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Introduction: Rehabilitation devices assist individuals with movement disorders by supporting daily activities and facilitating effective rehabilitation training. Accurate and early motor intention detection is vital for real-time device applications. However, traditional methods of motor intention detection often rely on single-mode signals, such as EEG or EMG alone, which can be limited by low signal quality and reduced stability. This study proposes a multimodal fusion method based on EEG–EMG functional connectivity to detect sitting and standing intentions before movement execution, enabling timely intervention and reducing latency in rehabilitation devices. Methods: Eight healthy subjects and five spinal cord injury (SCI) patients performed cue-based sit-to-stand and stand-to-sit transition tasks while EEG and EMG data were recorded simultaneously. We constructed EEG–EMG functional connectivity networks using data epochs from the 1.5-s period prior to movement onset. Pairwise spatial filters were then designed to extract discriminative spatial network topologies. Each filter paired with a support vector machine classifier to classify future movements into one of three classes: sit-to-stand, stand-to-sit, or rest. The final prediction was determined using a majority voting scheme. Results: Among the three functional connectivity methods investigated—coherence, Pearson correlation coefficient and mutual information (MI)—the MI-based EEG–EMG network showed the highest decoding performance (94.33%), outperforming both EEG (73.89%) and EMG (89.16%). The robustness of the fusion method was further validated through a fatigue training experiment with healthy subjects. The fusion method achieved 92.87% accuracy during the post-fatigue stage, with no significant difference compared to the pre-fatigue stage (p > 0.05). Additionally, the proposed method using pre-movement windows achieved accuracy comparable to trans-movement windows (p > 0.05 for both pre- and post-fatigue stages). For the SCI patients, the fusion method showed improved accuracy, achieving 87.54% compared to single- modality methods (EEG: 83.03%, EMG: 84.13%), suggesting that the fusion method could be promising for practical rehabilitation applications. Conclusion: Our results demonstrated that the proposed multimodal fusion method significantly enhances the performance of detecting human motor intentions. By enabling early detection of sitting and standing intentions, this method holds the potential to offer more accurate and timely interventions within rehabilitation systems. [ABSTRACT FROM AUTHOR]

AbstractColor is an important attribute of human-perceived human-machine interface (HMI), and it is unclear which color combinations produce the best performance and user experience in visual search tasks. Taking the metro electronic guide screen (MEGS) as an example, the eye-tracking (E-T) device is used to record the pupil diameter and search time, and the Likert scale is used to evaluate the satisfaction and usability, and the influence of the four factors of background hue, saturation, brightness and text color in HMI on user search efficiency and user experience is discussed in depth. The results show that the recognition efficiency is best when the background hue is medium brightness, medium saturation green with white text color. Meanwhile, the results of user satisfaction and ease of use are basically consistent with the objective experimental results, and there is a certain correlation between the subjective preference of color combination and visual performance. [ABSTRACT FROM AUTHOR]

This study explores whether labeling AI as either "trustworthy" or "reliable" influences user perceptions and acceptance of automotive AI technologies. Utilizing a one-way between-subjects design, the research presented online participants (N = 478) with a text presenting guidelines for either trustworthy or reliable AI, before asking them to evaluate 3 vignette scenarios and fill in a modified version of the Technology Acceptance Model which covers different variables, such as perceived ease of use, human-like trust, and overall attitude. While labeling AI as "trustworthy" did not significantly influence people's judgements on specific scenarios, it increased perceived ease of use and human-like trust, namely benevolence, suggesting a facilitating influence on usability and an anthropomorphic effect on user perceptions. The study provides insights into how specific labels affect adopting certain perceptions of AI technology. [ABSTRACT FROM AUTHOR]

AbstractReal-time goal collaboration represents a promising approach to human-vehicle cooperative driving; however, it remains underexplored. To address this gap, we introduced an innovative human-vehicle cooperative approach and designed four interactive types with increasing autonomous levels to implement it. Additionally, we proposed seven interface design principles to design three increasing levels of transparency for the four interactive types, aiming to enhance collaboration. Experimental results demonstrate the favorable reception of the proposed cooperative approach by users. Furthermore, higher interactive autonomous levels result in reduced workload, and higher interface transparency levels lead to increased satisfaction, trust, and mutual dependence. Notably, the combination of the highest interactive autonomous level and interface transparency level, which exhibited the best performance, is recommended for practical application. This collaborative approach expands the research domain of human-vehicle cooperative driving and offers extensive potential applications across various relevant scenarios. [ABSTRACT FROM AUTHOR]

Self-healing triboelectric nanogenerators (TENGs), which incorporate self-healing materials capable of recovering their structural and functional properties after damage, are transforming the field of artificial skin by effectively addressing challenges associated with mechanical damage and functional degradation. This review explores the latest advancements in self-healing TENGs, emphasizing material innovations, structural designs, and practical applications. Key materials include dynamic covalent polymers, supramolecular elastomers, and ion-conductive hydrogels, which provide rapid damage recovery, superior mechanical strength, and stable electrical performance. Innovative structural configurations, such as layered and encapsulated designs, optimize triboelectric efficiency and enhance environmental adaptability. Applications span healthcare, human–machine interfaces, and wearable electronics, demonstrating the immense potential for tactile sensing and energy harvesting. Despite significant progress, challenges remain in scalability, long-term durability, and multifunctional integration. Future research should focus on advanced material development, scalable fabrication, and intelligent system integration to unlock the full potential of self-healing TENGs. This review provides a comprehensive overview of current achievements and future directions, underscoring the pivotal role of self-healing TENGs in artificial skin technology. [ABSTRACT FROM AUTHOR]

With the rapid development of intelligent wearable technology, multimodal tactile sensors capable of data acquisition, decoupling of intermixed signals, and information processing have attracted increasing attention. Herein, a decoupled temperature–pressure dual‐mode sensor is developed based on single‐walled carbon nanotubes (SWCNT) and poly(3,4‐ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS) decorated porous melamine foam (MF), integrating with a deep learning algorithm to obtain a multimodal input terminal. Importantly, the synergistic effect of PEDOT:PSS and SWCNT facilitates the sensor with ideal decoupling capability and sensitivity toward both temperature (38.2 µV K−1) and pressure (10.8% kPa−1) based on the thermoelectric and piezoresistive effects, respectively. Besides, the low thermal conductivity and excellent compressibility of MF also endow it with the merits of a low‐temperature detection limit (0.03 K), fast pressure response (120 ms), and long‐term stability. Benefiting from the outstanding sensing characteristics, the assembled sensor array showcases good capacity for identifying spatial distribution of temperature and pressure signals. With the assistance of a deep learning algorithm, it displays high recognition accuracy of 99% and 98% corresponding to "touch" and "press" actions, respectively, and realizes the encrypted transmission of information and accurate identification of random input sequences, providing a promising strategy for the design of high‐accuracy multimodal sensing platform in human–machine interaction. [ABSTRACT FROM AUTHOR]

Tactile sensors with capability of multiaxial force perception play a vital role in robotics and human–machine interfaces. Flexible optical waveguide sensors have been an emerging paradigm in tactile sensing due to their high sensitivity, fast response, and antielectromagnetic interference. Herein, a flexible multiaxial force sensor enabled by U‐shaped optical micro/nanofibers (MNFs) is reported. The MNF is embedded within an elastomer film topped with a dome‐shaped protrusion. When the protrusion is subjected to vector forces, the embedded MNF undergoes anisotropic deformations, yielding time‐resolved variations in light transmission. Detection of both normal and shear forces is achieved with sensitivities reaching 50.7 dB N−1 (14% kPa−1) and 82.2 dB N−1 (21% kPa−1), respectively. Notably, the structural asymmetry of the MNF induces asymmetrical optical modes, granting the sensor directional responses to four‐directional shear forces. As proof‐of‐concept applications, tactile visualizations for texture and relief pattern recognition are realized with a spatial resolution of 160 µm. Moreover, a dual U‐shaped MNF configuration is demonstrated as a human–machine interface for cursor manipulation. This work represents a step towards advanced multiaxial tactile sensing. [ABSTRACT FROM AUTHOR]

A triboelectric nanogenerator (TENG) is a kind of energy harvester which converts mechanical energy into electrical energy with electron transfer and transport between two different materials during cycling tribology. To increase the contact area between tribo-layers and enhance the output of TENGs, many studies prepare patterned micro/nanostructured tribo-layers using semiconductor processes like lithography and etching at high cost and with long processing times. Here, we propose a new method to quickly produce high-aspect-ratio (HAR) microneedles of polydimethylsiloxane (PDMS) for TENG triboelectric layers using a two-pulse laser-ablated polymethyl methacrylate mold and casting. It has the merit of employing low-cost CO2 laser microfabrication and polymer casting in a feasible way to produce efficient tribo-electric layers. Two-pulse laser ablation is an efficient method for fabricating HAR microstructures with increasing depth at a constant width and density compared to single-pulse ablation. It enhances the depth of microneedles at a constant width and successfully casts PDMS tribo-layers with microneedles that have an aspect ratio 1.88 times higher than those produced by the traditional single-pulse process. The microneedle-PDMS (MN-PDMS) layer is combined with Al sheets to form the MN-PDMS-Al TENG. Compared with the flat PDMS-Al TENG and single-pulse PDMS-Al TENG, the two-pulse TENG enhances open-circuit voltage (Voc) by 1.63 and 1.48 times, the short-circuit current (Isc) by 1.92 and 1.47 times, and the output power by 3.69 and 2.16 times, respectively. This two-pulse ablation method promotes the output performance of TENGs, which has the potential for applications in self-powered devices and sustainable energy. [ABSTRACT FROM AUTHOR]

For people with physical disabilities, it is often desirable to regain control over their personal environment and communication tools. This paper introduces a novel Human- Machine Interface (HMI) using one-shot learning for individualized control signals without extensive training or specialized hardware. Our work suggests a modular system that utilizes common, easily accessible devices like webcams to interpret user-defined gestures and commands through a single demonstration. As a feasibility study on healthy volunteers, we investigate the control of a computer mouse by head movements only. We demonstrate the technical details of the HMI and discuss its potential applications in enhancing the autonomy and interaction capabilities of users with disabilities. By combining usercentric design principles with the advancements in one-shot learning, we aim to forge a more inclusive, accessible path forward in the development of assistive technologies. [ABSTRACT FROM AUTHOR]

Most flexible human–machine interfaces emulate the tactile system of the skin, which has the risk of contact damage. Additionally, contact deformation often leads to a hysteresis response. Non‐contact interaction can address these problems. Inspired by the electroreception capabilities of the elephantnose fish, this study introduces a non‐contact sensing model employing monopolar controlled ionic hydrogel. Compared to most existing mutual capacitive non‐contact sensing models, this model not only boosts responsivity by over 3.5 times but also streamlines the sensing architecture. Utilizing this sensing model, a flexible non‐contact human–machine interface is developed by organizing three differently shaped hydrogels into an asymmetric configuration. This device reliably discerns six non‐contact gestures using machine learning algorithms and supports at least eleven interactive functions by detecting the duration of gestures, enabling continuous real‐time control over external devices. This advancement heralds a more liberated paradigm of human–machine interaction with promising implications for the Internet of Things. [ABSTRACT FROM AUTHOR]

AbstractThe prefrontal cortex (PFC) is essential in emotional and cognitive processes, yet the effects of light and color on brain activity and emotional perception remain underexplored. This study examined the influence of screen brightness and saturation on emotional perception in human-machine interfaces under simulated daytime, cloudy, and nighttime conditions, with 29 volunteers participating. Functional near-infrared spectroscopy assessed PFC hemodynamics. Results indicated higher pleasantness for low versus high saturation under both high and low brightness, while high brightness and saturation were more attractive to participants. High-saturation screens activated the orbitofrontal cortex (OFC) and left dorsolateral PFC (dIPFC) under high brightness, whereas low saturation increased left dIPFC activation under low brightness. OFC activation positively correlated with pleasantness, while left dIPFC activation negatively correlated with attractiveness. These findings highlight the neurophysiological impact of light and color on emotional perception, providing insights to optimize interface design and user experience in varying lighting conditions. [ABSTRACT FROM AUTHOR]

Smart wearable sensors in human–machine interfaces (HMI) facilitate communication and interface between humans and robots, as well as among humans. Conventional wearables face significant limitations, including performance degradation under various deformations (e.g., strain and pressure), limited stretchability and flexibility, poor comfort, and breathability, complicating their integration into HMI applications. In response to these limitations, a smart, sewable, high‐precision HMI device based on a soft, textile‐based sensor with machine learning (ML)‐assisted data processing is proposed. The (Ag)‐based fabric electrode integrated into polymeric composite dielectric layer, exhibiting hysteresis error of (≤4%), a tensile strain sensitivity of gauge factor (GF) ≃ 1.03 at 100% elongation, a pressure sensitivity of ≈1.583 × 10−2 kPa−1 at pressures (<50 kPa) and excellent stability (>1000 cycles). To demonstrate its versatility in HMI, the textile‐based sensor is integrated into a glove for real‐time control of a commercially available prosthetic hand and a drone, as well as for sign language recognition, achieving 95.4% classification accuracy using the random forest (RF) classifier across 10 gestures. Beyond gesture recognition, the sensor measures a range of subtle physiological activities (>0.35 kPa), functioning as a force myograph, esophageal manometry, and in gait analysis, among other strain and pressure‐related applications, highlighting exceptional capabilities for advanced wearable systems. [ABSTRACT FROM AUTHOR]

Motion analysis is of great interest to a variety of applications, such as virtual and augmented reality and medical diagnostics. Hand movement tracking systems, in particular, are used as a human–machine interface. In most cases, these systems are based on optical or acceleration/angular speed sensors. These technologies are already well researched and used in commercial systems. In special applications, it can be advantageous to use magnetic sensors to supplement an existing system or even replace the existing sensors. The core of a motion tracking system is a localization unit. The relatively complex localization algorithms present a problem in magnetic systems, leading to a relatively large computational complexity. In this paper, a new approach for pose estimation of a kinematic chain is presented. The new algorithm is based on spatially rotating magnetic dipole sources. A spatial feature is extracted from the sensor signal, the dipole direction in which the maximum magnitude value is detected at the sensor. This is introduced as the "maximum vector". A relationship between this feature, the location vector (pointing from the magnetic source to the sensor position) and the sensor orientation is derived and subsequently exploited. By modelling the hand as a kinematic chain, the posture of the chain can be described in two ways: the knowledge about the magnetic correlations and the structure of the kinematic chain. Both are bundled in an iterative algorithm with very low complexity. The algorithm was implemented in a real-time framework and evaluated in a simulation and first laboratory tests. In tests without movement, it could be shown that there was no significant deviation between the simulated and estimated poses. In tests with periodic movements, an error in the range of 1° was found. Of particular interest here is the required computing power. This was evaluated in terms of the required computing operations and the required computing time. Initial analyses have shown that a computing time of 3 μ s per joint is required on a personal computer. Lastly, the first laboratory tests basically prove the functionality of the proposed methodology. [ABSTRACT FROM AUTHOR]

Facing the challenge of improving efficiency and stability in industrial processes, this study examines the impact of implementing advanced human-machine interface (HMI) systems, complemented by the internet of things (IoT) and artificial intelligence (AI). The integration of a PLC and HMI-controlled system has resulted in a 22.85% increase in efficiency, stabilizing production and reducing process variability. Tools such as PLCSIM and TIA PORTAL were crucial for validating control logic and programming. Additionally, the study explores the potential of AI and IoT to amplify these benefits, suggesting a significant advancement in automation that could transform operational efficiency and quality in related industries. These findings provide a relevant framework for companies looking to integrate emerging technologies into their operations, promoting continuous improvement and more informed management. [ABSTRACT FROM AUTHOR]

Artificial intelligence enables machines to increase their autonomy. A concept of a bio-intelligent manufacturing machine has been developed. This is characterised by a self-learning expert system and a sensor strategy which are intensively integrated with each other. The machine tool control is at any time completely aware of the process performance, the environmental conditions and the machine status. It exploits further information streams from human operators and other machines in a federated learning approach and is connected to the MES system of the company. The expert system learns from human operators by mere commanding the operation. It is capable to generate new process parameters and deal with incomplete process data by adding the missing information. [ABSTRACT FROM AUTHOR]

Tapping and scrolling are two widely adopted touch modes of content acquisition in mobile apps. We empirically investigate factors that affect users' preferences over these two modes and their subsequent content consumption. We find that higher content quality uncertainty, higher content fit uncertainty, and lower content acquisition cost are the main reasons that scrolling is more likely to be adopted than tapping in content acquisition. We model users' content consumption as a two-stage decision process consisting of the choice stage and interaction stage. Compared to tapping, scrolling can significantly increase users' likelihood of choosing specific content. It can also increase (reduce) users' likelihood of interacting with content when it generates a positive (negative) confirmation on content quality and fit. Our work sheds new light on the effects of tapping and scrolling on users' content acquisition and content consumption. These findings provide practical implications for platforms' content management strategies. [ABSTRACT FROM AUTHOR]

A human–machine interface (HMI)‐based visual interface along with an embedded system was developed to real‐time measure, display, and store the various tractor–implement performance parameters, that is, geoposition, depth, speed, slip, fuel consumption, draft, and power take‐off (PTO) torque. The developed system consists of various commercially available (global positioning system, rotary potentiometer, tension/compression load cell, Hall‐effect sensor, flow meter, and strain gauge) sensors/transducers to measure the performance parameters. A strain‐gauge‐based special type of transducer was also developed for measuring the PTO torque acting on the implement and the output of the transducer was transferred to the virtual interface‐based data acquisition system using radio frequency‐based modules. Along with the sensors, the developed system is composed of a microcontroller to process the data received from sensors, an HMI‐assisted smart touch screen to display the output, and a secure digital card module to store the processed data. The developed visual interface of the embedded system comprises multiple operator‐friendly touch screens and each screen was designed with a graphical user interface for the visual presentation of the tractor–implement performance parameters. The system was installed in the TAFE Samrat 4410 tractor and tested under various field operations. Sensors employed in the system were calibrated for obtaining precise measurement, and excellent linearity with a high correlation between actual and measured variables. Under various field operations (plowing and tillering), a maximum error of 15% (except PTO torque) was found between parameters measured with the developed system and manual/predicted measurement. The field results fortify the acceptable accuracy of the developed system. The developed system will be helpful for researchers or students to study the matching of tractor–implement combinations. [ABSTRACT FROM AUTHOR]

Japan is becoming a super-aging society, where there are concerns regarding traffic accidents caused by elderly drivers. This study aimed to investigate the effectiveness of a human–machine interface (HMI) for assessing elderly drivers' behaviour. The drivers' behaviour was investigated when a pedestrian crossed while the drivers turned right at intersections and drove on straight roads, with vehicles travelling in the left lane. We investigated 16 elderly participants by using a driving simulator. The results indicated that the display of pedestrian information increased the closest distance of the vehicle to the crossing pedestrian. Moreover, time to collision increased under conditions where there were three preceding vehicles turning right at an intersection or driving straight on a straight road. In the questionnaire, most participants answered that the HMI was helpful. These findings can serve as the basis while designing an HMI that encourages elderly drivers to notice pedestrians. [ABSTRACT FROM AUTHOR]

Hand paralysis, caused by conditions such as spinal cord injuries, strokes, and arthritis, significantly hinders daily activities. Wearable exo-gloves and telerehabilitation offer effective hand training solutions to aid the recovery process. This study presents the development of lightweight wearable exo-gloves designed for finger telerehabilitation. The prototype uses NiTi shape memory alloy (SMA) actuators to control five fingers. Specialized end effectors target the metacarpophalangeal (MCP), proximal interphalangeal (PIP), and distal interphalangeal (DIP) joints, mimicking human finger tendon actions. A variable structure controller, managed through a web-based Human–Machine Interface (HMI), allows remote adjustments. Thermal behavior, dynamics, and overall performance were modeled in MATLAB Simulink, with experimental validation confirming the model's efficacy. The phase transformation characteristics of NiTi shape memory wire were studied using the Souza–Auricchio model within COMSOL Multiphysics 6.2 software. Comparing the simulation to trial data showed an average error of 2.76°. The range of motion for the MCP, PIP, and DIP joints was 21°, 65°, and 60.3°, respectively. Additionally, a minimum torque of 0.2 Nm at each finger joint was observed, which is sufficient to overcome resistance and meet the torque requirements. Results demonstrate that integrating SMA actuators with telerehabilitation addresses the need for compact and efficient wearable devices, potentially improving patient outcomes through remote therapy. [ABSTRACT FROM AUTHOR]

The electrophysiological interface is essential for understanding electrical activities within biological organisms and human‐machine interaction. However, traditional preformed bioelectrodes show insufficient interface contact with hairy and irregular skin, causing signal attenuation and distortion. Here, an on‐skin‐formed silk protein bioelectrode is reported with a conformable and robust interface, achieving a high signal‐to‐noise ratio for electromyographic signals, showing up to a 38.9% increase compared to traditional ones. The fluid‐gel transition of silk protein is induced by a gelling agent composed of ethanol and sodium alginate, leading to a conformable and seamless interface with hairy and irregular human skin, thus resulting in a high signal‐to‐noise ratio. The developed bioelectrode can record electrophysiological signals stably, even underwater. Additionally, human‐machine interaction using electromyographic signals is designed to precisely control the model car's motion trajectory. This work presents an effective method for developing a seamless and conformable electrophysiological interface in bioelectronics. [ABSTRACT FROM AUTHOR]

Smart electric vehicles (SEVs) hold significant potential in alleviating the energy crisis and environmental pollution. The augmented reality (AR) dashboard, a key feature of SEVs, is attracting considerable attention due to its ability to enhance driving safety and user experience through real-time, intuitive driving information. This study innovatively integrates Kansei engineering, factor analysis, fuzzy systems theory, analytic hierarchy process, grey relational analysis, and factorial experimentation to evaluate AR dashboards' visual imagery and subjective preferences. The findings reveal that designs featuring blue planar and unconventional-shaped dials exhibit the best performance in terms of visual imagery. Subsequent factorial experiments confirmed these results, showing that drivers most favor blue-dominant designs. Furthermore, in unconventional-shaped dial designs, the visual effect of vertical 3D is more popular with drivers than horizontal 3D, while the opposite is true in round dials. This study provides a scientific evaluation method for assessing the emotional experience of AR dashboard interfaces. Additionally, these findings will help reduce the subjectivity in interface design and enhance the overall competitiveness of SEV vehicles. [ABSTRACT FROM AUTHOR]

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This article presents the design and implementation of an innovative human–machine interface (HMI) in mixed reality for a robot model operating within Robot Operating System 2 (ROS 2). The interface is specifically developed for compatibility with Microsoft HoloLens 2 hardware and leverages the Unity game engine alongside the Mixed Reality Toolkit (MRTK) to create an immersive mixed reality application. The project uses the Turtlebot 3 Burger model robot, simulated within the Gazebo virtual environment, as a representative mechatronic system for demonstration purposes. Communication between the mixed reality application and ROS 2 is facilitated through a publish–subscribe mechanism, utilizing ROS TCP Connector for message serialization between nodes. This interface not only enhances the user experience by allowing for the real-time monitoring and control of the robotic system but also aligns with the principles of Industry 5.0, emphasizing human-centric and inclusive technological advancements. The practical outcomes of this research include a fully functional mixed reality application that integrates seamlessly with ROS 2, showcasing the potential of mixed reality technologies in advancing the field of industrial automation and human–machine interaction. [ABSTRACT FROM AUTHOR]

Integrating autonomous vehicles (AVs) into public roads presents profound implications for pedestrian safety and the broader acceptance of this emerging technology. This work examines the complex interactions between AVs and pedestrians, a dynamic influenced by the variability of pedestrian behaviors and the absence of traditional communication mechanisms, such as eye contact and gestures, commonly relied upon in human-driven scenarios. Given the nascent stage of AV deployment, this research addresses the challenges of evaluating AV−pedestrian interactions amid safety concerns and technological limitations. We review and synthesize global research on pedestrian behavior in the context of AV technology to track changes in pedestrians' acceptance over time and identify the factors driving these shifts. Additionally, this review incorporates insights from transportation authorities to highlight potential safety issues and the need for innovative communication strategies that ensure safe interactions between pedestrians and AVs. By analyzing these factors, the research aims to contribute to the development of guidelines and communication protocols that enhance pedestrian safety and facilitate the integration of AVs into urban environments. [ABSTRACT FROM AUTHOR]

External human–machine interfaces (eHMIs) may be useful for communicating the intention of an autonomous vehicle (AV) to road users, but it is questionable whether an eHMI is effective in guiding the actual behavior of road users, as intended by the eHMI. To address this question, we developed a Unity game in which the player drove a conventional car and the AVs were operating with eHMIs. We examined the effects of different eHMI designs—namely, textual, graphical, and anthropomorphic—on the driving behavior of a player in a gaming environment, and compared it to one with no eHMI. Participants (N = 18) had to follow a specified route, using the typical keys for PC games. They encountered AVs with an eHMI placed on the rear window. Five scenarios were simulated for the specified routes: school safety zone; traffic island; yellow traffic light; waiting for passengers; and an approaching e-scooter. All scenarios were repeated three times (a total of 15 sessions per participant), and the eHMI was randomly generated among the four options. The behavior was determined by observing the number of violations in combination with keystrokes, fixations, and saccades. Their subjective evaluations of the helpfulness of the eHMI and their feelings about future AVs revealed their attitudes. Results showed that a total of 45 violations occurred, the most frequent one being exceeding the speed limit in the school safety zones (37.8%) when the eHMI was textual, anthropomorphic, graphical, and when there was no eHMI, in decreasing order; the next was collisions (33.3%), when the eHMI was anthropomorphic, none, or graphical. The rest were ignoring the red light (13.3%), crossing the stop line (13.3%), and violation of the central line (2.2%). More violations occurred when the eHMI was set to anthropomorphic, followed by no eHMI, graphical, and textual eHMI. The helpfulness of the five scenarios scored high (5.611 to 6.389) on a seven-point Likert scale, and there was no significant difference for the scenarios. Participants felt more positive about the future of AVs after their gaming experience (p = 0.049). We conclude that gazing at unfamiliar and ambiguous information on eHMIs may cause a loss of driver attention and control. We propose an adaptive approach in terms of timing and distance depending on the behavior of other road users. [ABSTRACT FROM AUTHOR]

IntroductionRehabilitation devices assist individuals with movement disorders by supporting daily activities and facilitating effective rehabilitation training. Accurate and early motor intention detection is vital for real-time device applications. However, traditional methods of motor intention detection often rely on single-mode signals, such as EEG or EMG alone, which can be limited by low signal quality and reduced stability. This study proposes a multimodal fusion method based on EEG–EMG functional connectivity to detect sitting and standing intentions before movement execution, enabling timely intervention and reducing latency in rehabilitation devices.MethodsEight healthy subjects and five spinal cord injury (SCI) patients performed cue-based sit-to-stand and stand-to-sit transition tasks while EEG and EMG data were recorded simultaneously. We constructed EEG–EMG functional connectivity networks using data epochs from the 1.5-s period prior to movement onset. Pairwise spatial filters were then designed to extract discriminative spatial network topologies. Each filter paired with a support vector machine classifier to classify future movements into one of three classes: sit-to-stand, stand-to-sit, or rest. The final prediction was determined using a majority voting scheme.ResultsAmong the three functional connectivity methods investigated—coherence, Pearson correlation coefficient and mutual information (MI)—the MI-based EEG–EMG network showed the highest decoding performance (94.33%), outperforming both EEG (73.89%) and EMG (89.16%). The robustness of the fusion method was further validated through a fatigue training experiment with healthy subjects. The fusion method achieved 92.87% accuracy during the post-fatigue stage, with no significant difference compared to the pre-fatigue stage (p > 0.05). Additionally, the proposed method using pre-movement windows achieved accuracy comparable to trans-movement windows (p > 0.05 for both pre- and post-fatigue stages). For the SCI patients, the fusion method showed improved accuracy, achieving 87.54% compared to single- modality methods (EEG: 83.03%, EMG: 84.13%), suggesting that the fusion method could be promising for practical rehabilitation applications.ConclusionOur results demonstrated that the proposed multimodal fusion method significantly enhances the performance of detecting human motor intentions. By enabling early detection of sitting and standing intentions, this method holds the potential to offer more accurate and timely interventions within rehabilitation systems.

Abstract Wearable electronics revolutionize human–machine interfaces (HMIs) for robotic or prosthetic control. Yet, the challenge lies in eliminating conventional rigid and impermeable electronic components, such as batteries, while considering the comfort and usability of HMIs over prolonged periods. Herein, a self‐powered, flexible, and breathable HMI is developed based on piezoelectric sensors. This interface is designed to accurately monitor subtle changes in body and muscle movements, facilitating effective communication and control of robotic prosthetic hands for various applications. Utilizing engineered porous structures within the polymeric material, the piezoelectric sensor demonstrates a significantly enhanced sensitivity, flexibility, and permeability, highlighting its outstanding HMI applications. Furthermore, the developed control algorithm enables a single sensor to comprehensively control robotic hands. By successfully translating piezoelectric signals generated from bicep muscle movements into Morse Code, this HMI serves as an efficient communication device. Additionally, the process is demonstrated by illustrating the execution of the daily task of “drinking a cup of water” using the developed HMI to enable the control of a human‐interactive robotic prosthetic hand through the detection of bicep muscle movements. Such HMIs pave the way toward self‐powered and comfortable biomimetic systems, making a significant contribution to the future evolution of prosthetics.

Integrating autonomous vehicles (AVs) into public roads presents profound implications for pedestrian safety and the broader acceptance of this emerging technology. This work examines the complex interactions between AVs and pedestrians, a dynamic influenced by the variability of pedestrian behaviors and the absence of traditional communication mechanisms, such as eye contact and gestures, commonly relied upon in human-driven scenarios. Given the nascent stage of AV deployment, this research addresses the challenges of evaluating AV−pedestrian interactions amid safety concerns and technological limitations. We review and synthesize global research on pedestrian behavior in the context of AV technology to track changes in pedestrians’ acceptance over time and identify the factors driving these shifts. Additionally, this review incorporates insights from transportation authorities to highlight potential safety issues and the need for innovative communication strategies that ensure safe interactions between pedestrians and AVs. By analyzing these factors, the research aims to contribute to the development of guidelines and communication protocols that enhance pedestrian safety and facilitate the integration of AVs into urban environments.

External human–machine interfaces (eHMIs) may be useful for communicating the intention of an autonomous vehicle (AV) to road users, but it is questionable whether an eHMI is effective in guiding the actual behavior of road users, as intended by the eHMI. To address this question, we developed a Unity game in which the player drove a conventional car and the AVs were operating with eHMIs. We examined the effects of different eHMI designs—namely, textual, graphical, and anthropomorphic—on the driving behavior of a player in a gaming environment, and compared it to one with no eHMI. Participants (N = 18) had to follow a specified route, using the typical keys for PC games. They encountered AVs with an eHMI placed on the rear window. Five scenarios were simulated for the specified routes: school safety zone; traffic island; yellow traffic light; waiting for passengers; and an approaching e-scooter. All scenarios were repeated three times (a total of 15 sessions per participant), and the eHMI was randomly generated among the four options. The behavior was determined by observing the number of violations in combination with keystrokes, fixations, and saccades. Their subjective evaluations of the helpfulness of the eHMI and their feelings about future AVs revealed their attitudes. Results showed that a total of 45 violations occurred, the most frequent one being exceeding the speed limit in the school safety zones (37.8%) when the eHMI was textual, anthropomorphic, graphical, and when there was no eHMI, in decreasing order; the next was collisions (33.3%), when the eHMI was anthropomorphic, none, or graphical. The rest were ignoring the red light (13.3%), crossing the stop line (13.3%), and violation of the central line (2.2%). More violations occurred when the eHMI was set to anthropomorphic, followed by no eHMI, graphical, and textual eHMI. The helpfulness of the five scenarios scored high (5.611 to 6.389) on a seven-point Likert scale, and there was no significant difference for the scenarios. Participants felt more positive about the future of AVs after their gaming experience (p = 0.049). We conclude that gazing at unfamiliar and ambiguous information on eHMIs may cause a loss of driver attention and control. We propose an adaptive approach in terms of timing and distance depending on the behavior of other road users.

The impact of global population aging on older adults' health and emotional well-being is examined in this study, emphasizing innovative technological solutions to address their diverse needs. Changes in physical and mental functions due to aging, along with emotional challenges that necessitate attention, are highlighted. Gaze estimation and interactive art are utilized to develop an interactive system tailored for elderly users, where interaction is simplified through eye movements to reduce technological barriers and provide a soothing art experience. By employing multi-sensory stimulation, the system aims to evoke positive emotions and facilitate meaningful activities, promoting active aging. Named "Natural Rhythm through Eyes", it allows for users to interact with nature-themed environments via eye movements. User feedback via questionnaires and expert interviews was collected during public demonstrations in elderly settings to validate the system's effectiveness in providing usability, pleasure, and interactive experience for the elderly. Key findings include the following: (1) Enhanced usability of the gaze estimation interface for elderly users. (2) Increased enjoyment and engagement through nature-themed interactive art. (3) Positive influence on active aging through the integration of gaze estimation and interactive art. These findings underscore technology's potential to enhance well-being and quality of life for older adults navigating aging challenges. [ABSTRACT FROM AUTHOR]

AbstractCommunicating effectively with autonomous vehicles requires contextualized visual and auditory cues to ensure clear message delivery. Evaluating the user experience involves assessing which types of information can be safely reacted to without additional monitoring and how it is presented. Validating the visual and auditory cues supports the driver’s course of action. This study investigates message types and preferred modalities of driver-to-driver communication via vehicle-to-everything (V2X) in advanced driver assistance systems technologies for autonomous and manual driving and proposes efficient ways to respond to event situations. Four modalities, including baseline and three message types with different information were proposed to investigate the information required by drivers. Results indicate that providing notifications during autonomous driving is more helpful and less workload-intensive than during manual driving. Most notifications were highly visible and easy to recognize. Although behavioral messages in both autonomous and manual driving enhance usability, providing advice and behavioral messages is safer for autonomous driving. Designing V2X notification information based on future events is vital because of its highly pragmatic nature. [ABSTRACT FROM AUTHOR]

We propose a hands-free control system for a human-guided smart stroller. The proposed method uses real-time peer-to-peer localization technology of the human and stroller to realize an intuitive hands-free control system based on the relative position between the human and the stroller. The control method is also based on functional and mechanical safety to ensure the safety of the stroller's occupant (child) and the pilot (parent) during locomotion. In this paper, first, we present a preliminary investigation of the humans' preference for the relative position in the context of hands-free guided strollers. Then, we present the control method and a prototype implemented with an electric wheelchair and UWB sensors for localization. We present an experimental evaluation of the proposed method with 14 persons walking with the developed prototype to investigate the usability and soundness of the proposed method compared to a remote joystick and manual operation. The evaluation experiments were conducted in an indoor environment and revealed that the proposed method matches the performance of joystick control but does not perform as well as manual operation. Notably, for female participants, the proposed method significantly surpasses joystick performance and achieves parity with manual operation, which shows its efficacy and potential for a smart stroller. Also, the results revealed that the proposed method significantly decreased the user's physical load compared to the manual operation. We present discussions on the controllability, usability, task load, and safety features of the proposed method, and conclude this work with a summary assessment. [ABSTRACT FROM AUTHOR]

In our daily life, flexible flow sensors endow us with a 'sixth sense' capability, i.e. 'touch' the fluids, improving living quality. Although there are kinds of flexible flow sensors developed to implement this capability, they still have insufficient sensitivity and limited intelligent applications in daily life. Biomimetic engineering provides us with a powerful and effective approach to develop highly sensitive and intelligent flow sensing systems served in our life, comparable to that in creatures. Here, in this review, we present a comprehensive review of recent studies on the flexible flow sensors for human intelligent life. Firstly, we briefly introduce the excellent flow sensing systems selected by nature, and typical design strategies of artificial flexible flow sensors. Furthermore, we collect and exhibit kinds of flexible flow sensors and their applications in intelligent and digital life. Finally, we discuss the challenges and future perspectives of the flexible flow sensor for the metaverse applications. [ABSTRACT FROM AUTHOR]

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By deconstructing the human–machine interface across China's economic history, this commentary on Luger and Schwarze's paper highlights the critical need to explore gender dynamics in post-industrial cities through the human–machine interface. We suggest two new concepts, subjectivity and deep interface, which may offer a way forward for us to move deeper into post-industrial cities for a more intricate understanding of the enduring inequalities shaping these urban landscapes. [ABSTRACT FROM AUTHOR]

Monitoring and classifying cognitive workload in real time is vital for optimizing human–machine interactions and enhancing performance while ensuring safety, particularly in industrial scenarios. Considering this significance, the authors aim to formulate a cognitive workload monitoring system (CWMS) by leveraging the deep gated neural network (DGNN), a hybrid model integrating bi-directional long short-term memory (Bi-LSTM) and gated recurrent unit (GRU) networks. In our experimental setup, each of the four virtual users is equipped with a Raspberry Pi Zero W module to ensure efficient data transmission, thereby enhancing the reliability and efficacy of the monitoring process. This seamless monitoring framework utilizes the constrained application protocol (CoAP) and the Things Board platform to evaluate cognitive workload in real time. The most popular EEG benchmark dataset, the STEW is utilized for workload classification in this study. We employ the short-time Fourier transformation (STFT) to extract frequency bands corresponding to users in both high and low cognitive workload modes. The proposed DGNN models achieve a perfect accuracy of 99.45%, outperforming every previous state-of-the-art model. We meticulously monitored critical parameters, including latency, classification processing time, and cognitive workload levels. This research demonstrates the importance of continuous monitoring for increasing productivity and safety in industries by introducing a novel method of real-time cognitive workload monitoring. The implementation codes for each experiment are documented and made available for reproducibility. [ABSTRACT FROM AUTHOR]

Remote condition monitoring (RCM) aims to ensure the availability of railway assets. Previous work has indicated the importance of a user-centred RCM design approach based on cognitive principles, but there has been no known demonstration of the application of these principles. The following paper takes this theory-based approach and applies it to the design of an RCM system for the rail pantograph/Overhead Line (OHL) system. The paper first presents a high-level conceptual architecture, based on four stages of cognitive decision-making (notification, acceptance, analysis and clearance), linked to the wider monitoring architecture. Second, the paper uses cognitive principles to propose demonstration Human–Machine Interface designs for the OHL system. These HMIs were presented in an evaluation with subject matter experts. The outcomes of the process generated user-centred design recommendations for RCM. Furthermore, the evaluation suggested the importance of multiple paths through the HMI dependent on the type and urgency of fault. Finally, the outcomes of the evaluation also highlighted the importance of considering context when deploying user-centred RCM. [ABSTRACT FROM AUTHOR]

This paper investigates the application of fuzzy logic to enhance situational awareness in Advanced Driver Assistance Systems (ADAS). Situational awareness is critical for drivers to respond appropriately to dynamic driving scenarios. As car automation increases, monitoring situational awareness ensures that drivers can effectively take control of the vehicle when needed. Our study explores whether fuzzy logic can accurately assess situational awareness using a set of 14 critical predictors categorized into time decision, criticality, eye-related metrics, and driver experience. We based our work on prior research that used machine learning (ML) models to achieve high accuracy. Our proposed fuzzy logic system aims to match the predictive accuracy of ML models while providing additional benefits in terms of interpretability and robustness. This approach emphasizes a fresh perspective on situational awareness within ADAS, potentially improving safety and efficiency in real-world driving scenarios. [ABSTRACT FROM AUTHOR]

The design of automotive human–machine interfaces (HMIs) for global consumers' needs to cater to a broad spectrum of drivers. This paper comprises benchmark studies and explores how users from international markets—Germany, China, and the United States—engage with the same automotive HMI. In real driving scenarios, N = 301 participants (premium vehicle owners) completed several tasks using different interaction modalities. The multi-method approach included both self-report measures to assess preference and satisfaction through well-established questionnaires and observational measures, namely experimenter ratings, to capture interaction performance. We observed a trend towards lower preference ratings in the Chinese sample. Further, interaction performance differed across the user groups, with self-reported preference not consistently aligning with observed performance. This dissociation accentuates the importance of integrating both measures in user studies. By employing benchmark data, we provide insights into varied market-based perspectives on automotive HMIs. The findings highlight the necessity for a nuanced approach to HMI design that considers diverse user preferences and interaction patterns. [ABSTRACT FROM AUTHOR]