Applications In Human-machine Interaction Research Articles

Enhancing Sensing Performance of Capacitive Sensors Using Kirigami Structures.

Capacitive sensors have widespread applications in human-machine interaction, Internet of Things, and smart home systems due to their low cost, high sensitivity, and ease of integration. However, improving the sensitivity and sensing distance of capacitive sensors remains a challenging issue. This study proposes a novel capacitive sensor design method based on Kirigami structures, which enhances sensor performance by introducing specific cutting patterns into the conductive layer to leverage edge effects. Through experimental testing and statistical analysis, we systematically investigated the influence of Kirigami geometric parameters on sensor sensitivity and sensing distance. We designed and fabricated 12 different Kirigami structures, including circular flower patterns, array patterns, layered pointed flower patterns, and circular strip structures, and compared them with traditional non-cut structures. The results show that Kirigami structures significantly improved sensor performance. Compared to traditional sensors without Kirigami structures, optimally designed Kirigami capacitive sensors demonstrated approximately a 3-fold increase in sensitivity and up to 170 percent extension in sensing distance. Multivariate regression analysis and nonlinear models revealed complex relationships between Kirigami structural parameters and sensor performance. Notably, the circular strip (three-layer) structure exhibited the best performance, possibly due to its maximization of edge effects and optimization of electric field distribution. This study provides new design insights for developing high-performance capacitive sensors, with potential applications in improving smart home systems and indoor activity monitoring for solitary elderly individuals.

Oriented Alginate-Poly(vinyl alcohol) Electrospun Nanofibers for Multimodal Sensing and Gesture Language Recognition.

Flexible nanofiber sensors have gained substantial attention in extending application scenarios owing to their desirable lightweight, comfort, and breathability. Nevertheless, disorder and uneven dimension issues of nanofibers are the leading concerns in their multifunctional response, which often leads to erratic response signals as well as poor linearity. In this work, a high-performance oriented nanofiber film with a three-dimensional network consisting of alginate sodium, poly(vinyl alcohol), and poly(ethylene oxide) was successfully fabricated by a controllable directional electrospinning technique. The main properties of the nanofibers are capable of being regulated intentionally by varying the electrospinning temperature, collector rotation speed, and polymer concentrations. Based on the favorable structure orientation, the nanofiber film displays satisfied biodegradability and high mechanical strength (575.1 MPa). Being integrated with modified magnetic particles, the sensors not only display a fast response speed, high magnetic sensitivity, and exceptional recoverability in response to magnetic fields but also show favorable sensitivities and reliable long-term durability under mechanical excitations. As a wearable sensor, it can accurately perceive the physiological signals generated by the human body in real-time. Furthermore, with the assistance of a convolutional neural network model, a gesture language recognition system is developed by integrating multiple sensors to realize a high recognition accuracy (∼99.08%). This study provides a feasible strategy to manufacture high-performance multimodal sensors for wearable human-machine interaction applications.

Scalable Production of Functional Fibers with Nanoscale Features for Smart Textiles.

Functional fibers, retaining nanoscale characteristics or nanomaterial properties, represent a significant advance in nanotechnology. Notably, the combination of scalable manufacturing with cutting-edge nanotechnology further expands their utility across numerous disciplines. Manufacturing kilometer-scale functional fibers with nanoscale properties are critical to the evolution of smart textiles, wearable electronics, and beyond. This review discusses their design principles, manufacturing technologies, and key advancements in the mass production of such fibers. In addition, it summarizes the current applications and state of progress in scalable fiber technologies and provides guidance for future advances in multifunctional smart textiles, by highlighting the upcoming impending demands for evolving nanotechnology. Challenges and directions requiring sustained effort are also discussed, including material selection, device design, large-scale manufacturing, and multifunctional integration. With advances in functional fibers and nanotechnology in large-scale production, wearable electronics, and smart textiles could potentially enhance human-machine interaction and healthcare applications.

Asymmetric Wettability Hydrogel Surfaces for Enduring Electromyographic Monitoring.

Hydrogel electrode interfaces have shown tremendous promise in the acquisition of surface electromyography (EMG) signals. However, the perspiration or moisture environments will trigger the deadhesion between hydrogel electrodes and human skin. Despite the hydrophobic/hydrophilic surfaces can perform the anti-moisture or adhesion respectively, it remains a challenge to integrally form a Janus hydrogel with homogeneous mechanical elasticity and electronic performance. Herein, a surface induction strategy is proposed to approach the hydrophobic/hydrophilic hydrogel surfaces. The hydrophobic interaction between surfactants and molds regulates the distribution of hydrophobic/hydrophilic monomers on the surface. The hydrophobic molds induce a hydrophilic hydrogel surface, while the hydrophilic molds induce a hydrophobic surface. It presents a new phenomenon of reversal wettability inducing and optional hydrogel surfaces. The integral Janus hydrogel can be easily obtained by the hydrophilic molds. Balance of adhesion, elasticity, and conductivity endows the hydrogel electrode patch with durable conformal adhesion and high-fidelity EMG signals even in the sweaty epidermis due to the asymmetric wettability surfaces. This hydrogel performs the quantitative description of muscle strength and accurate fatigue assessment. It offers a reliable candidate for future practical applications in continuous digital healthcare and intelligent human-machine interaction, even the Metaverse.

Noncontact Perception of Thin-Film Transistors by the Synergy of Both Capacitive and Electrostatic Induction Mechanisms.

In recent years, the rapid expansion of research and application of the Internet of Things and wearable electronics has prompted the development of a variety of sensors to perceive physical or chemical information from both the human body and the environment, among which the proximity sensor is a kind of noncontact sensor used to detect the approach of a target and thus exhibits promising applications in human-machine interactions. Thin-film transistors are one type of key components in modern electronics and have been further developed as electrostatic-induction-type proximity sensors to perceive the approach of electrically charged objects. However, they are immune to the approach of a zero-potential object. Capacitive-induction-type proximity sensors are capable of detecting the approach of conductive targets while being less sensitive to insulated ones. Integration of both electrostatic and capacitive induction mechanisms into one proximity sensor is highly expected to broaden its perception to a variety of targets. Here, an interdigital electrode was introduced as an extended gate into an amorphous metal oxide thin-film transistor to construct proximity sensors that combine both electrostatic and capacitive induction mechanisms and therefore can sensitively perceive the approach of a variety of objects that were electrically charged, grounded, or floated. Besides proximity sensing, remote velocity measurement and positioning of an invasive object were also realized, which further extended its functions as a kind of interdigital-electrode gate transistor.

Intelligent Song Recognition via a Hollow-Microstructure-Based, Ultrasensitive Artificial Eardrum.

Artificial ears with intelligence, which can sensitively detect sound-a variant of pressure-and generate consciousness and logical decision-making abilities, hold great promise to transform life. However, despite the emerging flexible sensors for sound detection, most success is limited to very simple phonemes, such as a couple of letters or words, probably due to the lack of device sensitivity and capability. Herein, the construction of ultrasensitive artificial eardrums enabling intelligent song recognition is reported. This strategy employs novel geometric engineering of sensing units in the soft microstructure array (to significantly reduce effective modulus) along with complex song recognition exploration leveraging machine learning algorithms. Unprecedented pressure sensitivity (6.9 × 103 kPa-1) is demonstrated in a sensor with a hollow pyramid architecture with porous slants. The integrated device exhibits unparalleled (exceeding by 1-2 orders of magnitude compared with reported benchmark samples) sound detection sensitivity, and can accurately identify 100% (for training set) and 97.7% (for test set) of a database of the segments from 77 songs varying in language, style, and singer. Overall, the results highlight the outstanding performance of the hollow-microstructure-based sensor, indicating its potential applications in human-machine interaction and wearable acoustical technologies.

Recent Advances in Wearable Textile-Based Triboelectric Nanogenerators.

We review recent results on textile triboelectric nanogenerators (T-TENGs), which function both as harvesters of mechanical energy and self-powered motion sensors. T-TENGs can be flexible, breathable, and lightweight. With a combination of traditional and novel manufacturing methods, including nanofibers, T-TENGs can deliver promising power output. We review the evolution of T-TENG device structures based on various textile material configurations and fabrication methods, along with demonstrations of self-powered systems. We also provide a detailed analysis of different textile materials and approaches used to enhance output. Additionally, we discuss integration capabilities with supercapacitors and potential applications across various fields such as health monitoring, human activity monitoring, human-machine interaction applications, etc. This review concludes by addressing the challenges and key research questions that remain for developing viable T-TENG technology.

Core-shell porous LM/TPU fibers with tunable conductive properties for use as strain and pressure sensors

Flexible wearable sensors have attracted widespread attention in health monitoring, human-machine interaction, and biomedical applications. However, developing a flexible sensor that possesses high sensitivity, wide detection range, and matches the conductive material with the modulus of elasticity remains challenging. Here, we developed a coaxial wet spinning process to fabricate conductive fibers with a core-multi-hollow-shell structure, termed LHPTF. The shell comprises a hollow porous structure of TPU, while the core consists of gallium-based LM with excellent electrical conductivity. LHPTF fibers exhibit electrical conductivity of 8690 S cm−1, high flexibility, appropriate strength, and high elongation at break. The hollow porous structure of TPU fibers can be adjusted with various hollow diameters, thereby enabling the switching between stable conduction and strain sensing of conductive fibers. Due to the protection provided by TPU, LHPTF fibers exhibit good environmental durability and stability. We also demonstrate the application of these fibers in wearable sensors and stable conductor.

A novel design method of a nonlinear elastic mechanism for series elastic actuators

Series elastic actuators with increasing nonlinear stiffness(NlSEA) have promising applications in human-machine interactions for the existence of a nonlinear elastic mechanism that supplies inherent safety and can implement high torque resolution under low load and quick dynamic response under high load. In this study, a novel design method for a nonlinear elastic mechanism that can be applied in NlSEA is proposed. By combining the S-shaped cantilever beam and specialized cam surface, this study proposed an analytical approach to design a nonlinear elastic mechanism satisfying a given stiffening torque-deformation relationship. The geometric parameters constraints were discussed to support parameter determination. Two kinds of cases of nonlinear elastic mechanisms with increasing and constant stiffness characteristics were conducted to verify the effectiveness and generalizability of the proposed design method by simulations. Moreover, a nonlinear elastic mechanism with an increasing stiffness characteristic was designed and a prototype was fabricated. A torque-deformation verification experiment, regulation response experiments, and sinusoidal tracking experiments were conducted to verify the accuracy, response performance, and tracking performance of the prototype. The designed nonlinear elastic mechanism has a large torque-to-mass ratio in a compact and lightweight structure.

An MXene/cellulose nanofiber-based multi-parametric tactile sensor for object material identification

Abstract We propose a novel multi-parametric tactile sensor that can realize force and slip sensing during the contact between a manipulator and a target. The pressure-sensitive unit of this sensor is prepared by homemade MXene and cellulose nanofiber (CNF) composite conductive film, which has high sensitivity of pressure-sensitive response. The triboelectric unit is made of a chemically stable PTFE film with high electron-acquiring ability as the triboelectric sensing layer together with metal electrodes. Experiments have demonstrated that the prepared sensors have high sensitivity, fast response, and excellent durability and stability over 5000 cycles. Combined with the established machine learning algorithm model, the pressure and triboelectric signals of this multi-parametric tactile sensor were used to recognize 15 types of materials with similar characteristics and with 94.33% identification accuracy. This work shows the great potential of multi-parametric tactile sensors for applications in item sorting, human-machine interaction, and other fields.

Advancements in Ti3C2Tx MXene Stability: Synergistic Antioxidant Strategies and Their Impact on Long-Lasting Flexible Sensors.

Two-dimensional (2D) transition metal carbides (Ti3C2Tx MXene) have demonstrated substantial application potential across various fields, owing to their excellent metallic conductivity and solution processability. However, the rapid oxidation of Ti3C2Tx in aqueous environments, leading to a loss of stability within mere days, poses a significant obstacle for its practical applications. Herein, we introduce an antioxidant strategy that combines free radical scavenging with surface passivation, culminating in the design and synthesis of imidazolium-based ionic liquids (ILs) incorporating siloxane groups. By deploying a straightforward hydrolysis-addition reaction, we successfully fabricated IL-modified Ti3C2Tx materials (Ti3C2Tx-IL). The Ti3C2Tx -IL not only displayed exceptional conductivity exceeding 3.85 × 104 S/m and hydrophilic contact angles below 45° but also showcased its superior chemical stability and antioxidation mechanisms through various analyses, including visual color change experiments, spectroscopic and energy spectrum characterization, free radical scavenging tests, and density-functional-theory-based molecular simulations. Furthermore, when utilized as a conductive filler in the fabrication of a poly(vinyl alcohol)/nanocellulose fiber (PVA/CNF) composite hydrogel (PCMIL), the resultant sensors exhibited remarkable mechanical performance with up to 535% strain, 1.59 MPa strength, 4.35 MJ/m3 toughness, and a conductivity of 3.40 mS/cm, as well as a high sensitivity gauge factor of 3.3. Importantly, even after 45 days of storage, the PCMIL retained most of its functionalities, demonstrating superior performance in human-machine interaction applications compared to hydrogels made from unmodified Ti3C2Tx. This research establishes a robust antioxidant protection strategy for Ti3C2Tx, offering substantial technical reinforcement for its prospective applications in the realm of flexible electronics and sensing technologies.

Flexible and washable MXene@PEDOT:PSS@CS@PU pressure sensors

Flexible electronics have attracted great interest because of their potential applications in healthcare monitoring and human-computer interaction. The performance of flexible electronics depends on the design of structure, materials, and mechanical responses. In this work, we explore the use of polyurethane (PU) sponge as the scaffold to form flexible piezoresistive pressure sensors with conductive networks made from conductive polymer PEDOT:PSS and MXene nanosheets. The sensitivity of the prepared pressure sensors reaches 0.2171 kPa−1 under mechanical pressure in a range of 1.8–6.15 kPa. The prepared pressure sensors are re-usable after washing. The applications of the prepared pressure sensors in the sensing of physiological behavior are demonstrated under the action of finger tapping, finger bending, vocal cord vibration, swallowing, and pulsing of radial artery. The prepared pressure sensors have great prospects for applications in human-machine interaction and physiological diagnosis.

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

Objective.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.Approach.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.Main results.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.Significance.This work provides valuable insights into the future design and evaluation of myoelectric control systems for emerging HMI applications.

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 346 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.

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.

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 0.3–303.03 %. 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.

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.

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.