Human-machine Interaction Research Articles

A Triboelectric Nanogenerator-Based Wide Range Self-Powered Flexible Pressure Sensor

Self-powered flexible pressure sensors are needed in applications such as human-machine interaction. Here, we present a triboelectric nanogenerator (TENG) based self-powered flexible pressure sensor capable of operating over a wide-range of pressures (3.2 - 1176 kPa). The sensor exhibits excellent sensitivities in various regions of the pressure range, i.e., 3.16, 0.023 and 0.031 V/kPa in the low (1-10 kPa), medium-high (10-500 kPa) and ultra-high (>500 kPa) pressure regimes respectively. Stable and repeatable TENG responses are achieved for all three pressure regimes, and this is due to the way the real contact area at the TENG interface varies with contact pressure. These results highlight the potential for TENGs in a wide range of applications such as: detecting pressure in wearable devices, human-machine interface, biomedical and automotive applications. As a proof-of-concept, we have demonstrated the use of the presented device in applications such as detection of human and robot finger tapping, collection of human gait information, and detection of impact forces.

Capacitive BaTiO3-PDMS hand-gesture sensor: Insights into sensing mechanisms and signal classification with machine learning

Flexible sensors have gained extensive interest because of their versatile applications in healthcare, robotics, and wearable devices. This study introduces a capacitive sensor utilizing barium titanate oxide (BaTiO3)-polydimethylsiloxane (PDMS) for bending sensing and addresses crucial performance parameters including sensitivity, repeatability, response time, and durability. The sensor exhibited a notable capacitance change of 42.85% in conjunction with fast (1 s) responses and recovery times, and minimal hysteresis (<2%). Its reliable performance across varying bending rates and durability through extensive cyclic tests underscore its applicability in real-world scenarios. Importantly, the sensor's capabilities were enhanced by integrating machine learning (ML), achieving an impressive accuracy of 97.11% in recognizing hand-sign language gestures. Furthermore, finite element analysis was employed to validate the correlation between the increase in compression-induced packing density and capacitance enhancement. This holistic integration of advanced materials, computational simulations, and ML not only extends the boundaries of sensor technology but also holds promise for revolutionizing human–machine interactions, aiding speech-impaired individuals, and enriching virtual reality experiences. This study represents a pivotal advancement in the field of flexible sensors and the unlocking of new dimensions of their applications.

Distributed virtual simulation experimental software for high-power electric traction system of 600 km/h high-speed maglev train

The operational speed of high-speed maglev trains has reached a groundbreaking 600 km/h, facilitating faster intercity transportation. However, the design and verification of corresponding high-power electric traction systems face significant challenges. To address these issues, this study develops a distributed virtual simulation experimental software for the high-power electric traction system of a 600 km/h high-speed maglev train. The software first establishes virtual digital models for the high-speed maglev train, the high-speed maglev guideways, and the high-power electric traction converters to simulate the high-speed maglev line operation scenarios. Next, it constructs a client–server simulation software architecture, designs a human–machine interaction platform and an electric traction mathematical module, and devises an information exchange method to enable real-time tracking and display for the operating conditions of high-power electric traction systems. Lastly, by integrating electric traction control strategies, fault analysis and optimization design modules, the software effectively designs and verifies the optimal electric traction systems of high-speed maglev trains, and promptly identifies the potential faults in long stator linear synchronous motors and high-power electric traction converters within the electric traction systems. Engineering applications demonstrate that the software can significantly enhance the design and virtual simulation efficiency for the high-power electric traction systems of high-speed maglev trains, thus meeting critical engineering application needs.

MDVC corpus: empowering Moroccan Darija speech recognition

Automatic speech recognition (ASR) technology has significantly transformed human-machine interactions, but it remains limited in its representation of diverse languages and dialects. Moroccan Darija, the lively Moroccan dialect, has long been underrepresented in the realm of language technology. To address this gap, we present a novel corpus of audio files accompanied by meticulously transcribed Moroccan Darija speech. The corpus comprises 1,000 hours of diverse content, featuring multiple Moroccan accents, extracted from 80 YouTube channels. To standardize the representation of Moroccan Darija in our corpus, we made efforts to establish consistent writing norms and conventions. In addition to the dataset creation, we applied fine-tuning using the Wav2Vec2 model on the Moroccan Darija voice corpus (MDVC) dataset achieving a remarkable word error rate (WER) of 9%. This article discusses the current state of Moroccan Darija research, highlighting the scarcity of resources and the need for robust ASR systems. Our contribution offers a valuable resource for researchers and developers, and by standardizing the Darija language, we strive to improve ASR system for this low resource language.

Task-Efficiency Oriented V2X Communications: Digital Twin Meets Mobile Edge Computing

Digital Twin (DT) has emerged as an enabling technology for the sixth generation (6G) vehicle-to-everything (V2X) communications. However, there are two crucial issues on leveraging DT for 6G V2X communications. First, what kind of DT capabilities can be combined with the 6G V2X networks? Second, how to transform the DT capabilities into the practical V2X network performance gain? Motivated to solve these problems, this article investigates the DT capabilities under a DT and mobile edge computing empowered 6G V2X network architecture. Specifically, three DT capabilities are presented: (1) Strengthening the human-machine interaction via driving behavior analysis; (2) Improving the traffic safety via knowledge-based vehicle fault diagnosis; (3) Analyzing the spatial-temporal traffic characteristics via data aggregation. Furthermore, we investigate two case studies for illustrating how the DT capabilities can be utilized to perform the task-efficiency oriented V2X network scheduling. In the first case study, the driver behavior analysis result is combined with the V2X channel scheduling strategy. In the second case study, a deep reinforcement learning based vehicle merging decision is devised in the DT domain. Then, a coalition based V2X channel scheduling strategy is proposed to help accomplish the vehicle merging decision task. Finally, we evaluate the performance of our proposed task-efficiency oriented V2X channel scheduling schemes and highlight the future research directions.

Soft Polymer Optical Fiber Sensors for Intelligent Recognition of Elastomer Deformations and Wearable Applications.

In recent years, soft robotic sensors have rapidly advanced to endow robots with the ability to interact with the external environment. Here, we propose a polymer optical fiber (POF) sensor with sensitive and stable detection performance for strain, bending, twisting, and pressing. Thus, we can map the real-time output light intensity of POF sensors to the spatial morphology of the elastomer. By leveraging the intrinsic correlations of neighboring sensors and machine learning algorithms, we realize the spatially resolved detection of the pressing and multi-dimensional deformation of elastomers. Specifically, the developed intelligent sensing system can effectively recognize the two-dimensional indentation position with a prediction accuracy as large as ~99.17%. The average prediction accuracy of combined strain and twist is ~98.4% using the random forest algorithm. In addition, we demonstrate an integrated intelligent glove for the recognition of hand gestures with a high recognition accuracy of 99.38%. Our work holds promise for applications in soft robots for interactive tasks in complex environments, providing robots with multidimensional proprioceptive perception. And it also can be applied in smart wearable sensing, human prosthetics, and human-machine interaction interfaces.

Real-time decision support for human–machine interaction in digital railway control rooms

This study proposes a real-time Decision Support System (DSS) using machine learning to enhance proactive management of Human–Machine Interaction (HMI) in safety–critical digital control rooms. The DSS provides explainable predictions and recommendations regarding near-future automation usage, customized for the railway control room management, who supervise the operations of traffic controllers (TCs). In this setting, TCs decide on the spot whether to manually or automatically open signals to regulate railway traffic, a critical aspect of ensuring punctuality and safety. This time-setting specific HMI differs across TCs and is not yet supported by a data-driven tool. The proposed DSS includes agreement levels for predictions among different modeling paradigms: linear models, tree-based models, and deep neural networks. SHAP (SHapley Additive exPlanations) values are deployed to assess the agreement level in explainability between these different modeling paradigms. The prescriptions are based on the HMI of well-performing peers. We implement the DSS as proof of concept at the Belgian railway infrastructure company and report end-user feedback on the perception, the operational impact, and the inclusion of agreement levels.

Highly sensitive and ultra-durable microfiber-based sensor with synergistic conductive network for motion detection

Smart wearable devices have applications in diverse fields, including sports, medicine, electronic skin, and human–machine interactions. However, the effective integration of flexible sensors into clothing to create practical wearable devices remains a formidable challenge. Thus, this study designed a resilient and flexible sensor featuring an innovative “island-bridge”-structured conductive network. The remarkable flexibility and adaptability of the proposed sensor allows for its seamless integration into various clothing styles, thereby preserving the original aesthetics and comfort of the garment. Consequently, it facilitates real-time monitoring of physiological and environmental data while enhancing ergonomic characteristics. In this study, electrospun fibers were meticulously modified using MXene and carbon nanotubes, resulting in their physical and chemical integration and the formation of a dependable conductive network. The island-bridge design significantly improved the sensor performance, which achieved a remarkable gauge factor of 30.5, fast response time of 57 ms, and expanded detection range of 0–570 %. Furthermore, owing to the exceptional elasticity of the electrospun fibers, the sensor demonstrated outstanding durability, maintaining consistent sensing performance even after 65,000 draw cycles. Thus, this study presents a novel approach for designing and fabricating flexible sensors and highlights their potential for various practical applications.

Coupling thermogalvanic and piezoresistive effects in a robust hydrogel for Deep-Learning-Assisted Self-Powered sign language and object recognition

The human hand provides rich sensory information for proper interaction with the environment. However, designing passive hand-sensing arrays with robust mechanical properties, high conductivity, ease of integration and reliability remains a long-term challenge. Here, we develop a robust dynamic crystalline ion-injected thermoelectric gel (DCI-TEG) via a simple solvent-exchange salt-out strategy. The resultant DCI-TEG exhibits high tensile strength (3.8 MPa), toughness (7.5 MJ m−3), and ionic conductivity of 1.84 S m−1. Benefiting from non-covalent crosslinking, the gel electrolyte and electrodes can be fully integrated into modules with tight physical and electrical contact, facilitating a stable and reliable signal output. By coupling thermogalvanic and piezoresistive effects, the DCI-TEG permits self-powered timely sensing of pressure using current signals. By implanting ionic thermoelectric module arrays on a glove, a wearable pressure sensing route is demonstrated with the merits of self-power, low cost and simple preparation. With the help of deep learning algorithms, a self-powered recognition for sign language and objects is realized in wearable/portable scenarios by active high-accuracy pressure detection. This work gives people a new direction toward barrier-free passive communication and human–machine interaction in the next-generation artificial intelligence.

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.

Robust dynamic surface control of series elastic actuators based on reduced-order extended state observer

The compliance of series elastic actuators (SEAs) is significant for ensuring safe human–machine interaction. However, in practical applications, the SEA is inevitably encountered with unknown disturbances, such as parameter uncertainties, unmodeled dynamics, and environmental interferences. In this paper, a robust dynamic surface control scheme is presented to address the high-precision trajectory tracking problem of the SEA. Firstly, a reduced-order extended state observer (RESO) with only link-side position measurements is designed to estimate the unknown lumped disturbance and unmeasurable system states. Based on the estimated values from the RESO, a robust dynamic surface controller is proposed for the trajectory tracking of the SEA. Additionally, the finite-time filter is introduced to overcome the “explosion of complexity” in the backstepping controller, and the filtering error in the dynamic surface controller is reduced compared with the conventional first- and second-order filters. The stability of the closed-loop system is guaranteed by the Lyapunov theory. Finally, the effectiveness and superiority of the proposed control algorithm are verified through simulations and experiments.

The Prospects and Challenges of Artificial Intelligence Technology in Archival Management

The evolution of information technology prompts inquiry into AI's role in archival management, emphasizing future trajectories, technological advancements, and challenges. AI promises intelligent, automated, and personalized archive management, enhancing efficiency and service quality. Potential innovations include learning-based categorization and natural language summarization, offering advanced user services. Challenges like data privacy and human-machine interaction balance also offer developmental opportunities. This discourse stresses collaborative efforts to sustainably integrate AI in archival management, fostering digitalization and intelligence transformation.

Attentional cueing: Gaze is harder to override than arrows.

Gaze is an important and potent social cue to direct others' attention towards specific locations. However, in many situations, directional symbols, like arrows, fulfill a similar purpose. Motivated by the overarching question how artificial systems can effectively communicate directional information, we conducted two cueing experiments. In both experiments, participants were asked to identify peripheral targets appearing on the screen and respond to them as quickly as possible by a button press. Prior to the appearance of the target, a cue was presented in the center of the screen. In Experiment 1, cues were either faces or arrows that gazed or pointed in one direction, but were non-predictive of the target location. Consistent with earlier studies, we found a reaction time benefit for the side the arrow or the gaze was directed to. Extending beyond earlier research, we found that this effect was indistinguishable between the vertical and the horizontal axis and between faces and arrows. In Experiment 2, we used 100% "counter-predictive" cues; that is, the target always occurred on the side opposite to the direction of gaze or arrow. With cues without inherent directional meaning (color), we controlled for general learning effects. Despite the close quantitative match between non-predictive gaze and non-predictive arrow cues observed in Experiment 1, the reaction-time benefit for counter-predictive arrows over neutral cues is more robust than the corresponding benefit for counter-predictive gaze. This suggests that-if matched for efficacy towards their inherent direction-gaze cues are harder to override or reinterpret than arrows. This difference can be of practical relevance, for example, when designing cues in the context of human-machine interaction.

Ionic Rectification by Dynamic Regulation of the Electric Double Layer at the Hydrogel Interface.

Hydrogels play a pivotal role in the realm of iontronics, contributing to the realization of futuristic human-machine interactions. The electric double layer (EDL) between the hydrogel and electrode provides an essential ionic-electronic coupling interface. While prior investigations primarily delved into elucidating the formation mechanism of the EDL, our study shifts the focus to showcasing the current generation through the mechanical modulation of the EDL at the hydrogel-metal interfaces. The dynamic EDL was constructed by the mechano-driven contact-separation process between the polyacrylamide (PAAm) hydrogel and Au. Influencing factors on the dynamic regulation of the EDL such as ion concentration, types of salt, contact-separation frequency, and deformation degree were investigated. Dehydration usually limits the practical applications of hydrogels, and it is a long-standing and difficult problem. However, it seemed to be able to slow the EDL formation process here, resulting in a sustained continuous direct current signal output. Such hydrogel iontronics could rectify the displacement electronic current of a triboelectric nanogenerator by the ionic current. The directional migration of ions could be further enhanced by using charge-collecting metals with different work functions, for example, Au and Al. It offers a paradigm to enable ionic rectification that could be seamlessly incorporated into electronic systems, ushering in a new era for efficient energy harvesting and biomimetic nervous systems.

MXene-Based Pressure Sensor with a Self-Healing Property for Joule Heating and Friction Sliding.

As a kind of flexible electronic device, flexible pressure sensor has attracted wide attention in medical monitoring and human-machine interaction. With the continuous deepening of research, high-sensitivity sensor is developing from single function to multi-function. However, Current multifunctional sensors lack the ability to integrate joule heating, detect sliding friction, and self-healing. Herein, a MXene/polyurethane (PU) flexible pressure sensor with a self-healing property for joule heating and friction sliding is fabricated. The MXene/PU sensitive layer with special spinosum structure is prepared by a simple spraying method. After face-to-face assembly of the sensitive layers, the MXene/PU flexible pressure sensor is obtained and showed excellent sensitivity (150.65kPa-1), fast response/recovery speed (75.5/63.9ms), and good stability (10000 cycles). Based on the self-healing property of PU, the sensor also has the ability to heal after mechanical damage. In addition, the sensor realizes the joule heating function under low voltage, and has the real-time monitoring ability of sliding objects. Combined with low cost and simple manufacturing method, the multi-functional MXene/PU flexible sensor shows a wide range of application potential in human activity monitoring, thermal management, and slip recognition.

The heart of an AI

The article presents an analysis centered on the emotional lapses of artificial intelligence (AI) and the influence of these lapses on two critical aspects. Firstly, the article explores the ontological impact of emotional lapses, elucidating how they hinder AI’s capacity to develop a moral sense. The absence of a moral emotion, such as sympathy, creates a barrier for machines to grasp and ethically respond to specific situations. This raises fundamental questions about machines’ ability to act as moral agents in the same manner as human beings. Additionally, the article sheds light on the practical implications within human-machine relations and their effect on human friendships. The lack of friendliness or its equivalent in interactions with machines directly impacts the quality and depth of human relations. This concerningly suggests the potential replacement or compromise of genuine interpersonal connections due to limitations in human-machine interactions.

Human Machine Interface

This document presents a comprehensive literature survey of recent advancements in Human-Machine Interaction (HMI), with a primary focus on cutting-edge technologies poised to significantly shape the future trajectory of this field. By providing insights into current technical achievements and prospective developments, it becomes evident that advanced HMI technologies are on the brink of convergence. This convergence entails the integration of functionalities across HMI devices, the establishment of novel sensor fusion methodologies, and an overarching goal of bridging the gap between intelligent machines and humans. HMI holds pivotal importance in enabling the widespread adoption of robots and machines among populations lacking expertise in software and hardware. This is particularly transformative for segments such as the elderly and disabled, where intuitive interfaces facilitated by HMI facilitate enhanced accessibility and usability. As the field progresses, it is poised to revolutionize the way individuals interact with and benefit from intelligent machines, thereby fostering inclusivity and empowerment across diverse demographic groups. Key Words: HMI, sensor, robot and machines, software and hardware.

Predicting User Preference for Innovative Features in Intelligent Connected Vehicles from a Cultural Perspective

The increasing level of intelligence in automobiles is driving a shift in the human–machine relationship. Users are paying more attention to the intelligent cabin and showing a tendency toward customization. As culture is considered to be an important factor in guiding user behavior and preference, this study innovatively incorporates cultural and human factors into the model to understand how individual cultural orientation influences user preference for innovative human-machine interaction (HMI) features. Firstly, this study considered five Hofstede cultural dimensions as potential impact factors and constructed a prediction model through the random forest algorithm so as to analyze the influence mechanism of culture. Subsequently, K-means clustering was used to classify the sample into three user groups and then predict their preferences for the innovative features in the intelligent cabin. The results showed that users with a higher power distance index preferred a sense of ceremony and show-off-related features such as ambient lighting and welcome mode, whereas users with high individualism were keen on a more open and personalized in-vehicle information system. Long-term orientation was found to be associated with features that help to improve efficiency, and users with a lower level of uncertainty avoidance and restraint were more likely to be attracted to new features and were also more willing to use entertainment-related features. The methodology developed in this study can be widely applied to people in different countries, thus effectively exploring the personal requirements of different individuals, guiding further user experience design and localization when breaking into a new market.

Balancing Humans and Machines: A Study on Integration Scale and Its Impact on Collaborative Performance

In the evolving artificial intelligence domain, hybrid human-machine systems have emerged as a transformative research area. While many studies have concentrated on individual human-machine interactions, there is a lack of focus on multi-human and multi-machine dynamics. This paper delves into these nuances by introducing a novel statistical framework that discerns integration accuracy in terms of precision and diversity. Empirical studies reveal that performance surges consistently with scale, either in human or machine settings. However, hybrid systems present complexities. Their performance is intricately tied to the human-to-machine ratio. Interestingly, as the scale expands, integration performance growth isn't limitless. It reaches a threshold influenced by model diversity. This introduces a pivotal `knee point', signifying the optimal balance between performance and scale. This knowledge is vital for resource allocation in practical applications. Grounded in rigorous evaluations using public datasets, our findings emphasize the framework's robustness in refining integrated systems.

From Analog to Algorithm

In the digital age, the current Management literature remains heavily rooted in anthropocentric and analog perspectives, often overlooking the profound influence of algorithms and artificial intelligence (AI) in modern organizational dynamics. To this end, this systematic literature review seeks to bridge this gap by exploring the evolving landscape of AI in organizations and the role of the Actor-Network Theory (ANT) as a theoretical-methodological lens for understanding AI’s impact on Management. Through a systematic literature review, we address research on the utilization of AI in Management over the past decade, the applications of ANT in non-IT or non-AI management studies, and studies explaining IT or AI concepts via ANT. Our findings highlight a significant research gap in understanding human-machine interactions and Business Management within organizations that use AI. Additionally, we identify potential avenues for future scholarly contributions, emphasizing the need for a more integrated approach that considers human and machine actors in organizational contexts.