum impedance point represents a pressed point. (C) Multiple points mapping capability of the soft tactile sensor. Three points can be respectively mapped with applied local pressure magnitude. (D) An illustration of sensing resolution measurement; pressure applying in a distance of 1 mm (left) and 2D graphical plot of estimated maximum pressure points for each of nine presses (right). (E) Wide pressure sensing range of the soft tactile sensor attached on the skin-like mold (~5 mm). The right graph shows the change in average impedance from 16 adjacent electrodes (blue) and the maximum impedance value from the reconstructed image (red).

The repeated injection of alternating current for EIT measurement, considering the wearable operation of the device, raises concerns about potential harm from leakage current flowing through the human body via sweat. Although we injected a small current of 1 mA at a frequency of 1 kHz into the device, it is essential to characterize the leakage current to ensure the safety of wearable applications. Therefore, we conducted an encapsulation performance test by measuring electrical impedance spectroscopy in a phosphate-buffered saline solution to simulate a sweaty environment. The results showed that the impedance at a frequency of 1 kHz was approximately 1 megohm, which is sufficiently high to prevent current leakage through the skin surface, confirming the safety of the device against leakage currents (fig. S11).

As the conductivity reconstruction of the EIT method is based on relative and not absolute voltage, we first set the reference voltage as the initial baseline, which represents the state of the device when no pressure is applied. Then, by subtracting the measured voltages from this reference voltage, we obtain the voltage difference for every measurement. Before carrying out impedance tomography, this measured voltage data can offer insights into mechanical deformation in the sensing area, owing to the established relation between mechanical and electrical properties (fig. S12). Then, a pressure-induced change in conductivity distribution is reconstructed from the measured boundary voltages by using a reconstruction method. We adopt a linearized reconstruction method with a regularization, which can be represented as a matrix-vector calculation as followsδσ=(STS+λRTR)−1STδV

where δV is the vector of changes in voltage measurements, δσ is the vector of change in conductivity distribution, S is the sensitivity or Jacobian matrix, and R is the regularization operator. Here, the sensitivity matrix, sized [1600 × 208], incorporates the electrode position, the material’s boundary geometry, and the current injection and voltage measure patterns. This matrix plays a pivotal role in determining the dimensions of the reconstructed image, which is [40 × 40]. The actual sensing area comprises 920 pixels. For this work, we have assumed a simple model in which the device is considered as a unit disk filled with homogeneous materials. By leveraging the EIT method with a strategic layout of 16 electrodes, we successfully mapped the tactile interface with a high-resolution grid achieving an effective pixel/interconnect ratio exceeding 57. This enhancement substantially improves the interface’s tactile mapping capabilities, which are crucial for the precise detection and interpretation required in wearable tactile systems.

Along with mechanical softness, tactile mapping sensors should exhibit adequate mapping performance, represented by metrics such as minimum detectable force, discernible pressure, and dynamic change recognition. These metrics should meet or exceed the sensing capabilities of human skin. For example, through tactile sensing elements within the epidermis, humans can recognize the magnitude of external stimuli such as slight touch, mild press, or hard squeeze (50, 51). In addition, the sensing elements and connected neurons are widely and compactly spread over the skin so that they can locate the stimuli point and discriminate two different pressures in ~1-mm scale at the fingertip and ~35 mm at the forearm (52, 53). In this context, 3D mapping ability to simultaneously measure the magnitude and location of the applied stimuli is considered as a key performance indicator for tactile interfaces (54). To assess the characteristics of the soft tactile interface, the point of maximum impedance identified in the reconstructed tomography was established as the estimated pressure point for subsequent mapping performance evaluations (Fig. 3B).

Initially, the capability to detect multiple points within a single cell was evaluated in the soft tactile interface. Similar to conventional array-structured systems, multiple points can be monitored by the single active cell and surrounding electrodes (Fig. 3C and fig. S13). Three points were manually pressed, applying varying degrees of pressure to each. In three test cases, differing pressure levels were used: One point was subjected to high pressure while the remaining two were pressed more gently. The results indicated that the region subjected to greater pressure exhibited a dominant voltage change compared to the other areas. This disparity was visually confirmed through the impedance reconstruction, demonstrating the device’s capability for independent mapping of simultaneous, localized pressures within a single sensing region. This ability to detect multiple points enhances the device’s functionality, extending its applicability beyond traditional single-point tracking applications.

Subsequently, the 2D spatial resolution was assessed by pinpointing the location of maximum pressure within the impedance tomography (Fig. 3D and fig. S14). Following the initial indentation, the indentation tip was repositioned by 1 mm, after which a second press was applied. Then, pressure was administered to four points arrayed vertically, each separated by a 1-mm interval period. The specific points of pressure were discerned through the maximal impedance point estimation and were graphically represented, illustrating the device’s competency in distinguishing between pressures exerted at 1-mm intervals. This procedure highlighted the tactile interface’s spatial resolution, which is approximately 1.7 pixels/mm, as established through detailed pixel analysis across a 20-mm-diameter sensing area. This refined resolution allows for accurate detailed detection, akin to the sensory functions of human skin. The study suggests that the tactile interface closely mimics these sophisticated human sensory abilities. In-depth resolution assessments were conducted utilizing indentation tips of varying sizes. Our findings indicate that larger indentation tips can complicate the accurate estimation of the maximum impedance point, thus potentially affecting the mapping resolution. Notably, this effect becomes more pronounced as the tip size increases relative to the sensing area (fig. S15).

The device’s sensing capacity for vertical pressure sensing was evaluated, specifically focusing on variations in indentation depth (Fig. 3E). As the pressure depth was increased to 5 mm, which is equivalent to 159 kPa, both the maximum reconstructed impedance and the average impedance change were observed to rise. Given that our device has a thickness of only 50 μm, demonstrating functionality with a vertical press depth of 5 mm is a remarkable outcome. The vertical sensitivity of the soft tactile interface was further evaluated through the application of minimal pressure (fig. S16). Although the variations under small pressure change are not distinctly reflected in the voltage data, clear images correlating with increasing pressure levels were obtained. This suggests that the sensitivity and 3D mapping performance of the sensor could be enhanced through a more sophisticated reconstruction process (55, 56).

We demonstrated the tactile mapping interface with typical tactile performance considerations. The 3D mapping and multipoint detection show comparable performance to the mapping ability of human skin. The characteristics of this tactile mapping interface were compared to state-of-the-art soft tactile sensors, featuring the advantages of using the EIT method in tactile sensing (Table 1). In addition, the reconstruction method can be more advanced for higher sensitivity and spatial resolution by using a more complicated reconstruction processing.

Table 1. State-of-the-art soft electronics for wearable tactile mapping interface.

Studies	Interconnect free	Wearable demonstration	Multipoint mapping	Mechanical property	Thickness (μm)	Sensing area (mm2)	Electrode number	Pixel number	Sensing range (kPa)	
Park et al. (62)	X	X	X	Flexible	>20	0.09	16	64	300–3000	
Park et al. (6)	X	O	O	Flexible	2	784	16	64	1–120	
Kim et al. (16)	X	O	O	Flexible	>10,000	25	6	25	16–80	
Oh et al. (7)	X	O	O	Flexible	>70	600	25	128	0–5	
Huang et al. (8)	X	O	O	Flexible	>250	625	10	25	20–35	
Zhong et al. (63)	X	O	O	Stretchable	>100	10	>20	200	Unmentioned	
Cho et al. (17)	O	X	X	Flexible	132	2000, 10,000	2	16,891	N/A	
Malischewski et al. (64)	O	O	X	Stretchable	3000	18,000	4	18,000	N/A	
Lee et al. (18)	O	O	X	Stretchable	50	1200	4	9	N/A	
Xu et al. (3)	O	O	X	Stretchable	1130	10,000	2	100	N/A	
Xia et al. (19)	O	O	X	Stretchable	350	3600	2	49	N/A	
Our work	O	O	O	Stretchable	50	314	16	>920	3.2–159	

Mapping capability under various harsh environments

Wearable electronics are subjected to harsh external environments, including heat, moisture, motion artifacts, and various interferences, posing challenges for their stable operation in real-time wearable applications (57, 58). Although an encapsulation layer can shield the device from such elements, it is often insufficient in preventing unexpected mechanical damage or external temperature fluctuations. Consequently, the incorporation of compensation circuits, such as a Wheatstone bridge, and signal processing becomes essential for maintaining regular operation. In such a context, the EIT-based tactile interface exhibits remarkable mapping performance because of its reconstruction mechanism. The EIT method uses the contrast between the reference and pressed states, enabling the unique reconstruction of the mapping image according to the specific reference state (fig. S17). Typically, the reference state represents the homogeneous impedance of the sensing medium, but sometimes, certain external environmental shifts or damage can cause impedance changes inside the sensing area, even after establishing the reference state. These changes can lead to problems in accurately figuring out where the pressure is applied. In such instances, adjusting the reference state to match the changed conditions can improve the accuracy of internal mapping reconstruction (Fig. 4A). This adaptability enhances the effectiveness of our tactile interfaces, particularly in wearable applications filled with factors that could otherwise undermine tactile mapping accuracy.

Fig. 4. Robust mapping capability under various environmental changes.

(A) Calibration of unwanted noise signal by resetting reference information. By setting the reference as abnormal state, the press signal is clearly mapped. (B) The EIT-based tactile sensor shows damage-independent mapping capability by resetting or changing the reference. The graph below shows the measured voltages of the device cut state (gray) and the device under pressure on the damaged area (red). The press is normally recognized by resetting the reference state as the damaged state. (C) Average impedance changes from 16 adjacent electrodes under heating and cooling experiment and reconstructed images showing the impedance decrease overall in the sensing area. (D) An illustration of press mapping capability under high temperature and the reconstructed image. (E) Average impedance changes from 16 adjacent electrodes for various bending radii (blue). The purple bar represents the average impedance of the device for each device state on glass, float (after detachment from glass), and the skin (wearable). (F) An illustration of press mapping capability of the bent-state device and the reconstructed image.

The robust mapping capability of the EIT-based tactile sensor was tested under three extreme environments. First, irreversible mechanical damage was inflicted within the sensing area, with the device being slashed by a razor, leading to monitored impedance changes through subsequent measurements and reconstructions (Fig. 4B and fig. S18). Given that the reference was set to a nondamaged state, this permanent damage continued to be detected after an incident. However, by updating the reference to the damaged state, pressure mapping was restored to normal operation levels. This recovery of mapping functionality highlights the high usability during wearable applications and emphasizes the benefits of EIT for tactile sensors with surrounding electrodes (fig. S19).

Temperature fluctuations are a source of noise for wearable devices. Furthermore, MWCNT has a relatively high-temperature coefficient of resistance, leading to the potential that reconstructed images might reflect impedance changes resulting from temperature variations. As the device was heated up from 20° to 100°C, the average impedance measured from 16 adjacent electrodes gradually decreased by approximately 15%, mirrored by a reduction in the internally reconstructed impedance (Fig. 4C). Upon cooling the device back to initial temperature, the average internal impedance progressively returned close to its original value. Then, mapping functionality under an extremely hot environment was assessed. After updating the reference to the high-temperature state of the device, normal press detection was achieved (Fig. 4D and fig. S20). This indicates that comprehensive impedance shifts within the sensing area can be recalibrated simply by resetting the reference without further compensation circuits.

The device’s operation was evaluated under a mechanically bent state, simulating wearable conditions on the curved human body. To confirm its robustness against bending, average impedance changes for various bending radii were measured (Fig. 4E). When the device was bent over a radius of 2 mm, the average impedance changed slightly by approximately 4%, which is a smaller change compared to a 0.1-mm depth of vertical press. In addition, the average impedance was monitored during the transfer process, including placements on glass, in float, and on skin. At each stage, the overall impedance rose but maintained stable voltage specifications, crucial for the reconstruction process. Once the device was bent and after resetting the reference, the ability to map pressure points was confirmed (Fig. 4F).

The recovery ability through reference change is remarkable, illustrating that the device can operate despite severe damage or varying environments. In particular, recovery from damage such as a razor cut is highly improbable in traditional array-structured direct measurement devices, whereas the interconnect-free structure of our EIT tactile sensor facilitates such recuperative capabilities.

Wearable application of soft tactile interfaces

The thin and soft interface enables conformal contact and high functionality on the human body for HMI applications (Fig. 5A and fig. S21). This interface enables more natural and straightforward user interaction, promising a user-friendly approach to technology use. For the intuitive HMI applications, we traced the moving pressure by tracking the maximum impedance point in reconstructed image data. We added a few signal processing steps, including classification and a simple neural network, and demonstrated two real-time applications (Fig. 5B and fig. S22).

Fig. 5. Real-time applications of the wearable tactile sensor: handwriting recognition and drone control.

(A) Picture of the wearable tactile sensor on the forearm and monitoring finger touch writing the letter “S.” (B) A flowchart of HMI applications with soft tactile sensor, divided into three-section measurement, signal processing, and application. (C) Real-time 3D plot of maximum impedance point tracking for the handwriting “S.” (D) Relative accuracy plot of handwriting prediction for 26 uppercase letters. (E) Accuracy changes during 10,000 cycles of handwriting “A”; every 100 letters are used as measurement data for every 1000 cycles. (F) Real-time drone control via the tactile interface. The measured voltage represents the applied dynamic pressure changes (left). The dynamic pressure is mapped in a high-resolution image via the EIT method and divided by the number of points and movement of pressure: multiple points, rotate, press harder, and move (center). The classified command is delivered to the mini drone as landing, rotation, rise, and move (right).

Handwriting recognition is one of the typical HMI applications for wearable tactile interfaces. It requires a clear mapping capability of touch, a high SNR, localization of the applied pressure of the sensing device, and a high-performance recognition algorithm. We tracked the maximum point of the pressure and used the trace for a complete letter (Fig. 5C and fig. S23). Then, we classified the written 26 letters by using the EMNIST dataset and little practice data for the fine-tuning (fig. S24). The accuracy of the writing was demonstrated for the uppercase letters, suggesting the high potential as a soft touch interface (Fig. 5D). Considering that each letter has been configured by a simple neural network with 80 fine-tuning data, the accuracy of recognition can be enhanced by further learning process. Moreover, long-term usability is another key factor for wearable tactile measurement. We tested the device’s durability by monitoring the accuracy of handwriting recognition for 10,000 writings (Fig. 5E). During the test, the sensor showed slight degradation under repeated writings. However, by reference frame reset, we generated clear writing images. The accuracy was measured by 100 writings for every 1000 writings. In more than 10,000 writings, the soft tactile interfaces showed stable operation for an extended use (>80%). We also demonstrated many handwritings for various letters such as lowercase letters, numbers, signs, or other languages (fig. S25). Via high accuracy of recognition for uppercase letters, we completed various words, which shows more practical applicability of the tactile interface.

A tactile interface, especially with 3D mapping capability of location and magnitude, has high potential in various HMIs such as a wearable controller. The potential of our tactile interface as a wearable controller was validated through real-time drone control (Fig. 5F and fig. S26). A drone shows multiple movement modes depending on various types of command: take-off/landing, rotation, altitude change, and one-directional movement. Multipoint pressing triggers take-off/landing based on pressure’s orientation (horizontal/vertical). Single-point pressure applied near the edge initiates rotational commands (90°/180°/270°/360°). If applied near the center, increasing/decreasing pressure prompts ascent/descent, while directional pressing moves the craft into the input direction.

On the basis of the conformal adhesion of human skin, the EIT soft tactile interface shows 3D mapping capability, real-time applicability, and robust durability. Through a handwriting recognition and drone control system, we validated our device’s functionality and feasibility for HMI applications. These reliable and functional HMI demonstrations offer practical usability and expandability beyond previous e-skin devices.

Further investigation of large-area soft e-skin

One key challenge in the large-area demonstration of EIT-assisted tactile sensors is the variation in mapping performance across the conductive domain, especially with lower performance noted at the center. Wearable e-skin, in particular, requires mechanical softness and ultrathin thickness comparable to human skin, which limits the choice of materials and fabrication methods. On the basis of the fabrication method of our soft and ultrathin e-skin device, we have further demonstrated a large-area tactile interface using the EIT method (fig. S27). By using the EIT method as a robust reconstruction approach, we were able to evaluate the sensor’s pressure-sensing capabilities effectively for multiple points and various indentation shapes (spherical, planar, and annular). This level of performance was challenging to achieve with smaller-sized tactile interfaces, illustrating the unique advantages of our sensor design (fig. S28). We also tested our large-area tactile sensor by placing it on the lower back of a human participant. This test proved that the sensor works well in real time and is suitable for use in HMI, marking a big step forward from older e-skin technology (fig. S29). With the aid of computational advancement for multipoint discrimination and spatial resolution improvement, this demonstration can be further developed as a large-area wearable e-skin (59).

DISCUSSION

We have reported intrinsically soft and ultrathin e-skin for tactile interfaces using EIT for high-performance mapping. Through the MEMS fabrication process, we demonstrated soft elastomer-based substrate and micropatterning of nanomaterial (MWCNT), achieving an organic/inorganic hybrid MEMS manufacturing device in a thin film shape. This simple fabrication method demonstrated large-area soft e-skin device, which has been challenging in an EIT-based tactile interface. The fabricated device shows skin-like mechanical softness and electrical performance along with high sensitivity to the various applied pressures. A simple EIT algorithm is applied to reconstruct the pressure image with high spatiotemporal resolution. Furthermore, this indirect measurement ensures the device functionality under various harsh wearable environments. From the two wearable HMI applications, we validated the functionality of the interface: multipoint detection, 3D mapping capability, and mechanical robustness.

As this work shows the feasibility of an EIT-based soft e-skin, challenges to be solved remain as future works, for the enhanced functionalities. First, as a wearable device, a wireless communication system should be integrated with the sensor to make the system portable, which will result in adjusting this conformal tactile interface to real-life applications. In addition, by adjusting the deep learning process and high-frequency operation, smoother and clearer tactile mapping images can be reconstructed with higher resolution for differentiation of the tactile information in detail. On the basis of these development potentials, this EIT-based soft e-skin for tactile interface is expected to be applied to advanced HMI applications.

MATERIALS AND METHODS

Material preparation

The PDMS base and curing agent, Sylgard 184, was purchased from DOW. Ecoflex 0030 part A and part B were purchased from Smooth-on. MWCNT ink [1 weight % (wt %)] was purchased from Applied Carbon Nano. Two positive photoresists for the patterning process of metal and MWCNT, AZ 5214E and AZ 4620, and one negative photoresist for passivation, SU-8 2, were from Microchemicals.

Fabrication of soft and ultrathin tactile sensor

The fabrication began with depositing a Cu sacrificial layer (50 nm) onto the handling glass substrate via thermal evaporation (KVE-T2000, Korea Vacuum Tech). The Cu layer was used as an anti-adhesion layer that prevents unintended delamination of the device from the handling glass substrate during the high-temperature process, while still allowing mechanical delamination when necessary (60, 61). To prepare the soft elastomer for the device substrate, PDMS (10:1 mixing rate of base and curing agent) and Ecoflex 0030 (1,1 mixing rate of part A and part B) were mixed at a 2:1 weight ratio. The 2:1 mixing ratio was chosen based on an investigation of mechanical softness across various mixing ratios. Higher Ecoflex content reduced fabrication compatibility with subsequent processes such as photolithography (fig. S30). After being thoroughly stirred for 5 min, the composite was placed in a vacuum desiccator and degassed for 30 min before being used. The soft elastomer layer (PECO) was then formed by spin-coating (3000 rpm) as a device substrate. Subsequent curing was held on a hot plate at 60°C for 60 min, then at 80°C for 60 min, and then at 100°C for 30 min. The sample was then subjected to oxygen plasma treatment [O2 of 20 standard cubic centimeter per minute (sccm), 140 mtorr, 50 W] via a reactive ion etching system (Q190620-M01, Young Hi-Tech), for the enhanced surface adhesion with the following oxide layer. Silicon dioxide, SiO2 (100 nm), was deposited by RF sputtering (LSP-06, LAT). After rinsing with deionized (DI) water, a single layer of liquid PI was spin-coated (3000 rpm) and baked as an interconnect supporting substrate. The sample was first soft baked on a hot plate at 110°C for 1 min and 150°C for 3 min, and hard baked in a vacuum oven at 210°C for 120 min. After oxygen plasma treatment (20 sccm, 140 mtorr, 50 W), a Cu layer (450 nm) was deposited by thermal evaporation to form the metal interconnection. Using the mask aligner and exposure system [MDA-400S(IM), MIDAS system], the surrounding Cu interconnect pattern was formed through a photolithographic process. A subsequent photolithography process and dry etching process were conducted to etch the PI layer in the sensing area. Then, SU-8 2 was patterned as a passivation layer of metal interconnections. To pattern the MWCNT layer, a lift-off process using a positive photoresist (AZ P4620) was performed. After developing the process of the photoresist, the RF sputtered SiO2 was wet etched by immersing in 6:1 buffered oxide etchant for 10 s. The device was then coated with water-dispersed MWCNT solution (1.0 wt %) that has high photolithography compatibility (fig. S31), by spin-coating (2500 rpm) and baking on a hot plate at 100°C for 5 min to vaporize the DI water dispersant. A photoresist lift-off process for the removal of MWCNTs in areas other than the sensing area was performed by immersing the sample in acetone for 60 s and rinsing off the residues with acetone and IPA. The device was encapsulated by spin-coating (3000 rpm) PECO.

Experimental process of device characteristics and tactile mapping performance

Before the detachment from a glass substrate, a soft tactile sensor was subjected to oxygen plasma treatment (O2 of 20 sccm, 140 mtorr, 50 W) via a reactive ion etching system for adhesion enhancement. An anisotropic conductive film (ACF) cable was connected to the device metal interconnect for electrical contact. The ACF cable–connected device is then detached by a water-soluble tape, and skin-safe adhesive (Derma-tac, from Smooth-On) was attached to the backside of the sensor. After a minute of solvent drying, the device is carefully attached to a skin-like mold with a small amount of press. The water-soluble tape (PVA tape) is then removed by gentle dispersal of DI water for 1 min, followed by residue cleaning by repeated rubbing with a wet wiper. After drying the remaining water, the device was connected to an external EIT measurement instrument (16-channel EIT system, from Sciospec). EIT measurement was processed with an AC current 1 mA at 1 kHz by 10 fps. From the 16 adjacent electrodes, 256 AC voltages were rotationally measured for image reconstruction. For the impedance characteristics of adjacent electrodes, the voltage from the current injection pair was divided by the injected current. For the measurement of impedance from 16 adjacent electrodes, we divided the measured voltages from 16 adjacent electrodes by the injection current. The prepared soft tactile sensor on a skin-like mold is subjected to vertical force by an indentation machine and a z-axis electric push-pull tester (KMX-E1000N, MAS) (fig. S32). For the reconstruction of internal impedance, we use Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software to solve the inverse problem. On the reconstructed impedance, we extracted the maximum impedance point to represent the press point.

Experimental for wearable demonstration

The fabricated device was transferred onto the forearm of the participant by a water-soluble tape and skin-safe adhesive. The device was connected to the EIT measurement instrument for each application.

For handwriting recognition, we fine-tuned the EMNIST-based handwriting recognition algorithm with our writings, 80 writings for each uppercase letter. The recognition algorithm operated within our MATLAB program, including the EIDORS model. After the modulation of the scaling factor of the reconstruction image for visibility, the participant wrote letters with his/her fingertip. For the accuracy measurement, 100 letters were used for each alphabet.

For drone control, we used a Ryze Tello drone and connected it with a laptop by Wi-Fi. The measured press information from the reconstruction MATLAB was converted to drone commands and delivered to the drone.

Ethics statement

In accordance with Article 13, Paragraph 1 of the Bioethics and Safety Act by the Ministry of Health and Welfare, Korea, the authors confirm that Institutional Review Board approval was not needed as the study involved volunteers using wearable sensors and simple contact devices without any physical alterations or invasive methods. All participants provided informed consent for the publication of images in Fig. 5A and movies S2 and S3.

The FEA method

The FEA was conducted through the ABAQUS/Standard package. In the FEA model, the tactile sensor was bonded to an elastomer that mimics the human skin. The mechanical modulus (E) and Poisson’s ratio of the tactile sensor and the skin-like elastomer substrate were 1400 kPa and 0.45, and 150 kPa and 0.45, respectively. An eight-node brick element with reduced integration (C3D8R) elements was used, and mesh refinements under the devices were confirmed to capture the local stress concentration. For the single-point and multipoint compression, the displacement boundary condition was applied at the pressure range.

Acknowledgments

We would like to express our gratitude to J. K. Seo for insightful feedback and expert review on this research.

Funding: This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) RS-2024-00353768; an NRF grant funded by the Korean government (MSIT) RS-2023-00222166; the Yonsei Fellowship, funded by Lee Youn Jae; and Samsung Research Funding Center of Samsung Electronics under Project Number SRFC-IT1901-08.

Author contributions: Conceptualization: K. Kim, J.-H.H., and K.B., Methodology: K. Kim, J.E.J., and Y.U.C. Investigation: H.Z., M.S., K. Kang, D.J.L., and S.J. Visualization: K.L., J.P., and W.P. Supervision: K.J.Y. and J.K. Writing—original draft: K. Kim, J.-H.H., and K.B. Writing—review and editing: K.J.Y., J.K., B.X., and J.W.L.

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:

Supplementary Text

Figs. S1 to S32

Legends for movies S1 to S4

References

Other Supplementary Material for this manuscript includes the following:

Movies S1 to S4
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REFERENCES AND NOTES

1 J. Zhu, C. Zhou, M. Zhang, Recent progress in flexible tactile sensor systems: From design to application. Soft Sci. 1 , 3 (2021).
2 M. Kang, J. Kim, B. Jang, Y. Chae, J.-H. Kim, J.-H. Ahn, Graphene-based three-dimensional capacitive touch sensor for wearable electronics. ACS Nano 11 , 7950–7957 (2017).28727414
3 R. Xu, M. She, J. Liu, S. Zhao, J. Zhao, X. Zhang, L. Qu, M. Tian, Skin-friendly and wearable iontronic touch panel for virtual-real handwriting interaction. ACS Nano 17 , 8293–8302 (2023).37074102
4 A. S. Nittala, A. Withana, N. Pourjafarian, J. Steimle, Multi-touch skin: A thin and flexible multi-touch sensor for on-skin input, in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Association for Computing Machinery, 2018), pp. 1–12.
5 S. Yun, S. Park, B. Park, S. Ryu, S. M. Jeong, K.-U. Kyung, A soft and transparent visuo-haptic interface pursuing wearable devices. IEEE Trans. Ind. Electron. 67 , 717–724 (2020).
6 Y. J. Park, B. K. Sharma, S. M. Shinde, M. S. Kim, B. Jang, J. H. Kim, J. H. Ahn, All MoS2-based large area, skin-attachable active-matrix tactile sensor. ACS Nano 13 , 3023–3030 (2019).30768896
7 H. Oh, G.-C. Yi, M. Yip, S. A. Dayeh, Scalable tactile sensor arrays on flexible substrates with high spatiotemporal resolution enabling slip and grip for closed-loop robotics. Sci. Adv. 6 , eabd7795 (2020).33188031
8 Y. C. Huang, Y. Liu, C. Ma, H. C. Cheng, Q. He, H. Wu, C. Wang, C. Y. Lin, Y. Huang, X. Duan, Sensitive pressure sensors based on conductive microstructured air-gap gates and two-dimensional semiconductor transistors. Nat. Electron. 3 , 59–69 (2020).
9 X. Zhi, S. Ma, Y. Xia, B. Yang, S. Zhang, K. Liu, M. Li, S. Li, W. Peiyuan, X. Wang, Hybrid tactile sensor array for pressure sensing and tactile pattern recognition. Nano Energy 125 , 109532 (2024).
10 T. Yang, D. Xie, Z. Li, H. Zhu, Recent advances in wearable tactile sensors: Materials, sensing mechanisms, and device performance. Mater. Sci. Eng. R Rep. 115 , 1–37 (2017).
11 A. Chortos, Z. Bao, Skin-inspired electronic devices. Mater. Today 17 , 321–331 (2014).
12 R. S. Saxena, N. K. Saini, R. K. Bhan, Analysis of crosstalk in networked arrays of resistive sensors. IEEE Sens. J. 11 , 920–924 (2011).
13 Y. Luo, M. R. Abidian, J.-H. Ahn, D. Akinwande, A. M. Andrews, M. Antonietti, Z. Bao, M. Berggren, C. A. Berkey, C. J. Bettinger, J. Chen, P. Chen, W. Cheng, X. Cheng, S.-J. Choi, A. Chortos, C. Dagdeviren, R. H. Dauskardt, C. Di, M. D. Dickey, X. Duan, A. Facchetti, Z. Fan, Y. Fang, J. Feng, X. Feng, H. Gao, W. Gao, X. Gong, C. F. Guo, X. Guo, M. C. Hartel, Z. He, J. S. Ho, Y. Hu, Q. Huang, Y. Huang, F. Huo, M. M. Hussain, A. Javey, U. Jeong, C. Jiang, X. Jiang, J. Kang, D. Karnaushenko, A. Khademhosseini, D.-H. Kim, I.-D. Kim, D. Kireev, L. Kong, C. Lee, N.-E. Lee, P. S. Lee, T.-W. Lee, F. Li, J. Li, C. Liang, C. T. Lim, Y. Lin, D. J. Lipomi, J. Liu, K. Liu, N. Liu, R. Liu, Y. Liu, Y. Liu, Z. Liu, Z. Liu, X. J. Loh, N. Lu, Z. Lv, S. Magdassi, G. G. Malliaras, N. Matsuhisa, A. Nathan, S. Niu, J. Pan, C. Pang, Q. Pei, H. Peng, D. Qi, H. Ren, J. A. Rogers, A. Rowe, O. G. Schmidt, T. Sekitani, D.-G. Seo, G. Shen, X. Sheng, Q. Shi, T. Someya, Y. Song, E. Stavrinidou, M. Su, X. Sun, K. Takei, X.-M. Tao, B. C. K. Tee, A. V.-Y. Thean, T. Q. Trung, C. Wan, H. Wang, J. Wang, M. Wang, S. Wang, T. Wang, Z. L. Wang, P. S. Weiss, H. Wen, S. Xu, T. Xu, H. Yan, X. Yan, H. Yang, L. Yang, S. Yang, L. Yin, C. Yu, G. Yu, J. Yu, S.-H. Yu, X. Yu, E. Zamburg, H. Zhang, X. Zhang, X. Zhang, X. Zhang, Y. Zhang, Y. Zhang, S. Zhao, X. Zhao, Y. Zheng, Y.-Q. Zheng, Z. Zheng, T. Zhou, B. Zhu, M. Zhu, R. Zhu, Y. Zhu, Y. Zhu, G. Zou, X. Chen, Technology roadmap for flexible sensors. ACS Nano 17 , 5211–5295 (2023).36892156
14 A. Chortos, J. Liu, Z. Bao, Pursuing prosthetic electronic skin. Nat. Mater. 15 , 937–950 (2016).27376685
15 S. Yao, Y. Zhu, Wearable multifunctional sensors using printed stretchable conductors made of silver nanowires. Nanoscale 6 , 2345–2352 (2014).24424201
16 K. K. Kim, I. Ha, P. Won, D.-G. Seo, K.-J. Cho, S. H. Ko, Transparent wearable three-dimensional touch by self-generated multiscale structure. Nat. Commun. 10 , 2582 (2019).31197161
17 Y. Cho, T. Kim, G. Kim, H. W. Do, S. R. Kim, J. W. Park, J. M. Myoung, W. Shim, Three-dimensional touch device with two terminals. Adv. Mater. 35 , e2305697 (2023).37616471
18 Y. Lee, S. Lim, W. J. Song, S. Lee, S. J. Yoon, J. M. Park, M. G. Lee, Y. L. Park, J. Y. Sun, Triboresistive touch sensing: Grid-free touch-point recognition based on monolayered ionic power generators. Adv. Mater. 34 , e2108586 (2022).35245965
19 Y. Xia, Y. Zhu, X. Zhi, W. Guo, B. Yang, S. Zhang, M. Li, X. Wang, C. Pan, Transparent self-healing anti-freezing ionogel for monolayered triboelectric nanogenerator and electromagnetic energy-based touch panel. Adv. Mater. 36 , e2308424 (2024).38038698
20 K. Park, H. Yuk, M. Yang, J. Cho, H. Lee, J. Kim, A biomimetic elastomeric robot skin using electrical impedance and acoustic tomography for tactile sensing. Sci. Robot. 7 , eabm7187 (2022).35675452
21 H. Chen, X. Yang, P. Wang, J. Geng, G. Ma, X. Wang, A large-area flexible tactile sensor for multi-touch and force detection using electrical impedance tomography. IEEE Sens. J. 22 , 7119–7129 (2022).
22 X. Duan, S. Taurand, M. Soleimani, Artificial skin through super-sensing method and electrical impedance data from conductive fabric with aid of deep learning. Sci. Rep. 9 , 8831 (2019).31222040
23 E. Demidenko, An analytic solution to the homogeneous EIT problem on the 2D disk and its application to estimation of electrode contact impedances. Physiol. Meas. 32 , 1453–1471 (2011).21799240
24 A. Boyle, A. Adler, The impact of electrode area, contact impedance and boundary shape on EIT images. Physiol. Meas. 32 , 745–754 (2011).21646710
25 M. Sang, K. Kim, J. Shin, K. J. Yu, Ultra-thin flexible encapsulating materials for soft bio-integrated electronics. Adv. Sci. 9 , e2202980 (2022).
26 M. Amjadi, Y. J. Yoon, I. Park, Ultra-stretchable and skin-mountable strain sensors using carbon nanotubes-Ecoflex nanocomposites. Nanotechnology 26 , 375501 (2015).26303117
27 R. Yeasmin, S. I. Han, L. T. Duy, B. Ahn, H. Seo, A skin-like self-healing and stretchable substrate for wearable electronics. Chem. Eng. J. 455 , 140543 (2023).
28 R. Helgason, A. Banavali, Y. Lai, Cohesive dry ECG sensor using silver nanowires and PDMS tuned for adhesion. Med. Devices Sens. 2 , e10025 (2019).
29 S. Zhang, C. Lin, Z. Xia, M. Chen, Y. Jia, B. Tao, S. Li, K. Cai, A facile and novel design of multifunctional electronic skin based on polydimethylsiloxane with micropillars for signal monitoring. J. Mater. Chem. B. 8 , 8315–8322 (2020).32785401
30 C. Pang, J. H. Koo, A. Nguyen, J. M. Caves, M. G. Kim, A. Chortos, K. Kim, P. J. Wang, J. B. H. Tok, Z. Bao, Highly skin-conformal microhairy sensor for pulse signal amplification. Adv. Mater. 27 , 634–640 (2015).25358966
31 X. Zhang, Z. Li, C. Liu, J. Shan, X. Guo, X. Zhao, J. Ding, H. Yang, High-performance fingerprint bionic Ecoflex@AgNW/graphite/Pt hybrid strain sensor. J. Materiomics 10 , 7–16 (2024).
32 H. Jin Nam, J. Yeong Park, V.-P. Vu, S.-H. Choa, Effects of binder and substrate materials on the performance and reliability of stretchable nanocomposite strain sensors. J. Nanosci. Nanotechnol. 21 , 2969–2979 (2021).33653467
33 C. Yang, Z. Zhong, C. M. Lieber, Encoding electronic properties by synthesis of axial modulation-doped silicon nanowires. Science 310 , 1304–1307 (2005).16311329
34 M. Amjadi, A. Pichitpajongkit, S. Lee, S. Ryu, I. Park, Highly stretchable and sensitive strain sensor based on silver nanowire-elastomer nanocomposite. ACS Nano 8 , 5154–5163 (2014).24749972
35 C. Robert, J. F. Feller, M. Castro, Sensing skin for strain monitoring made of PC–CNT conductive polymer nanocomposite sprayed layer by layer. ACS Appl. Mater. Interfaces 4 , 3508–3516 (2012).22704247
36 S. J. Lim, H. S. Lim, Y. Joo, D.-Y. Jeon, Impact of MWCNT concentration on the piezo-impedance response of porous MWCNT/PDMS composites. Sens. Actuators A. Phys. 315 , 112332 (2020).
37 R. Wang, S. Hu, W. Zhu, Y. Huang, W. Wang, Y. Li, Y. Yang, J. Yu, Y. Deng, Recent progress in high-resolution tactile sensor array: From sensor fabrication to advanced applications. Prog. Nat. Sci. Mater. Int. 33 , 55–66 (2023).
38 C.-W. Ma, T.-H. Lin, Y.-J. Yang, Tunneling piezoresistive tactile sensing array fabricated by a novel fabrication process with membrane filters, in 2015 28th IEEE International Conference on Micro Electro Mechanical Systems (MEMS) (IEEE, 2015), pp. 249–252.
39 A. Deshpande, E. Pourshaban, C. Ghosh, A. Banerjee, H. Kim, C. Mastrangelo, Adhesion strength of PDMS to polyimide bonding with thin-film silicon dioxide, in 2021 IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS) (IEEE, 2021), pp. 1–4.
40 S. Pyo, J. Lee, K. Bae, S. Sim, J. Kim, Recent progress in flexible tactile sensors for human-interactive systems: From sensors to advanced applications. Adv. Mater. 33 , e2005902 (2021).33887803
41 Y. Zhang, Q. Lu, J. He, Z. Huo, R. Zhou, X. Han, M. Jia, C. Pan, Z. L. Wang, J. Zhai, Localizing strain via micro-cage structure for stretchable pressure sensor arrays with ultralow spatial crosstalk. Nat. Commun. 14 , 1252 (2023).36878931
42 M. Liu, Y. Zhang, J. Wang, N. Qin, H. Yang, K. Sun, J. Hao, L. Shu, J. Liu, Q. Chen, P. Zhang, T. H. Tao, A star-nose-like tactile-olfactory bionic sensing array for robust object recognition in non-visual environments. Nat. Commun. 13 , 79 (2022).35013205
43 B. X. Xu, B. Zhao, Z. F. Yue, Finite element analysis of the indentation stress characteristics of the thin film/substrate systems by flat cylindrical indenters. Materwiss. Werksttech. 37 , 681–686 (2006).
44 J. Du, L. Wang, Y. Shi, F. Zhang, S. Hu, P. Liu, A. Li, J. Chen, Optimized CNT-PDMS flexible composite for attachable health-care device. Sensors 20 , 4523 (2020).32823502
45 H. Li, W. Zhang, Q. Ding, X. Jin, Q. Ke, Z. Li, D. Wang, C. Huang, Facile strategy for fabrication of flexible, breathable, and washable piezoelectric sensors via welding of nanofibers with multiwalled carbon nanotubes (MWCNTs). ACS Appl. Mater. Interfaces 11 , 38023–38030 (2019).31556287
46 S. Li, Y. Su, R. Li, Splitting of the neutral mechanical plane depends on the length of the multi-layer structure of flexible electronics. Proc. R. Soc. A. Math. Phys. Eng. Sci. 472 , 20160087 (2016).
47 C. Keum, C. Murawski, E. Archer, S. Kwon, A. Mischok, M. C. Gather, A substrateless, flexible, and water-resistant organic light-emitting diode. Nat. Commun. 11 , 6250 (2020).33288769
48 A. Adler, D. Holder, Electrical Impedance Tomography: Methods, History and Applications (CRC Press, 2021).
49 J. K. Seo, E. J. Woo, Nonlinear Inverse Problems in Imaging (John Wiley & Sons, 2013).
50 E. P. Gardner, J. H. Martin, Coding of sensory information. Princ. Neural Sci. 4 , 411–429 (2000).
51 K. O. Johnson, The roles and functions of cutaneous mechanoreceptors. Curr. Opin. Neurobiol. 11 , 455–461 (2001).11502392
52 R. S. Johansson, A. B. Vallbo, Tactile sensibility in the human hand: Relative and absolute densities of four types of mechanoreceptive units in glabrous skin. J. Physiol. 286 , 283–300 (1979).439026
53 K. Myles, M. S. Binseel, The tactile modality: A review of tactile sensitivity and human tactile interfaces. Army Res. Lab. 21005 , 5425 (2007).
54 X. Pu, Q. Tang, W. Chen, Z. Huang, G. Liu, Q. Zeng, J. Chen, H. Guo, L. Xin, C. Hu, Flexible triboelectric 3D touch pad with unit subdivision structure for effective XY positioning and pressure sensing. Nano Energy 76 , 105047 (2020).
55 S. Quqa, Y. Shu, S. Li, K. J. Loh, Pressure mapping using nanocomposite-enhanced foam and machine learning. Front. Mater. 9 , 862796 (2022).
56 A. Coxson, I. Mihov, Z. Wang, V. Avramov, F. B. Barnes, S. Slizovskiy, C. Mullan, I. Timokhin, D. Sanderson, A. Kretinin, Machine learning enhanced electrical impedance tomography for 2D materials. Inverse Probl. 38 , 085007 (2022).
57 D. Hemapriya, P. Viswanath, V. M. Mithra, S. Nagalakshmi, G. Umarani, Wearable medical devices—Design challenges and issues, in 2017 International Conference on Innovations in Green Energy and Healthcare Technologies (IGEHT) (IEEE, 2017), pp. 1–6.
58 W. Wu, H. Haick, Materials and wearable devices for autonomous monitoring of physiological markers. Adv. Mater. 30 , e1705024 (2018).29498115
59 H. Park, K. Park, S. Mo, J. Kim, Deep neural network based electrical impedance tomographic sensing methodology for large-area robotic tactile sensing. IEEE Trans. Robot. 37 , 1570–1583 (2021).
60 H. Wang, X. Dou, Z. Wang, Z. Liu, Q. Ye, R. Guo, F. Zhou, Boosting sensitivity and durability of pressure sensors based on compressible Cu sponges by strengthening adhesion of “rigid-soft” interfaces. Small 19 , e2303234 (2023).37501331
61 Z. Liu, H. Wang, P. Huang, J. Huang, Y. Zhang, Y. Wang, M. Yu, S. Chen, D. Qi, T. Wang, Y. Jiang, G. Chen, G. Hu, W. Li, J. Yu, Y. Luo, X. J. Loh, B. Liedberg, G. Li, X. Chen, Highly stable and stretchable conductive films through thermal-radiation-assisted metal encapsulation. Adv. Mater. 31 , e1901360 (2019).31282042
62 J. Park, R. Ghosh, M. S. Song, Y. Hwang, Y. Tchoe, R. K. Saroj, A. Ali, P. Guha, B. Kim, S. W. Kim, M. Kim, G. C. Yi, Individually addressable and flexible pressure sensor matrixes with ZnO nanotube arrays on graphene. NPG Asia Mater. 14 , 40 (2022).
63 D. Zhong, C. Wu, Y. Jiang, Y. Yuan, M. G. Kim, Y. Nishio, C. C. Shih, W. Wang, J. C. Lai, X. Ji, T. Z. Gao, Y. X. Wang, C. Xu, Y. Zheng, Z. Yu, H. Gong, N. Matsuhisa, C. Zhao, Y. Lei, D. Liu, S. Zhang, Y. Ochiai, S. Liu, S. Wei, J. B. H. Tok, Z. Bao, High-speed and large-scale intrinsically stretchable integrated circuits. Nature 627 , 313–320 (2024).38480964
64 M. Malischewski, M. Adelhardt, J. Sutter, K. Meyer, K. Seppelt, Isolation and structural and electronic characterization of salts of the decamethylferrocene dication. Science 353 , 678–682 (2016).27516596
65 E. Somersalo, M. Cheney, D. Isaacson, Existence and uniqueness for electrode models for electric current computed tomography. SIAM J. Appl. Math. 52 , 1023–1040 (1992).
66 K. Lee, E. J. Woo, J. K. Seo, A fidelity-embedded regularization method for robust electrical impedance tomography. IEEE Trans. Med. Imaging 37 , 1970–1977 (2018).29035213
67 A. Adler, W. R. B. Lionheart, Uses and abuses of EIDORS: An extensible software base for EIT. Physiol. Meas. 27 , S25–S42 (2006).16636416
68 A. Adler, J. H. Arnold, R. Bayford, A. Borsic, B. Brown, P. Dixon, T. J. C. Faes, I. Frerichs, H. Gagnon, Y. Gärber, GREIT: A unified approach to 2D linear EIT reconstruction of lung images. Physiol. Meas. 30 , S35–S55 (2009).19491438
69 J. L. Mueller, S. Siltanen, D. Isaacson, A direct reconstruction algorithm for electrical impedance tomography. IEEE Trans. Med. Imaging 21 , 555–559 (2002).12166850
