Human-machine Interaction Research Articles

Biodegradable, stretchable, and high-performance triboelectric nanogenerators through interfacial polarization in bilayer structure

The increasing demand for wearable electronics has led to the development of triboelectric nanogenerators (TENGs) as a promising energy harvesting and sensing technology. However, conventional TENGs often utilize non-biodegradable materials, contributing to environmental pollution. In this work, we present a stretchable and biodegradable TENG based on hydroxyethyl cellulose (HEC) and gelatin (HG-TENG). The HG-TENG features a bilayered structure, where the large difference in their relative permittivity between HEC and gelatin induces interfacial polarization, effectively mitigating charge recombination and enhancing triboelectric performance. The optimized HG-TENG achieves an open-circuit voltage (Voc) of 93 V, a maximum power density of 57.8 µW/cm2, and can power 38 blue light-emitting diodes. The device exhibits a stretchability of 150 % and biodegrades within 3 hours in phosphate-buffered saline. Furthermore, we demonstrate the application of the HG-TENG as a wearable sensor by modifying it with trichloro(1H, 1H, 2H, 2H-perfluorooctyl)silane (FOTS) (FHG-TENG). The FHG-TENG-based smart glove, integrated with machine learning algorithms, enables real-time monitoring of blood pressure waveforms and finger motions, showcasing its potential for human-machine interfaces. The smart glove, equipped with five FHG-TENGs on the proximal interphalangeal joints of each finger, detects diverse finger gestures and generates voltage signals that control a robotic hand in real-time, demonstrating effective human-machine interaction through synchronized motion. Moreover, the smart glove achieves a high recognition accuracy of 96.15 % for 10 different hand sign languages.

Intelligent fault prediction with wavelet-SVM fusion in coal mine

Fault prediction in coal mining is crucial for safety, and recent technological advancements are steering this field towards supervised intelligent interpretation, moving beyond traditional human-machine interaction. Currently, support vector machine (SVM) predictions often rely on seismic attribute data; however, the poor quality of some fault data characteristics hampers their predictive capability. To localize the fault based on original seismic data and improve SVM prediction we propose the W-SVM algorithm, which integrates wavelet transform and SVM. Through wavelet transform, we localize fault features in seismic data, which are then used for SVM prediction. Validation using real data confirms the feasibility of the W-SVM approach. The W-SVM model successfully identifies 34 known faults. Beyond achieving high prediction accuracy, the model exhibits improved stability and generalization. The difference among the evaluation metrics for training, validation, and testing is within 5%. Moreover, this study localizes the response of faults through wavelet transform, simplifies the dataset preparation process, improves computational efficiency, and increases overall applicability. This advancement further promotes the development of intelligent identification of faults in coal mines.

Sodium Alginate/MXene-Based Flexible Humidity Sensors with High-Humidity Durability and Application Potentials in Breath Monitoring and Non-Contact Human-Machine Interfaces.

Flexible humidity sensors (FHSs) with fast response times and durability to high-humidity environments are highly desirable for practical applications. Herein, an FHS based on crosslinked sodium alginate (SA) and MXene was fabricated, which exhibited high sensitivity (impedance varied from 107 to 105 Ω between 10% and 90% RH), good selectivity, prompt response times (response/recover time of 4 s/11 s), high sensing linearity (R2 = 0.992) on a semi-logarithmic scale, relatively small hysteresis (~5% RH), good repeatability, and good resistance to highly humid environments (negligible changes in sensing properties after being placed in 98% RH over 24 h). It is proposed that the formation of the crosslinking structure of SA and the introduction of MXene with good conductivity and a high specific surface area contributed to the high performance of the composite FHS. Moreover, the FHS could promptly differentiate the respiration status, recognize speech, and measure fingertip movement, indicating potential in breath monitoring and non-contact human-machine interactions. This work provides guidance for developing advanced flexible sensors with a wide application scope in wearable electronics.

Physically crosslinked hydrogel based on silver nanowires with a discontinuous rigid framework for robust, sensitive, antibacterial, and biocompatible flexible sensors

Conductive hydrogels as flexible sensors fulfill the essential requirements of realtime monitoring and sensitive transmission in the fields of human-machine interaction. However, it is still a great challenge to integrate satisfying mechanical properties, sensitivity, antibacterial efficacy, and biocompatibility into one hydrogel sensor while ensuring a precise output signal. Herein, ultrathin silver nanowires (AgNWs) were prepared by controlling the growth of Ag atoms in (111) crystal planes within the Ethylene Glycol-Polyvinylpyrrolidone (EG-PVP) reduction system. Then, multifunctional hydrogel (SA-SH-AgNWs/PVA) was developed by physically crosslinking polyvinyl alcohol (PVA) and thiol-modified sodium alginate (SA-SH) in AgNWs aqueous solution through freeze-thaw circulation and Ca2+ crosslink. The AgNWs were adsorbed onto sodium alginate (SA) chains through electrostatic adsorption with thiol groups (-SH), forming a discontinuous rigid framework structure with a 278 % increase in tensile strength. The uniform dispersion of AgNWs within the hydrogel offers good sensing performance: gauge factor (GF) of 2.40 and sensitivity (S) of 3.24 × 10−2 kPa−1, and satisfying antibacterial abilities. What’s more, the obtained hydrogel can serve as stretching or compressing sensors to detect tiny yet intricate changes of human bodies. Therefore, the hydrogel is a great inspiration for the development of portable, intelligent, and highly flexible sensors.

A new sociology of humans and machines.

From fake social media accounts and generative artificial intelligence chatbots to trading algorithms and self-driving vehicles, robots, bots and algorithms are proliferating and permeating our communication channels, social interactions, economic transactions and transportation arteries. Networks of multiple interdependent and interacting humans and intelligent machines constitute complex social systems for which the collective outcomes cannot be deduced from either human or machine behaviour alone. Under this paradigm, we review recent research and identify general dynamics and patterns in situations of competition, coordination, cooperation, contagion and collective decision-making, with context-rich examples from high-frequency trading markets, a social media platform, an open collaboration community and a discussion forum. To ensure more robust and resilient human-machine communities, we require a new sociology of humans and machines. Researchers should study these communities using complex system methods; engineers should explicitly design artificial intelligence for human-machine and machine-machine interactions; and regulators should govern the ecological diversity and social co-development of humans and machines.

Paperless libraries: The trending way to go in the fifth industrial revolution

The idea that spurred the author’s interest in carrying out this study results from understanding a cashless society, where users of banking systems only move around with their ATM cards. With their ATM cards, they can access their money anywhere globally. This approach could resonate in libraries, where we now have a paperless society, having cost-benefit analysis in the era of the fifth Industrial Revolution (5IR). The 5IR has to do with humans interacting between technology or machines either to solve individual and organizational societal problems from a general or specific perspective. The interaction between humans and technology would assist in recognizing the synergistic in addressing the support for task accomplishment. The methodology applied is in two stages, first, a systematic literature review and second, interpretive content analysis of literature harvested from the database of Scopus. Findings revealed that technological advancements have enabled paperless libraries such that on-screen reading is increasingly prevalent, rapid development support through mobile computing, software, and internet/connections/Wi-Fi, multifarious electronic information retrieval among others. Furthermore, other findings were anchored on the relevance of paperless libraries in the 5IR, the functionality of paperless library systems, enhanced accessibility, and inclusivity, collaboration and connectivity, and environmental impact. Meanwhile, certain challenges and mitigation strategies were proffered. The study concludes that as we navigate this transformative era, the emergence of paperless libraries stands as a testament to the harmonious integration of technological prowess and human aspirations. Therefore, envision human-machine interactions be encouraged to bring about paperless libraries, considering the significance of AI and human-centric innovation.

Suggestion of a hybrid computational model to optimize EEG motor imagery signals classification

Brain-computer interfaces (BCIs) have proven to be innovative and promising technologies for human-machine interaction. However, distinguishing with relative precision between EEG motor signals generated by real or imagined movements of the left and right hands has still been a major challenge. By analyzing brain electrical signals and the processes of reading, processing, and interpreting data, the hybrid model suggested in this dissertation contributed to greater effectiveness of the algorithms, with accuracy above that normally found in most models present in the most recent literature. The experimental and observational research carried out in the creation of the methodology applied in this dissertation ensures the replication of automation methods, techniques, and algorithms, machine learning, and more specifically, in data preparation, feature extraction, signal classification, and obtaining results with greater accuracy for the implementation of brain-computer interfaces, BCIs. This dissertation opens new possibilities for future research and deepening of techniques, tools, and new experiments. With a premise to create personalized BCIs and trained specifically for individual pattern recognition, as they have shown greater potential results. Each human being is unique and has singular characteristics that distinguish them from all other beings.

Directional Characteristic Enhancement of an Omnidirectional Detection Sensor Enabled by Strain Partitioning Effects in a Periodic Composite Hole Substrate.

An omnidirectional stretchable strain sensor with high resolution is a critical component for motion detection and human-machine interaction. It is the current dominant solution to integrate several consistent units into the omnidirectional sensor based on a certain geometric structure. However, the excessive similarity in orientation characteristics among sensing units restricts orientation recognition due to their closely matched strain sensitivity. In this study, based on strain partition modulation (SPM), a sensitivity anisotropic amplification strategy is proposed for resistive strain sensors. The stress distribution of a sensitive conductive network is modulated by structural parameters of the customized periodic hole array introduced underneath the elastomer substrate. Meanwhile, the strain isolation structures are designed on both sides of the sensing unit for stress interference immune. The optimized sensors exhibit excellent sensitivity (19 for 0-80%; 109 for 80%-140%; 368 for 140%-200%), with nearly a 7-fold improvement in the 140%-200% interval compared to bare elastomer sensors. More importantly, a sensing array composed of multiple units with different hole configurations can highlight orientation characteristics with amplitude difference between channels reaching up to 29 times. For the 48-class strain-orientation decoupling task, the recognition rate of the sensitivity-differentiated layout sensor with the lightweight deep learning network is as high as 96.01%, superior to that of 85.7% for the sensitivity-consistent layout. Furthermore, the application of the sensor to the fitness field demonstrates an accurate recognition of the wrist flexion direction (98.4%) and spinal bending angle (83.4%). Looking forward, this methodology provides unique prospects for broader applications such as tactile sensors, soft robotics, and health monitoring technologies.

Scenario Design for the Evaluation of Human–Machine Interaction in Modular Process Plants

AbstractOperators in modular process plants face new demands due to features like increasing automation and digitalization combined with an architecture allowing for reconfigurations in short time spans. However, operators must be able to master process control tasks at all times. For research purposes on topics like supporting operators in their performance in process control of modular process plants, this article aims at developing scenarios of such plants. Based on three conceptual levels describing the relation of chemical processes and their modularized realization and a neutralization process as use case, four scenarios were developed. Having the common task to increase the throughput, the scenarios differ in terms of complexity and constraints that need to be considered for process control.

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.

Ultra-stretchable, robust, self-healable conductive hydrogels enabled by the synergistic effects of hydrogen bonds and ionic coordination bonds toward high-performance e-skins

Ionic conductive hydrogels as electronic skins (e-skins) have showcased pivotal potentials in the realm of human health monitoring and human–machine interfaces. However, developing intelligent hydrogel with remarkable stretchability and mechanical elasticity is yet challenging. Herein, high-performance ion-conducting hydrogels with unprecedented mechanical properties and good transparent, conductive, self-healing properties are constructed via free-radical polymerization of acrylic acid (AA) within the polyvinyl alcohol (PVA) network, and the subsequent freeze–thaw (F-T) treatment and Zr4+ ion immersion crosslinking technique. Profiting from the synergy of multiple intermolecular/intramolecular hydrogen bonds between different polymers and coordination bonds of carboxyl-Zr4+, the resultant PVA/PAA/Zr4+ hydrogel exhibits high transparency (∼97 %), superior conductivity (0.6089 S/m), a long elongation at break (2053 %), robust self-healing efficiency (96.3 %), high deformation-tolerate property, and notable mechanical repeatability. These multifaceted attributes render the hydrogel to serve as an ionic conductor for capacitive/resistive −type strain sensor. The hydrogel-based resistive-type strain sensor features a wide detection range (0.5 %-800 %), rapid response time (150 ms), long-term stability, and consistent output stability, making it promising in monitoring diverse human motions and seamless human–machine interactions. Additionally, the hydrogel sensor demonstrates excellent linear response in ultra-wide range (0–900 % strain), high sensitivity, and excellent cycling stability in a capacitive mode, enabling to be used for accurately detecting the weights of objects. As such, this work advances the development of ionic conductive hydrogel as flexible and wearable electronic devices.

The Impact and Future of Generative Pre-trained Transformers: A Study of GPT in Enhancing Business and Technology

With the development of artificial intelligence (AI), natural language processing (NIP) has clearly become the key to enhancing AI capabilities, especially through the deployment of GPT. This paper explores the major advances and related commercial applications of GPT, as it is at the cutting edge of AI technology with far-reaching capabilities in terms of text generation, language understanding, and human-machine interaction. Developed by OpenAI, GPT uses innovative transformer architecture to make breakthroughs in a variety of language tasks, marking a major evolution in the potential applications of AI in various industries. This research explores the integration of GPT in sectors, highlighting how its complex text processing capabilities can be utilized to improve efficiency and innovative service delivery. The study highlights the transformative impact of GPT on business efficiency, strategic decision-making, and customer engagement, thanks to its scalable and adaptable AI framework. Finally, the document reflects the need for a strong ethical framework and regulatory supervision to guide the deployment of GPT technologies. As the Global Trade Agreement evolves, it is committed to shaping the future of global industry by promoting innovation while calling for a balanced approach to ethical considerations and social impacts.

Optimized design and performance testing of hydraulic electrostatic actuator

ABSTRACT Hydraulic electrostatic actuators have become a research hotspot for their inherent flexibility and safety of human-machine interaction. This paper aims to combine the characteristics of dielectric elastomers and fluid actuators, enhancing the dielectric constant and breakdown field strength by modifying Al2O3 on the surface of nanostructured BaTiO3 and in order to develop a hydraulic electrostatic actuator with silicone rubber as the matrix material. Single-factor experiments were firstly conducted to confirm the range of three factors affecting the actuation strain of the actuator: BaTiO3@Al2O3 content, thickness of the silicone rubber elastomer film, and pre-stretching ratio coefficient. The response surface methodology was used to study the interactive influence of the above three influencing factors on the actuation strain. It was obtained that A has an insignificant influence on the actuation strain, AB has a significant influence on the actuation strain, and B, C, AC, and BC have a highly influence on the actuation strain. Maximizing actuation strain as the optimization objective yielded the combination results of each factor: BaTiO3@Al2O3 content of 2.57%, thickness of 0.60 mm, and pre-stretching ratio coefficient of 2.50. Actuation strain tests were conducted with optimized parameters under no-load. The experimental results of the actuation strain test show that the relative error between the test value and the model prediction value of 16.47% is less than 5%, and the optimization model results are reliable. The optimized hydraulic electrostatic actuator was tested for actuation strain and electrical performance under different loads. The experiment showed that the maximum actuation strain of the hydraulic electrostatic actuator was 17.20% under a load of 100 g. The critical breakdown current of the actuator ranged from 115 μA to 130 μA, and the maximum electromechanical conversion efficiency of the actuator under different loads was 67.93%. Finally, a joint actuating device was developed based on the structure of the actuator, amplifying the output displacement of the actuator to 30 mm, and the linear displacement of the soft electro-fluid actuator can be converted into a 20° rotation angle through the gear steering mechanism, thus validating the effectiveness of the hydraulic electrostatic actuator.

Pattern Recognition in the Processing of Electromyographic Signals for Selected Expressions of Polish Sign Language.

Gesture recognition has become a significant part of human-machine interaction, particularly when verbal interaction is not feasible. The rapid development of biomedical sensing and machine learning algorithms, including electromyography (EMG) and convolutional neural networks (CNNs), has enabled the interpretation of sign languages, including the Polish Sign Language, based on EMG signals. The objective was to classify the game control gestures and Polish Sign Language gestures recorded specifically for this study using two different data acquisition systems: BIOPAC MP36 and MyoWare 2.0. We compared the classification performance of various machine learning algorithms, with a particular emphasis on CNNs on the dataset of EMG signals representing 24 gestures, recorded using both types of EMG sensors. The results (98.324% versus ≤7.8571% and 95.5307% versus ≤10.2697% of accuracy for CNNs and other classifiers in data recorded with BIOPAC MP36 and MyoWare, respectively) indicate that CNNs demonstrate superior accuracy. These results suggest the feasibility of using lower-cost sensors for effective gesture classification and the viability of integrating affordable EMG-based technologies into broader gesture recognition frameworks, providing a cost-effective solution for real-world applications. The dataset created during the study offers a basis for future studies on EMG-based recognition of Polish Sign Language.

Design and Evaluation of a Novel Variable Stiffness Hip Joint Exoskeleton.

An exoskeleton is a wearable device with human-machine interaction characteristics. An ideal exoskeleton should have kinematic and kinetic characteristics similar to those of the wearer. Most traditional exoskeletons are driven by rigid actuators based on joint torque or position control algorithms. In order to achieve better human-robot interaction, flexible actuators have been introduced into exoskeletons. However, exoskeletons with fixed stiffness cannot adapt to changing stiffness requirements during assistance. In order to achieve collaborative control of stiffness and torque, a bionic variable stiffness hip joint exoskeleton (BVS-HJE) is designed in this article. The exoskeleton proposed in this article is inspired by the muscles that come in agonist-antagonist pairs, whose actuators are arranged in an antagonistic form on both sides of the hip joint. Compared with other exoskeletons, it has antagonistic actuators with variable stiffness mechanisms, which allow the stiffness control of the exoskeleton joint independent of force (or position) control. A BVS-HJE model was established to study its variable stiffness and static characteristics. Based on the characteristics of the BVS-HJE, a control strategy is proposed that can achieve independent adjustment of joint torque and joint stiffness. In addition, the variable stiffness mechanism can estimate the output force based on the established mathematical model through an encoder, thus eliminating the additional force sensors in the control process. Finally, the variable stiffness properties of the actuator and the controllability of joint stiffness and joint torque were verified through experiments.

Heron: Visualizing and Controlling Chemical Reaction Explorations and Networks.

Automated and high-throughput quantum chemical investigations into chemical processes have become feasible in great detail and broad scope. This results in an increase in complexity of the tasks and in the amount of generated data. An efficient and intuitive way for an operator to interact with these data and to steer virtual experiments is required. Here, we introduce Heron, a graphical user interface that allows for advanced human-machine interactions with quantum chemical exploration campaigns into molecular structure and reactivity. Heron offers access to interactive and automated explorations of chemical reactions with standard electronic structure modules, haptic force feedback, microkinetic modeling, and refinement of data by automated correlated calculations including black-box complete active space calculations. It is tailored to the exploration and analysis of vast chemical reaction networks. We show how interoperable modules enable advanced workflows and pave the way for routine low-entrance-barrier access to advanced modeling techniques.

Bacterial film-based degradable triboelectric nanogenerator for both contact and non-contact sensing

As an emerging environmentally friendly energy conversion device, degradable triboelectric nanogenerators (TENGs) play an important role in self-powered sensing, health care and human–machine interaction. However, traditional degradable materials used in TENGs often suffer from limited material sources and complex preparation processes, restricting large-scale production for widespread applications. Here, we propose a simple and efficient way to prepare degradable TENGs with inactivated bacterial film, taking advantage of wide source, self-proliferation and easy culture properties of bacteria. The prepared bacterial film-based TENG (BF-TENG) has stable electrical output, good durability and fatigue resistance, along with superior capabilities in both contact and non-contact sensing. In contact mode, the BF-TENG can generate a peak voltage of up to 83.3 V, which is employed for accurate Morse code transmission. In non-contact mode, the BF-TENG is capable of effectively perceiving the target polymer at a distance of 150 cm. The non-contact LED control circuit and a sensing array based on BF-TENG further demonstrate its practical potential for gesture recognition and spatial position perception. Furthermore, the fully degradable nature of BF-TENG ensures effortless disposal after use without environmental impact, serving as a promising solution for the new generation of eco-friendly and sustainable sensing devices.

Composite Janus film based on the synergistic interactions of π-π stacking and dynamic covalent bond toward direction recognition sensing

Although flexible strain sensors have made important advancements recently, most of them are unable to recognize the direction of motion, which greatly limits their application in fields such as human-machine interaction. This paper presents the fabrication of a bilayer asymmetric composite film that exhibits Janus dual-sided characteristics and interfacial properties. Specifically, the two sides possess different chemical compositions and surface features. The strong π-π stacking interaction between carbon nanotubes (CNTs) and pyrene enables a tight coating on the surface of poly(glycidyl propyl urethane) (PGPU), resulting in excellent sensing capabilities and electromagnetic shielding properties for the composite material. This composite film can effectively monitor the amplitude and direction of motion. Firstly, pyrene-grafted polyurethane (PGPU) was synthesized including on dynamic covalent bonds. The tensile strength of different samples can reach up to 19.69 MPa, and the strain at break is up to 501.95 %. Furthermore, PGPU/CNTs conductive composite films were fabricated by spray-coating carbon nanotubes (CNTs) onto PGPU, and the pyrene units in PGPU can effectively interact with CNTs via π-π stacking, ensuring that stable adhesion of CNTs layer during long-term usage. Due to the dynamic covalent bonds and hydrogen bonds inside PGPU, PGPU and PGPU/CNTs both exhibit well-performed self-healing capability. Notably, the Janus structure of PGPU/CNTs can adjust the positive and negative values of relative resistance based on stretchable and compressive status of CNTs layer. Thus, PGPU/CNTs are directionally sensitive and self-healing flexible wearable sensor, which might apply in human-machine interaction field.

Advanced facial recognition with LBP-URIGL hybrid descriptors

AbstractFacial recognition technology is transformative in security and human-machine interaction, reshaping societal interactions. Robust descriptors, essential for high precision in machine learning tasks like recognition and recall, are integral to this transformation. This paper presents a hybrid model enhancing local binary pattern descriptors for facial representation. By integrating rotation-invariant local binary pattern with uniform rotation-invariant grey-level co-occurrence, employing linear discriminant analysis for feature space optimization, and utilizing an artificial neural network for classification, the model achieves exceptional accuracy rates of 100% for Olivetti Research Laboratory, 99.98% for Maastricht University Computer Vision Test, and 99.17% for Extended Yale B, surpassing traditional methods significantly.

3D Network Spacer-Embedded Flexible Iontronic Pressure Sensor Array with High Sensitivity over a Broad Sensing Range.

Microstructure construction is a common strategy for enhancing the sensitivity of flexible pressure sensors, but it typically requires complex manufacturing techniques. In this study, we develop a flexible iontronic pressure sensor (FIPS) by embedding an isolated three-dimensional network spacer (3DNS) between an ionic gel and a flexible Ti3C2Tx MXene electrode, thereby avoiding complex microstructure construction techniques. By leveraging substantial deformation of the 3DNS and the high capacitance density resulting from the electrical double layer effect, the sensor exhibits high sensitivity (87.4 kPa-1) over a broad high-pressure range (400-1000 kPa) while maintaining linearity (R2 = 0.998). Additionally, the FIPS demonstrates a rapid response time of 46 ms, a low limit of detection at 50 Pa, and excellent stability over 10 000 cycles under a high pressure of 600 kPa. As practical demonstrations, the FIPS can effectively monitor human motion such as elbow bending and assist a robotic gripper in accurately sensing gripping tasks. Moreover, a real-time, adaptive 7 × 7 sensing array system is built and can recognize both numeric and alphabetic characters. Our design philosophy can be extended for fabricating pressure sensors with high sensing performance without involving complex techniques, facilitating the applications of flexible sensors in human motion monitoring, robotic tactile sensing, and human-machine interaction.