Applications In Human-machine Interaction Research Articles

Measuring Motor Unit Discharge, Myofiber Vibration, and Haemodynamics for Enhanced Myoelectric Gesture Recognition

It is of great significance to recognize hand gestures via measuring biological signals from forearm muscles for portable human–machine interaction (HMI). Decreasing the number of sensor nodes is imperative for practical HMI applications. However, it would be an enormous challenge to maintain gesture recognition performance for traditional myoelectric interface with sparse-site sensing. To overcome this drawback, we present a novel HMI predicting more than ten hand and wrist motions relying on only two hybrid mini-grid surface electromyography (sEMG), mechanomyography (MMG), and near-infrared spectroscopy (NIRS) sensor nodes. Beyond the time domain (TD) features of sEMG, additional information containing movement intention is measured from motor unit (MU) action potential trains (MUAPts) according to the decomposition of four-channel arrayed sEMG. Furthermore, low-frequency myofiber vibration and haemodynamics are extracted from MMG and NIRS, respectively. Experiments are performed on 13 healthy subjects to recognize 12 hand and wrist gestures. The results indicate that combining motor unit discharge feature yields consistently higher ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${p} &lt; 0.01$ </tex-math></inline-formula> ) classification accuracy (CA) (91.6%) than traditional TD features of sEMG (87.8%) using linear discrimination analysis (LDA) classifier. Additionally, both MMG and NIRS features are demonstrated effective supplementary to distinguish muscular activation patterns, producing significantly enhanced (4.2%–11.2%, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${p} &lt; 0.05$ </tex-math></inline-formula> ) recognition performance with the fused information. The outcomes of this study are promising for the HMI applications such as controlling prosthetic hand and wearable device.

3D printed smart glove with pyramidal MXene/Ecoflex composite-based toroidal triboelectric nanogenerators for wearable human-machine interaction applications

Self-powered sensors based on triboelectric nanogenerators (TENGs) have shown great advantages in human-machine interactions. However, the complex preparation process and high cost of conventional electrodes hinder their practical implementation. In this study, we develop a wearable self-powered toroidal triboelectric sensor (STTS) with a pyramidal structure for self-powered human-machine interactions based on an extremely simplified design strategy. 3D printing technology is employed to fabricate pyramidal arrays on MXene/Ecoflex nanocomposites, thus providing a comfortable space between the finger skin and the negatively charged layer to overcome the space requirements in traditional triboelectric-based sensors. Furthermore, the developed pyramidal structure of the nanocomposite and flexible conductive fabric electrodes assembled with 3D-printed gloves based on the flexible TPU material maintained the wearability of the sensing system. The flexible and simplified single-electrode design strategy of the STTS can be easily worn on the human hand for comfortable and natural interaction with machines and devices. The high peak-to-peak voltage (19.91 V), high sensitivity (0.088 VkPa−1), and wide pressure detection range (0–120 kPa) enable the generation of high-quality output signals for the accurate detection of various finger movements exhibiting great potential for use in human-machine interaction applications in next-generation artificial intelligence and interactive devices.

Air Permeable Vibrotactile Actuators for Wearable Wireless Haptics

AbstractVibrotactile actuators can evoke mechanical stimulations on human skins to induce haptic feedbacks for various human machine interaction applications. However, efforts toward their practical usages encounter several engineering challenges, including wearable comfortability and output abilities. Here, air permeable actuators are developed and embedded in common fabrics for vibrotactile actuation, achieving excellent air permeability of 108 L m−2 s−1, low preload requirement of 10 mN, high output sensitivity of 0.2 mN/V, and good mechanical durability by surviving 11 million testing cycles. As demonstration examples, a wireless haptic feedback glove is shown to distinguish 32 different English characters and symbols with an overall accuracy of 97.8%, and large size actuators (10 × 10 cm2) are also proved for providing haptic feedback for parts of human body. As such, the proposed system opens a new class of wearable vibrotactile actuators for potential applications in wide fields of metaverse, teleoperation, smart textiles, and robotics.

Automatic Foreground Detection at 784 FPS for Ultra-High-Speed Human–Machine Interactions

Human-machine interactive systems show increasing demand for analysing fast moving objects in high-frame-rate videos. Robust foreground detection, which is able to reduce large amount of redundant background data from high-frame-rate video, becomes the essence to achieve ultra-high-speed human-machine interactions. This paper proposes a local spatial propagation based background model generation, a local linear illumination correction based background model update, and a regional central coordinates and edge keypoints constrained foreground region reselection. The three proposals make up a robust and hardware-friendly foreground detection method. Experimental results prove that the proposed hardware-friendly algorithm achieves high accuracy and robustness on various kinds of challenging cases. Meanwhile, the hardware implementation utilizes little hardware resources and achieves realtime processing of high-frame-rate (784 frame/second) video with the delay less than 1 ms/frame in image processing core. In addition, a practical system is implemented by combing a PC, a high-speed camera and a field programmable gate array (FPGA) for realworld applications. This work will significatively promote the development and application of high-speed human machine interaction. A demo of the proposed vision system working at 784 FPS is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://wcms.waseda.jp/em/5f84f75136a6</uri> . Note to Practitioners—This paper was motivated by the problem of high-frame-rate video contains large amount of redundant background pixels which makes ultra-high-speed human-machine interactions inaccessible. Existing approaches are mainly focused on designing complex background models, but processing speed, which is the most important issue for ultra-high-speed human-machine interactions, has received relatively little attention. This paper suggests a robust and hardware-friendly foreground detection algorithm which has been implemented as a hardware system by using an FPGA, a high-frame-rate camera, and a PC. We show that the hardware implementation utilizes less hardware resources and achieves real-time processing speed of 784 FPS with the delay less than 1 ms/frame in the image processing core. This work is a pioneering attempt of ultra-high-speed foreground detection, which will significatively speed up the wide applications of ultra-high-speed human machine interactions.

A Flexible Triboelectric Nanogenerator Based on Multilayer MXene/Cellulose Nanofibril Composite Film for Patterned Electroluminescence Display.

The flexible self-powered display system integrating a flexible triboelectric nanogenerator (TENG) and flexible alternating current electroluminescence (ACEL) has attracted increasing attention for its promising potential in human–machine interaction applications. In this work, a performance-enhanced MXene/cellulose nanofibril (CNF)/MXene-based TENG (MCM-TENG) is reported for powering a flexible patterned ACEL device in order to realize self-powered display. The MCM multilayer composite film was self-assembled through the layer-by-layer method. The MCM film concurrently acted as a triboelectric layer and electrode layer due to its high conductivity and strength. Moreover, the effect of CNF concentration and number of layers on the output performance of TENG was investigated. It was found that the MCM-TENG realized the optimum output performance. Finally, a flexible self-powered display device was realized by integrating the flexible TENG and ACEL. The MCM-TENG with an output voltage of ≈90 V at a frequency of 2 Hz was found to be efficient enough to power the ACEL device. Therefore, the as-fabricated flexible TENG demonstrates a promising potential in terms of self-powered displays and human–machine interaction.

Self-powered multifunctional body motion detectors based on highly compressible and stretchable ferroelectrets with an air-filled parallel-tunnel structure

The new kind of ferroelectret electroactive polymers for flexible film sensors represent an attractive opportunity for self-powered wearable devices, which could offer sustainable and convenient health care services for people without the problem of energy supply. However, due to the mechanical heterogeneity, obtaining ferroelectrets with both a high longitudinal and transverse piezoelectric response is still a great challenge, which limits its multifunctional sensing application, especially in body motion tracking. Herein, we develop an air-filled parallel tunnel ferroelectret-based self-powered body motion detectors. Ascribed to the high mechanical compression and tensile properties of the specially designed parallel tunnel void structure and excellent charge storage stability of the fluorinated polyethylene propylene (FEP) electret film, these laminated films exhibit many fascinating merits, including ultrahigh longitudinal piezoelectric response (piezoelectric coefficient of 6200 pC/N, pressure sensitivity of 1950 pC/kPa), wide pressure detection range (0.03–62 kPa), remarkable transverse piezoelectric response (tensile sensitivity of 1600 pC/mm), long-term durability (1,08,0000 cycles), and superior friction force detection capability. This is the first work, to our knowledge, that ever use one kind of piezoelectric mechanism in a single ferroelectret-based sensor to achieve longitudinal stress, transverse stress, and tangential friction force sensing. Furthermore, on the basis of such sensors and machine learning algorithms, we designed an intelligent gesture recognition system for future human-machine interaction applications. Looking forward, our material design methodologies offer an approach to the self-powered flexible multifunctional sensor with great prosperity in household healthcare, smart mobile medical electronics, and humanoid robots.

Flexible Pressure Sensors with Combined Spraying and Self-Diffusion of Carbon Nanotubes.

High-performance wearable sensors are required for applications in medical health and human-machine interaction, but their application has limited owing to the trade-off between sensitivity, pressure range, and durability. Herein, we propose the combined spraying and self-diffusion process of carbon nanotubes (CNTs) to balance and improve these parameters with the CNTs spontaneously diffusing into the film surface before the film curing. The obtained sensor not only achieves high sensitivity (155.54 kPa-1) and ultrawide pressure detection range (0.1-500 kPa) but also exhibits exceptional durability (over 12,000 pressure cycles at a high pressure of 300 kPa). In addition, the sensor exhibits a fast response (25 ms), good stability, and full flexibility. This process is a general approach that may improve the performance of various types of thin film piezoresistive sensors. Besides, the fabricated sensors can be flexibly scaled into sensor arrays and communicate with smart devices to achieve wireless smart monitoring. At present, the sensor shows broad application prospects in the fields of intelligent medical health and motion sensing.

Deep human answer understanding for natural reverse QA

This study focuses on a reverse question answering (QA) procedure, in which machines proactively raise questions and humans supply the answers. This procedure exists in many real human–machine interaction applications. However, a crucial problem in human–machine interaction is answer understanding. Existing solutions have relied on mandatory option term selections to avoid automatic answer understanding. However, these solutions have led to unnatural human–computer interaction and negatively affected user experience. Thus, we propose a novel deep answer understanding network, AntNet, for reverse QA. The network consists of three new modules, namely, a skeleton attention for questions, a relevance-aware representation of answers, and a multi-hop-based fusion. Furthermore, to alleviate the negative influences of some quite difficult human answers, an improved self-paced learning strategy is proposed to train the AntNet by assigning different weights to training samples according to their learning difficulties. Given that answer understanding for reverse QA has not been explored, a new data corpus is compiled in this study. Experimental results indicate that our proposed network is significantly better than existing methods and those modified from classical natural language processing deep models. The effectiveness of the three modules and the improved self-paced learning strategy is also verified.

Sweeping-responsive interface using the intrinsic polarity of magnetized micropillars for self-powered and high-capacity human-machine interaction

The rapid development of wearable tactile sensors in human-machine interaction (HMI) is revolutionizing the way that people communicate with intelligent terminals. Compared with “tapping”, another daily operation, “directional sweeping”, is rarely explored in tactile sensors for HMI due to the limits from device configuration. Herein, we designed and optimized the in-plane magnetized flexible micropillars as the self-powered interface that can perceive the sweeping with distinguishable signals according to the operation directions. Based on Faraday’s law of induction, the intrinsic magnetic polarity was applied as an avenue to rapidly produce diverse electromotive forces of “+ /-” and “-/+ ” through the bi-directional micropillar deformation. With the self-powered interface, several interesting HMI applications, e.g., smartphone page flip, Morse code communication, and game playing, were demonstrated via habitual finger sweeping with high efficiency, accuracy, intuitive experience, and control diversity. Furthermore, the unique behavior of “+ /-” and “-/+ ” enables the build-up of ternary system with broader command capacity of 3 n if n devices were integrated in parallel. Along with the merits such as high sensitivity, applicable diversity (humid condition), mechanical robustness, and coding accuracy, we believe that the study would bring inspiration of future HMI design for a more fascinating, effective and intelligent living.

Tactile Near-Sensor Analogue Computing for Ultrafast Responsive Artificial Skin.

Ultrafast artificial skin enables unprecedented tactile internet applications in prosthetics, robotics, and human-machine interactions. However, current artificial skin systems that rely on front-end interface electronics typically perform redundant data transfer and analogue-to-digital conversions for decision-making, causing long latency (milliseconds). Here, a near-sensor analogue computing system based on a flexible memristor array for artificial skin applications is reported. This system, which seamlessly integrates a tactile sensor array with a flexible hafnium oxide memristor array, can simultaneously sense and compute raw multiple analogue pressure signals without interface electronics. As a proof-of-concept, the system is used for real-time noise reduction and edge detection of tactile stimuli. One sensing-computing operation of this system takes about 400ns and consumes on average 1000 times less power than a conventional interface electronic system. The results demonstrate that near-sensor analogue computing offers an ultrafast and energy-efficient route to large-scale artificial skin systems.

All-Nanofibrous Ionic Capacitive Pressure Sensor for Wearable Applications.

Currently, with the development of electronic skins (e-skins), wearable pressure sensors with low energy consumption and excellent wearability for long-term physiological signal monitoring are urgently desired but remain a challenge. Capacitive-type devices are desirable candidates for wearable applications, but traditional capacitive pressure sensors are limited by low capacitance and sensitivity. In this study, an all-nanofibrous ionic pressure sensor (IPS) is developed, and the formation of an electrical double layer at the electrode/electrolyte contact interface significantly enhances the capacitance and sensing properties. The IPS is fabricated by sandwiching a nanofibrous ionic gel sensing layer between two thermoplastic polyurethane nanofibrous membranes with graphene electrodes. The IPS has a high sensitivity of 217.5 kPa-1 in the pressure range of 0-5 kPa, which is much higher than that of conventional capacitive pressure sensors. Combined with the rapid response and recovery speed (30 and 60 ms), the IPS is suitable for real-time monitoring of multiple physiological signals. Moreover, the nanofiber network endows the IPS with excellent air permeability and heat dissipation, which guarantees comfort during long-term wearing. This work provides a viable strategy to improve the wearability of wearable sensors, which can promote healthcare and human-machine interaction applications.

Facial expression recognition under constrained conditions using stacked generalized convolution neural network

A cognitive-analysis of facial features can make facial expression recognition system more robust and efficient for Human-Machine Interaction (HMI) applications. Through this work, we propose a new methodology to improve accuracy of facial expression recognition system even with the constraints like partial hidden faces or occlusions for real time applications. As a first step, seven independent facial segments: Full-Face, half-face (left/right), upper half face, lower half face, eyes, mouth and nose are considered to recognize facial expression. Unlike the work reported in literature, where arbitrarily generated patch type occlusions on facial regions are used, in this work a detailed analysis of each facial feature is explored. Using the results thus obtained, these seven sub models are combined using a Stacked Generalized ensemble method with deep neural network as meta-learner to improve accuracy of facial expression recognition system even in occluded state. The accuracy of the proposed model improved up to 30% compared to individual model accuracies for cross-corpus seven model datasets. The proposed system uses CNN with RPA compliance and is also configured on Raspberry Pi, which can be used for HRI and Industry 4.0 applications which involve face occlusion and partially hidden face challenges.

Room-temperature high-precision printing of flexible wireless electronics based on MXene inks

Wireless technologies-supported printed flexible electronics are crucial for the Internet of Things (IoTs), human-machine interaction, wearable and biomedical applications. However, the challenges to existing printing approaches remain, such as low printing precision, difficulty in conformal printing, complex ink formulations and processes. Here we present a room-temperature direct printing strategy for flexible wireless electronics, where distinct high-performance functional modules (e.g., antennas, micro-supercapacitors, and sensors) can be fabricated with high resolution and further integrated on various flat/curved substrates. The additive-free titanium carbide (Ti3C2Tx) MXene aqueous inks are regulated with large single-layer ratio (>90%) and narrow flake size distribution, offering metallic conductivity (~6, 900 S cm−1) in the ultrafine-printed tracks (3 μm line gap and 0.43% spatial uniformity) without annealing. In particular, we build an all-MXene-printed integrated system capable of wireless communication, energy harvesting, and smart sensing. This work opens a door for high-precision additive manufacturing of printed wireless electronics at room temperature.

A Textile Proximity/Pressure Dual-Mode Sensor Based on Magneto-Straining and Piezoresistive Effects

The demand for flexible tactile sensors with both contact and contactless sensing capabilities is growing rapidly in the fields of smart robotics and prosthesis. However, it is still a challenge to integrate high performance proximity and pressure sensing functions in one sensor through simple fabrication process. Here, we propose a textile-based dual-mode sensor that can work on both magneto-straining mode (proximity) and piezoresistive mode (pressure). The sensor is simply fabricated by coating two functional materials in a concentric pattern, i.e., a magnetic layer followed by a conductive layer made of 2D MXene composite, on spandex substrate. As the dual-mode sensor approaches to a magnetic object, the attractive magnetic force will stretch the magnetic layer, along with the conductive layer. The strained conductive layer causes an increase of the resistance. Upon contact, the magnetic force reaches its maximum, while the pressure force works against the magnetic force and starts to reduce the strain of the conductive layer, leading to a decrease of the resistance. The turning point of the resistance can be used to identify the contact moment, and as an indicator of the switching of the two working modes of the sensor. Benefitting from the superior conductivity of the MXene material in the conductive layer, high sensitivity for both proximity sensing (13.76 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> ) and pressure sensing (0.7 kPa <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> ) was achieved. The proposed dual-mode sensing methodology has great potential in human-machine interaction (HMI) applications.

Integrated, self-powered, and omni-transparent flexible electroluminescent display system

Flexible transparent displays with self-powered features are crucial for artificial electronic skins and information communication in the internet of things (IoT). Here, we present a self-powered flexible display system by integrating the transparent alternating current electroluminescence (ACEL) device with a triboelectric nanogenerator (TENG), sharing the common transparent electrode. The emissive layer of ZnS:Cu@PDMS is sandwiched by transparent electrodes of single-wall carbon nanotubes (SWCNTs) and ITO/PET substrate to prepare transparent ACELs. The TENG composed of two transparent tribo-layers ITO and PDMS is obtained and demonstrates an open-circuit voltage of 200 V and a short-circuit current of 6 µA, which can easily light up ACEL devices. A ‘ZZU’ pattern array is instantly carried out by gently pressing the self-powered flexible transparent display system. The integrated system renders high transmittance over 80% in almost the entire visible region. This work possesses considerable potential applications in human-machine interactions, artificial electronic skins, and self-powered communications self-power communication in the IoT.

Ultrasoft Porous 3D Conductive Dry Electrodes for Electrophysiological Sensing and Myoelectric Control.

Biopotential electrodes have found broad applications in health monitoring, human-machine interactions, and rehabilitation. Here, we report the fabrication and applications of ultrasoft breathable dry electrodes that can address several challenges for their long-term wearable applications - skin compatibility, wearability, and long-term stability. The proposed electrodes rely on porous and conductive silver nanowire based nanocomposites as the robust mechanical and electrical interface. The highly conductive and conformable structure eliminates the necessity of conductive gel while establishing a sufficiently low electrode-skin impedance for high-fidelity electrophysiological sensing. The introduction of gas-permeable structures via a simple and scalable method based on sacrificial templates improves breathability and skin compatibility for applications requiring long-term skin contact. Such conformable and breathable dry electrodes allow for efficient and unobtrusive monitoring of heart, muscle, and brain activities. In addition, based on the muscle activities captured by the electrodes and a musculoskeletal model, electromyogram-based neural-machine interfaces were realized, illustrating the great potential for prosthesis control, neurorehabilitation, and virtual reality.

Self-powered and multi-mode flexible sensing film with patterned conductive network for wireless monitoring in healthcare

The flexible and multi-functional strain sensors have attracted extensive research attention due to their promising applications in various wearable electronics. However, it remains a huge challenge to design and fabricate the self-powered flexible sensing micro-system based on a single multifunctional material to relieve the dependence on external rigid and heavy battery. Herein, a self-powered and multi-mode flexible sensing system integrated with triboelectric nanogenerator (TENG), flexible solid-state supercapacitors (FSSC) and strain sensor is developed based on thermoplastic polyurethane film with asymmetric and cross conductive networks through a facile screen printing process. The unique conductive network endows the prepared strain sensor with an independent anisotropic response to the strain applied in different directions. The TENG in single-electrode mode is presented to generate electricity from ubiquitously bio-mechanical energy and stored it in the FSSC immediately, realizing continuous green power supply for the sensor. As proof-of-concept, an all-in-one smart device consisting of energy supply and sensing module is constructed, demonstrating the great convenience and feasibility for practical application in sustainable wearable electronics. Benefiting from the excellent comprehensive sensing performance, the wireless wearable device assembled by the prepared sensor exhibits excellent detection and recognition for various human motion, expressions, phonation, and physiological signal, revealing the broad potential applications in rehabilitation training, human-machine interaction and medical diagnosis. This work proposes an innovative and scalable approach to manufacture multifunctional micro-systems, which will bring new vitality and more possibilities to a new generation of wearable electronics.

Self-powered triboelectric-mechanoluminescent electronic skin for detecting and differentiating multiple mechanical stimuli

Electronic skin (e-skin) with the ability to sense and differentiate multiple stimuli is crucial for the development of intelligent robots and personalized medical monitoring. Here, a self-powered triboelectric-mechanoluminescent electronic skin (STMES) with the ability to discriminate multiple stimuli is proposed by introducing a strain-sensitive mechanoluminescent spacer layer in a contact-separation mode triboelectric nanogenerator (TENG). The STMES is sensitive to normal force and with a maximum sensitivity of 2 V/N when served as a contact-separation mode TENG, while the mechanoluminescent spacer exhibits optical strain response up to 35%. Both electrical and optical signals are driven by mechanical stimuli only (without the need for external power). In addition, with different signal response patterns to multiple mechanical stimuli, the device can differentiate among mechanical stimuli including pressing, bending, and stretching. Furthermore, continuous tracking of finger movements and external mechanic stimuli is achieved by attaching the STMES to the finger. And a 4 × 4 sensor array demonstrates the capability of the STMES in spatial position detection. Featuring with these characteristics, the STMES may have great potential for human-machine interaction, wearable electronics, and robotic manipulation applications.

Electroassisted Core-Spun Triboelectric Nanogenerator Fabrics for IntelliSense and Artificial Intelligence Perception.

IntelliSense fabrics that can sense transient mechanical stimuli are widely anticipated in flexible and wearable electronics. However, most IntelliSense fabrics developed so far are only sensitive to quasi-static forces, such as stretching, bending, or twisting. In this work, a sheath-core triboelectric nanogenerator (SC-TENG) yarn was developed via a rational design, electroassisted core spinning technique, that consisted of a rough nanoscale dielectric surface and mechanically strong and electrically conductive core yarns. The resulting system was used to sense and distinguish the instantaneous mechanical stimuli generated by different materials. To further improve the sensing accuracy, a machine learning model, based on a classification coding and recurrent neural network, was built to predict the type of contact materials from the peak profiles of output voltages. With these experimental and algorithmic optimizations, we finally used SC-TENG yarn to identify the type of materials in real-time. Moreover, by applying Internet of Things techniques, we investigated that SC-TENG yarn could be integrated into an IntelliSense system to recognize and control various electronic and electrical systems, demonstrating promising applications in wearable energy supply, IntelliSense fabrics, and human-machine interactions.

A super-flexible and transparent wood film/silver nanowire electrode for optical and capacitive dual-mode sensing wood-based electronic skin

A Super-flexible and transparent wood film/silver nanowire electrode for optical and capacitive dual-mode sensing wood-based electronic skin. This work opens new directions for directly incorporating natural wood into e-skin sensors and may offer potential applications in wearable electronics, artificial intelligence, soft robots and so on. • A wood-based e-skin with instrument- and naked eye-readable characteristics. • A pyramidal microstructure polydimethlysiloxane film was used as a sensing layer. • A super-flexible transparent wood film was used as the electrodes. • The e-skin can map the spatial distribution of static and dynamic pressures. • The e-skin in luminescence exhibited a maximum sensitivity value at 1.28 kPa −1 . Flexible electronic skin (e-skin) with multimode broad-range pressure sensing and high sensitivity is highly desirable for robotics, artificial intelligence and human–machine interaction applications. Most research on e-skin has focused on improving the sensitivity of sensing layers interfaced with an electronic output device, with only a limited number of studies focused on user interfaces based on a human-readable output. Moreover, naturally degradable materials are receiving increasing attention for e-skin. Herein, inspired by a bioluminescent noctiluca, a wood-based e-skin with instrument- and naked eye-readable characteristics was sandwich assembled using silver nanowires and a super-flexible transparent wood (STW) film as an electrode and a pyramidal microstructure polydimethlysiloxane film as a stimuli-responsive layer. The electrode was prepared by depositing silver nanowires on the surface of the STW film through a Meyer rod coating method. The film demonstrated outstanding performance with high transmittance (91.4%), a small curvature radius (2 mm) and excellent sheet resistance stability even after greater than 1500 bending cycles. The e-skin showed notable changes in electroluminescence from 0 to 150 kPa (where humans feel pain) and the sensitivity remains larger than 0.15 kPa −1 over the broad pressure range. The sensitivity in capacitance exhibited high sensitivity at 1.01 kPa −1 in the low-pressure regime, meanwhile, the luminescence exhibited a maximum value at 1.28 kPa −1 at medium pressure. Furthermore, the e-skin accurately mapped the spatial distribution of static and dynamic pressures through optical sensing. This work not only improves the sustainable development of materials in electrodes but also illustrates the potential of utilizing natural wood in high value-added e-skin.