Applications In Human-machine Interaction Research Articles

An afferent nerve-like electronic device with somatic mechanical perception and sensation management

Tactile and pain perception are essential for biological skin to interact with the external environment. This complex interplay of sensations allows for the detection of potential threats and appropriate responses to stimuli. However, the challenge is to enable flexible electronics to respond to mechanical stimuli such as biological skin, and researchers have not clearly reported the successful integration of somatic mechanical perception and sensation management functions into neuro-like electronics. In this work, an afferent nerve-like device with a pressure sensor and a perception management module is proposed. The pressure sensor comprises two conductive fabric layers and an ionic hydrogel, forming a capacitor structure that emulates the swift transition from tactile to pain perception under mechanical stimulation. Drawing inspiration from the neuronal “gate control” mechanism, the sensation management module adjusts signals in response to rubbing, accelerating the discharge process and reducing the perception duration, thereby replicating the inhibitory effect of biological neurons on pain following tactile interference. This integrated device, encompassing somatic mechanical perception and sensation management, holds promise for applications in soft robotics, prosthetics, and human-machine interaction.

Comparing online wrist and forearm EMG-based control using a rhythm game-inspired evaluation environment.

The use of electromyogram (EMG) signals recorded from the wrist is emerging as a desirable input modality for human-machine interaction (HMI). Although forearm-based EMG has been used for decades in prosthetics, there has been comparatively little prior work evaluating the performance of wrist-based control, especially in online, user-in-the-loop studies. Furthermore, despite different motivating use cases for wrist-based control, research has mostly adopted legacy prosthesis control evaluation frameworks. Gaining inspiration from rhythm games and the Schmidt's law speed-accuracy tradeoff, this work proposes a new temporally constrained evaluation environment with a linearly increasing difficulty to compare the online usability of wrist and forearm EMG. Compared to the more commonly used Fitts' Law-style testing, the proposed environment may offer different insights for emerging use cases of EMG as it decouples the machine learning algorithm's performance from proportional control, is easily generalizable to different gesture sets, and enables the extraction of a wide set of usability metrics that describe a users ability to successfully accomplish a task at a certain time with different levels of induced stress. The results suggest that wrist EMG-based control is comparable to that of forearm EMG when using traditional prosthesis control gestures and can even be better when using fine finger gestures. Additionally, the results suggest that as the difficulty of the environment increased, the online metrics and their correlation to the offline metrics decreased, highlighting the importance of evaluating myoelectric control in real-time evaluations over a range of difficulties. This work provides valuable insights into the future design and evaluation of myoelectric control systems for emerging HMI applications.

Strain-Temperature Dual Sensor Based on Deep Learning Strategy for Human-Computer Interaction Systems.

Thermoelectric (TE) hydrogels, mimicking human skin, possessing temperature and strain sensing capabilities, are well-suited for human-machine interaction interfaces and wearable devices. In this study, a TE hydrogel with high toughness and temperature responsiveness was created using the Hofmeister effect and TE current effect, achieved through the cross-linking of PVA/PAA/carboxymethyl cellulose triple networks. The Hofmeister effect, facilitated by Na+ and SO42- ions coordination, notably increased the hydrogel's tensile strength (800 kPa). Introduction of Fe2+/Fe3+ as redox pairs conferred a high Seebeck coefficient (2.3 mV K-1), thereby enhancing temperature responsiveness. Using this dual-responsive sensor, successful demonstration of a feedback mechanism combining deep learning with a robotic hand was accomplished (with a recognition accuracy of 95.30%), alongside temperature warnings at various levels. It is expected to replace manual work through the control of the manipulator in some high-temperature and high-risk scenarios, thereby improving the safety factor, underscoring the vast potential of TE hydrogel sensors in motion monitoring and human-machine interaction applications.

Electrostatic Force-Assisted Transfer of Flexible Silicon Photodetector Focal Plane Arrays for Image Sensors.

Flexible photodetectors are pivotal in contemporary optoelectronic technology applications, such as data reception and image sensing, yet their performance and yield are often hindered by the challenge of heterogeneous integration between photoactive materials and flexible substrates. Here, we showcase the potential of an electrostatic force-assisted transfer printing technique for integrating Si PIN photodiodes onto flexible substrates. This clean and dry process eliminates the need for chemical etchants, making it a highly desirable method for manufacturing high-performance flexible photodetector arrays, expanding their widespread applications in electronic eyes, robotics, and human-machine interaction. As a demonstration, a 5 × 5 flexible Si photodetector focal plane array is constructed for imaging sensors and shaped into a convex semicylindrical form to achieve a π field of view with long-term mechanical and thermal stability. Such an approach provides a high yield rate and consistent performance, with the single photodetector demonstrating exceptional characteristics, including a responsivity of 0.61 A/W, a response speed of 39.77 MHz, a linear dynamic range of 108.53 dB, and a specific detectivity of 2.75 × 1012 Jones at an applied voltage of -3 V at 940 nm.

Development of high sensitivity composite sensors for proprioceptive applications

This study describes the fabrication of extremely sensitive piezoresistive composite sensors designed to detect human motion and speech. Starting out, a solution of thermoplastic polyurethane (TPU) was formed in dimethyl formamide (DMF) with a concentration of 35 % weight to volume. The solution was subsequently spun using a custom-built centrifugal spinning setup to produce TPU fibers. Following their fabrication through spinning, TPU fibers were immersed in a solution of carbon nanoparticles (CNPs) dispersed in tetra hydrofuran (THF), having a concentration of 25 %w/v, for dip coating TPU fibers with CNPs. This resulted in the formation of highly piezoresistive fibers having strain sensing capability. These fibers were then spun into the form of a yarn and tested as a strain sensor for proprioceptive applications. The composite sensors exhibited exceptional repeatability in tests involving continuous stretching and relaxing for more than 5000 cycles. The composite strain sensor demonstrated remarkable extensibility as well. The composite strain sensor was attached to different body parts such as the elbow, knees, fingers, and ankles to detect and track motion. It was found that the sensor could measure and track the angle, position, and frequency of motion in all of these scenarios. The sensor's remarkable sensitivity allowed it to detect different spoken words and letters, in addition to recognizing the action of swallowing in humans. The results show that the newly developed composite strain sensors are suitable for proprioceptive and speech recognition applications in soft robotics, wearable devices, and human-machine interactions.

Self-powered and degradable humidity sensors based on silk nanofibers and its wearable and human–machine interaction applications

Humidity sensors have received increasing attention due to their broad potential application in wearable devices, health monitoring, intelligent control, and other fields. However, traditional humidity sensors are prone to produce various toxic substances to pollute the environment after discarding. Also, they are prone to damage during use, which shortens their service life. Therefore, the development of eco-friendly, degradable, and repairable humidity sensors is of great significance for reducing e-waste and protecting the environment. Here, we started with the selection of materials by using silk nanofiber (SNF) film as the substrate material, copper/aluminum as electrodes, and calcium chloride as the electrolyte, combined with the working mechanism of the primary battery, to construct a self-powered humidity sensor. The humidity sensor exhibits excellent humidity sensing performance and good linearity within the range of 30–90 % relative humidity (RH). A single humidity sensor can output 360 mV at 90 % RH. After being damaged, the humidity sensor can be repaired with the help of water. When the ruptured SNF film encounters water molecules, the water molecules can prompt the hydrogen bonds between the SNFs to rearrange and connect, connecting the broken area. Young’s modulus retention rate of the repaired SNF film is over 85 %. The repaired humidity sensor can also output 328 mV. Additionally, an alkaline solution can completely degrade the humidity sensor in less than 80 min. The proposed humidity sensors have been applied to wearable self-powered wristbands, wireless monitoring systems, and non-contact human–machine interaction systems. Hence, we provide comprehensive research from the material level (SNFs) to the functional device level (the self-powered humidity sensor), and then to the system level (wearable and human–machine interface system). The research will open a new way for the design of the self-powered humidity sensor and have great potential applications in wearable electronics and human–machine interface applications.

Sensitive strain sensors with wide-range detection and high stability for intelligent human motion monitoring

Miniaturized wearable electronic devices with strain sensors as core sensing elements hold significant applications in health monitoring and human-machine interaction. However, it remains a challenge to develop strain sensors with optimal response linearity, a wide detection range, and stability. In this study, to satisfy the above requirements, we construct high-performance wearable strain sensors (rGO-CNTOH@PSE) by a strategy of hybridizing conductive fillers with stretchable substrates. In detail, a synergistic conductive network is developed by adopting OH-containing carbon nanotube (CNTOH) and reduced graphene oxide (rGO) fillers with an appropriate mass ratio. After the network is integrated into a porous silicone elastomer substrate formed by the salt templating method, the developed strain sensor exhibits distinct resolution, consistent repeatability, and superior response linearity across a wide strain detection range of 0.3% to 303.03%. Furthermore, the sensor also maintains good response stability over 15000 cycles of strain. We also demonstrate that this strain sensor showing outstanding exceptional detection performance has the capability for full-range human motion detection. A wireless smart electronic glove is developed based on the sensor, which can accurately recognize gesture movements and control a bionic robotic hand to replicate human hand actions, offering the potential for replacing manual tasks (such as, grasping in hazardous environments) in the future.

Morse Code Recognition Based on a Flexible Tactile Sensor with Carbon Nanotube/Polyurethane Sponge Material by the Long Short-Term Memory Model.

Morse code recognition plays a very important role in the application of human-machine interaction. In this paper, based on the carbon nanotube (CNT) and polyurethane sponge (PUS) composite material, a flexible tactile CNT/PUS sensor with great piezoresistive characteristic is developed for detecting Morse code precisely. Thirty-six types of Morse code, including 26 letters (A-Z) and 10 numbers (0-9), are applied to the sensor. Each Morse code was repeated 60 times, and 2160 (36 × 60) groups of voltage time-sequential signals were collected to construct the dataset. Then, smoothing and normalization methods are used to preprocess and optimize the raw data. Based on that, the long short-term memory (LSTM) model with excellent feature extraction and self-adaptive ability is constructed to precisely recognize different types of Morse code detected by the sensor. The recognition accuracies of the 10-number Morse code, the 26-letter Morse code, and the whole 36-type Morse code are 99.17%, 95.37%, and 93.98%, respectively. Meanwhile, the Gated Recurrent Unit (GRU), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF) models are built to distinguish the 36-type Morse code (letters of A-Z and numbers of 0-9) based on the same dataset and achieve the accuracies of 91.37%, 88.88%, 87.04%, and 90.97%, respectively, which are all lower than the accuracy of 93.98% based on the LSTM model. All the experimental results show that the CNT/PUS sensor can detect the Morse code's tactile feature precisely, and the LSTM model has a very efficient property in recognizing Morse code detected by the CNT/PUS sensor.

Facile Electret-Based Self-Powered Soft Sensor for Noncontact Positioning and Information Translation.

Noncontact sensors have demonstrated significant potential in human-machine interactions (HMIs) in terms of hygiene and less wear and tear. The development of soft, stable, and simply structured noncontact sensors is highly desired for their practical applications in HMIs. This work reports on electret-based self-powered noncontact sensors that are soft, transparent, stable, and easy to manufacture. The sensors contain a three-layer structure with a thickness of 0.34 mm that is fabricated by simply stacking a polymeric electret layer, an electrode layer, and a substrate layer together. The fabricated sensors show high charge-retention capability, keeping over 98% of the initial surface potential even after 90 h, and can accurately and repeatedly sense external approaching objects with impressive durability. The intensity of the detected signal shows a strong dependence on the distance between the object and the sensor, capable of sensing a distance as small as 2 mm. Furthermore, the sensors can report stable signals in response to external objects over 3000 cycles. By virtue of the signal dependence on distance, an intelligent noncontact positioning system is developed that can precisely detect the location of an approaching object. Finally, by integrating with eyeglasses, the transparent sensor successfully captures the movements of blinks for information translation. This work may contribute to the development of stable and easily manufactured noncontact soft sensors for HMI applications, for instance, assisting with communication for locked-in syndrome patients.

Amharic spoken digits recognition using convolutional neural network

Spoken digits recognition (SDR) is a type of supervised automatic speech recognition, which is required in various human–machine interaction applications. It is utilized in phone-based services like dialing systems, certain bank operations, airline reservation systems, and price extraction. However, the design of SDR is a challenging task that requires the development of labeled audio data, the proper choice of feature extraction method, and the development of the best performing model. Even if several works have been done for various languages, such as English, Arabic, Urdu, etc., there is no developed Amharic spoken digits dataset (AmSDD) to build Amharic spoken digits recognition (AmSDR) model for the Amharic language, which is the official working language of the government of Ethiopia. Therefore, in this study, we developed a new AmSDD that contains 12,000 utterances of 0 (Zaero) to 9 (zet’enyi) digits which were recorded from 120 volunteer speakers of different age groups, genders, and dialects who repeated each digit ten times. Mel frequency cepstral coefficients (MFCCs) and Mel-Spectrogram feature extraction methods were used to extract trainable features from the speech signal. We conducted different experiments on the development of the AmSDR model using the AmSDD and classical supervised learning algorithms such as Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF) as the baseline. To further improve the performance recognition of AmSDR, we propose a three layers Convolutional Neural Network (CNN) architecture with Batch normalization. The results of our experiments show that the proposed CNN model outperforms the baseline algorithms and scores an accuracy of 99% and 98% using MFCCs and Mel-Spectrogram features, respectively.

Ultrasensitive Biomimetic Skin with Multimodal and Photoelectric Dual-Signal Sensing.

Mimicking biological skin enabling direct, intelligent interaction between users and devices, multimodal sensing with optical/electrical (OE) output signals is urgently required. Owing to this, this work aims to logically design a stretchable OE biomimetic skin (OE skin), which can sensitively sense complex external stimuli of pressure, strain, temperature, and localization. The OE skin consists of elastic thin polymer-stabilized cholesteric liquid crystal films, an ion-conductive hydrogel layer, and an elastic protective membrane formed with thin polydimethylsiloxane. The as-designed OE skin exhibits customizable structural color on demand, good thermochromism, and excellent mechanochromism, with the ability to extend the full visible spectrum, a good linearity of over 0.99, fast response speed of 93 ms, and wide temperature range of 119 °C. In addition, the conduction resistance variation of ion-conductive hydrogel exhibits excellent sensing capabilities under pressure, stretch, and temperature, endowing a good linearity of 0.99998 (stretching from 0 to 150%) and high thermal sensitivity of 0.86% per °C. Such an outstanding OE skin provides design concepts for the development of multifunctional biomimetic skin used in human-machine interaction and can find wide applications in intelligent wearable devices and human-machine interactions.

Antifreeze protein-based ultrastretchable and highly sensitive hydrogel for human-machine interaction

Intelligent and convenient human–computer interfaces realize efficient and close cooperation between people and the digital world. As windows of human–machine interaction (HMI), sensors directly determine the ability and quality of acquired signals and the utilization potential of HMI systems. However, it still remains challenging to develop antifreeze hydrogel sensors with freeze-resistant properties while avoiding the loss of other properties as much as possible to achieve stable HMI applications in low-temperature environments. Here, we present an Agar/PAAm dual-network hydrogel (Na+-Agar/PAAm-AFP) doped with both antifreeze protein (AFP) and sodium chloride (NaCl), which exhibits excellent antifreeze capability (−20 °C). The hydrogel integrates transparency (>90%), ultra stretchability (up to 2100%), good adhesion properties to metal, glass, and plastic, high electrical conductivity (3.41 S m−1), high sensitivity (GF = 10.23), and non-irritating to the skin. The hydrogel can be used for monitoring daily activities and glove-based HMI systems for intelligent control. In addition, based on this hydrogel, we have successfully developed an interactive system for real-time information transfer over long distances. It is expected to provide better health management monitoring in several fields and also help to advance the development of innovative and IoT technologies.

Kirigami-Shaped Dual-Functional Strain and Temperature Sensors for Monitoring Body Movements and Hyperthermia Toward Physiotherapy Applications

Multifunctional sensors find widespread applications in wearable healthcare, Internet-of-Things, environmental monitoring, and human-machine interactions, yet they suffer from reduced stretchability, durability, wearability, and enhanced signal crosstalk. This work presents a new kirigami-shaped dual-functional patch that integrates both strain and temperature sensors in a single platform. The kirigami architecture allows out-of-plane deformation of the sensors enabling them to conform to different body parts. The sensing regions are functionalized with gold nanoparticle-decorated multi-walled carbon nanotubes that provide highly sensitive and repeatable performance. The strain sensor has a gauge factor of around 220 at 0.12% bending strain and the temperature sensor shows a sensitivity of 0.0278 kΩ °C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> . Additionally, the sensor demonstrates an ultra-high stretchability for an applied tensile strain of up to 180%. The sensors can precisely monitor the dynamic bending of different joints in the body and temperature as well as decouple the strain and temperature measurements in response to dual stimuli. Furthermore, the sensors are interfaced with an Internet-of-Things platform that allows wireless transmission of body motion and temperature measurements to the cloud. The versatile nature of the sensing patch holds great promise for continuous monitoring of the progress in physiotherapy so that recovery can be tracked in real-time and appropriate intervention measures can be applied.

Wafer-Recyclable, Eco-Friendly, and Multiscale Dry Transfer Printing by Transferable Photoresist for Flexible Epidermal Electronics.

Flexible electronics have been of great interest in the past few decades for their wide-ranging applications in health monitoring, human-machine interaction, artificial intelligence, and biomedical engineering. Currently, transfer printing is a popular technology for flexible electronics manufacturing. However, typical sacrificial intermediate layer-based transfer printing through chemical reactions results in a series of challenges, such as time consumption and interface incompatibility. In this paper, we have developed a time-saving, wafer-recyclable, eco-friendly, and multiscale transfer printing method by using a stable transferable photoresist. Demonstration of photoresist with various, high-resolution, and multiscale patterns from the donor substrate of silicon wafer to different flexible polymer substrates without any damage is conducted using the as-developed dry transfer printing process. Notably, by utilizing the photoresist patterns as conformal masks and combining them with physical vapor deposition and dry lift-off processes, we have achieved in situ fabrication of metal patterns on flexible substrates. Furthermore, a mechanical experiment has been conducted to demonstrate the mechanism of photoresist transfer printing and dry lift-off processes. Finally, we demonstrated the application of in situ fabricated electrode devices for collecting electromyography and electrocardiogram signals. Compared to commercially available hydrogel electrodes, our electrodes exhibited higher sensitivity, greater stability, and the ability to achieve long-term health monitoring.

Robust Fiber-Shaped Flexible Temperature Sensors for Safety Monitoring with Ultrahigh Sensitivity.

Flexible temperature sensors capable of detecting and transmitting temperature data from the human body, environment, and electronic devices hold significant potential for applications in electronic skins, human-machine interactions, and disaster prevention systems. Nonetheless, fabricating flexible temperature sensors with exceptional sensing performance remains a formidable task, primarily due to the intricate process of constructing an intrinsically flexible sensing element with high sensitivity. In this study, a facile in situ two-step synthetic method is introduced for fabricating flexible fiber-shaped NiO/carbon nanotube fiber (CNTF) composites. The resulting NiO/CNTF flexible temperature sensors demonstrate outstanding deformability and temperature sensing characteristics, encompassing a broad working range (-15 to 60°C) and high sensitivity (maximum TCR of -20.2%°C-1 and B value of 3332 K). Importantly, the mechanical and thermal behaviors of the sensor in various application conditions are thoroughly examined using finite element analysis simulations. Moreover, the temperature sensors can effectively capture diverse thermal signals in wearable applications. Notably, a temperature monitoring and warning system is developed to prevent fire accidents resulting from abnormal thermal runaway in electronic devices.

Flexible capacitive pressure sensors and their applications in human-machine interactions

Flexible pressure sensors offer more significant advantages than rigid sensors in various applications, including human-computer interaction, medical health, and robot touch. However, this also places more stringent demands on the materials used. For example, the materials must be thin and soft enough to fit onto the surface of the human skin or be implanted into the body while also exhibiting good biocompatibility and matching the mechanical properties of biological tissues. Regarding device performance, flexible pressure sensor design primarily focuses on enhancing sensitivity, response time, detection limit, and stability. Researchers have recently been expanding these sensors' pressure response range, pressure resolution, spatial resolution, and tensile performance, which will further broaden their potential applications. This review provides an overview of the recent classification of flexible pressure sensors, covering their sensing principle, sensing performance, and future application prospects.

Graphene-based flexible temperature/pressure dual-mode sensor as a finger sleeve for robotic arms

The design of sensors with multiple sensing functions has become a new challenge to meet the measurement of multiple stimulus signals in people's productive lives. In this article, a sandwich type dual-mode flexible sensor is designed to achieve simultaneous detection of pressure and temperature by constructing different sensing model structures. The dual-mode flexible sensor essentially comprises graphene, melamine sponge and Ag nanoparticle ink. Graphene is coated on the melamine sponge to form a 3D conductive sponge using the dip coating method, while Ag nanoparticle ink is printed on PET to form a serpentine electrode through inkjet printing technology. The sensor has high sensitivity (1.937 × 10−4 Pa−1, R2 = 0.953), a wide operating range (from 0 to 40 kPa), speedy response time (248 ms) and remarkable stability in pressure measurement. It also shows high linearity (TCR = 1.035 × 10−3 °C−1, R2 = 0.987) from 10 to 60 °C and is pressure insensitive in temperature measurement. Experimental results show that the different sensing mechanisms and structural configurations allow the sensors to detect pressure and temperature signals independently or simultaneously without signal coupling. The fabricated sensor has applications in health monitoring, rehabilitation therapy, human-machine interaction and intelligent prosthetics.

Low voltage-driven, high-performance TiO2 thin film transistors with MHz switching speed.

High-speed circuits based on thin film transistors (TFTs) show promising potential applications in biomedical imaging and human-machine interactions. One of the critical requirements for high-speed electronic devices lies in high-frequency switching or amplification at low voltages, typically driven by batteries (∼3.0 V). To date, however, most electrical performances of metal oxide TFTs are measured under direct current (DC) conditions, and their dynamic switching behaviour is scarcely explored and studied systematically. Here in this work, we present low voltage-driven, high-performance TiO2 thin film transistors, which can be operated at a switching speed of MHz. Our proposed TiO2 TFTs demonstrated a high on-off ratio of 107, together with a subthreshold swing (SS) of ∼150 mV Dec-1 averaged over four orders of magnitude, which can be further reduced below 100 mV Dec-1 when the temperature cools to 77 K. Additionally, the TiO2 TFTs exhibit excellent gate-pulse switching at various frequencies ranging from 1.0 Hz to 1.0 MHz. We also explored the potential application of the TiO2 TFTs as logic gates by constructing a resistive-loaded inverter, which shows stable operation at 10 kHz frequency and various temperatures. Thus, our results show the great potential of TiO2 TFTs as a new platform for high-speed electronic applications.

&lt;i&gt;Hofmeister&lt;/i&gt; effect regulated gel iontronic sensors for wide-range pressure perception

&lt;p&gt;Flexible pressure sensors, vital for medical, human-machine interaction, and intelligent recognition applications due to their high-sensitivity, excellent-linearity, and broad-pressure response, face challenges in achieving a harmonious balance among these attributes. Inspired by the gradient modulus in human skin layers, we proposed a pioneering method to adjust the gradient elastic modulus of amino trimethylene phosphonic acid (ATMP)-assisted polyvinyl alcohol (PVA) hydrogel through the &lt;i&gt;Hofmeister&lt;/i&gt; effect, introducing micro-pyramid electrodes. This innovative approach successfully constructs a bio-gradient gel iontronic sensor (BGGITS) with an ultra-wide-range perception. The BGGITS exhibits a linear high-sensitivity of 700 kPa&lt;sup&gt;-1&lt;/sup&gt; within a broad-pressure detection range of up to 800 kPa. The composite design, integrating gradient gel and microstructure electrodes, demonstrates exceptional pressure resolution and mechanical stability. This biomimetic skin pressure sensor holds promise for achieving linear high-sensitivity across a broader pressure range simultaneously and may find applications in electronic skin for health monitoring and tactile perception in the future.&lt;/p&gt;

Flexible pressure sensors based on reduced graphene oxide @ porous silicone elastomers with good resolution and wide working range

Current flexible piezoresistive pressure sensors still face challenges, such as poor stability and a limited detection range, making it difficult to accurately discern small-scale pressures in the Pa or MPa range when meeting sudden pressure events. To tackle the above issues, this study presents the development of a competent sensor composed of porous conductive elastomer and laser-induced graphene electrodes. The substrate of the composite elastomer is a porous silicone elastomer obtained by the additive reaction using template methods, and the conductive filler consisting of reduced graphene oxide is synthesized through an eco-friendly approach. We demonstrated that the sensors fabricated here have the ability to precisely resolve pressures from 542 Pa to 1.5 MPa with long-term stability, as well as the detection of subtle signals, such as pulse pulsation and throat vibrations. Above all, such sensors have great potential in the application of human-machine interaction.