Near-hysteresis-free soft tactile electronic skins for wearables and reliable machine learning

Abstract

Electronic skins are essential for real-time health monitoring and tactile perception in robots. Although the use of soft elastomers and microstructures have improved the sensitivity and pressure-sensing range of tactile sensors, the intrinsic viscoelasticity of soft polymeric materials remains a long-standing challenge resulting in cyclic hysteresis. This causes sensor data variations between contact events that negatively impact the accuracy and reliability. Here, we introduce the Tactile Resistive Annularly Cracked E-Skin (TRACE) sensor to address the inherent trade-off between sensitivity and hysteresis in tactile sensors when using soft materials. We discovered that piezoresistive sensors made using an array of three-dimensional (3D) metallic annular cracks on polymeric microstructures possess high sensitivities (> 10⁷ Ω ⋅ kPa^-1), low hysteresis (2.99 ± 1.37%) over a wide pressure range (0-20 kPa), and fast response (400 Hz). We demonstrate that TRACE sensors can accurately detect and measure the pulse wave velocity (PWV) when skin mounted. Moreover, we show that these tactile sensors when arrayed enabled fast reliable one-touch surface texture classification with neuromorphic encoding and deep learning algorithms.

Keywords: electronic skin; machine learning; robotics; sensor; wearable.

Fig. 1.

Schematics of TRACE sensor and applications in health monitoring and robotics tactile perception. (A) Illustration of TRACE piezoresistive tactile sensing elements with 3D and regularly cracked bilayer micropyramids made of metal film and elastomer microstructure. (B) Scanning electron microscope (SEM) image of Pt-coated micropyramid array with controlled annular cracks. (C) Demonstration of local PWV measurement on a single-patch healthcare wearable by assembling TRACE on multichannel electrodes. (D) Illustration of TRACE sensor matrix on a prosthetic hand with a flexible sensor array. (E) Schematic of one-touch surface texture classifications.

Fig. 2.

Mechanism of SIP method for regular annular cracks. (A) Comparison of random and controllable annular cracks of Pt film on micropyramid (Top view). The pristine Pt-film coating on micropyramid elastomer leads to the onset crack on the tips due to intensive stress concentration, propagating into uncontrollable random cracks with the increase of pressure. The proposed SIP method can generate regular and annular morphology cracks by rearranging stress distribution on bilayer micropyramid. (B) The optimized electromechanical property of the SIP method with annular cracks compared to random cracks. The annular cracks-induced gradual trend of piezoresistance is more suitable for a tactile sensor for a wide range of pressure, in comparison with sharp drops of resistance based on random cracks. (C) Schematic of SIP method for generating annular cracks of Pt film on micropyramid. (D) Theoretical model of SIP method for the design of stiff Pt film/PDMS bilayer micropyramid structure when applied compression force, where α, h, and h_c are the sidewall angle of the pyramid, indentation depth, and the depth of the contact region, respectively. (E) FE simulation of stress-field distribution of bilayer microstructure by SIP method. The maximum stress occurs in annular fashion in a one-quarter model of bilayer structure (Left), and the stress distribution of Pt film on the surface (Right).

Fig. 3.

Piezoresistive response of TRACE sensor. (A) The schematic model of the conductive pathways of TRACE sensor under compressing loads. (B) The pressure-induced electrical performance of TRACE sensor, compared with the PEDOT:PSS-coated micropyramid sensor. (C) Benchmark of TRACE sensor with both sensitivity and hysteresis being considered. We used S_p/DH_e as an indicator to simultaneously evaluate sensitivity and hysteresis of resistive-type sensors, where S_p represents the peak sensitivity, and DH_e is the degree of hysteresis, respectively. The smaller DH_e (or namely, larger 1/DH_e) means a lower hysteresis observed for the piezoresistive sensor, indicating more accurate and repeatable sensing performance of TRACE sensor. Yellow region: low hysteresis; orange region: high sensitivity. (D) The ability of TRACE sensor to detect a small pressure of a floribunda petal under the preload of 7 kPa, where _σ1 is the conductance of the preload pressure of 7 kPa, respectively, where the black scale bar is 10 mm. (E) The output voltage of TRACE sensor under different frequencies when applied pressure. (F) Cyclic testing of TRACE sensor: time-resolved performance of successive 10 cycles (Top) and maximum conductivity change over 10,000 cycles (Bottom).

Fig. 4.

Superficial pulse measurement and pulse tracing. (A) The illustration of superficial pulse measurement located on the human neck and wrist. The zoom-in images show the photographs of the pulse measurement on a volunteer. (B) The graphs show results of the simultaneous pulse measurement at the carotid artery and radial artery, and (C) a zoom-in image showing the measurement result of one radial pulse. The specific peak and wave information are indicated. (D) The optical image of flexible electrodes with multiple channels. (E) The photograph showing localized pulse tracing by integrating TRACE sensor with multichannel electrodes. (F and G) The graphs showing the results of localized pulse measurement at the radial artery and pulse tracing.

Fig. 5.

High-density pressure distribution mapping on a robotic hand. (A) Images of conformable electrodes of TRACE-SA/FA sensor matrix attached to the index fingertip of a robot hand. (B) Design of electrodes (10 × 10) of sensor matrix, and laser-cut N, U, and S letters were touching TRACE-SA sensor matrix. (C–H) The pressure distribution map of TRACE-SA sensor matrix touching N, U, and S letters at different rotate angles.

Fig. 6.

Texture classification using deep learning. (A) Photographs of textures used in the classification task. (Scale bar, 200 μm.) (B) Examples of the corresponding surface profile measurements taken by a 3D optical profiler for sandpaper 100C and 600C. (C) The evolution of the tactile image of TRACE-FA sensor array over time for sandpaper 600C. (D and E) The confusion matrix of TRACE-FA and PEDOT:PSS-FA sensor arrays, respectively. (F) Average classification accuracy of the sensor arrays using CNNs. Error bars denote the standard derivation (SD). (G) Comparison of the classification accuracy achievable with various contact times. Error bars denote the SD.