Human-machine Interaction Technologies Research Articles

Challenges and Trends in Brain-Computer Interface Technology

Brain-Computer Interface (BCI) technology has become one of the focal points of scientific research in the modern world. Researchers from all corners of the globe have made significant contributions to this field, driving the continuous advancement of BCI technology. By 2025, Brain-Computer Interface (BCI) technology has become a groundbreaking human-machine interaction technology, enabling communication between the human brain and the external world, no longer a fantasy from science fiction but a tangible reality. It has now been widely applied across numerous fields, including communication, movement control, environment control, neurorehabilitation, and others.This paper first presents the basic principles of brain-computer interface (BCI) technology and provides a detailed classification of BCIs. It then reviews and enumerates the latest advancements in BCI research, exploring the various steps that form a standard BCI, including signal acquisition, preprocessing, feature extraction, and the control interface, while also investigating several mathematical algorithms. The aim is to gain a deeper understanding of BCI technology. Finally, the paper concludes with a summary and outlook on the future development prospects of this technology.

Enhancing Oral English Proficiency Through Human-Computer Interaction

Human-Machine Interaction (HMI) technology has revolutionized the landscape of oral English education, offering new possibilities for improving learning efficiency and experiences. This paper presents an innovative teaching system that integrates real-time speech recognition and feedback capabilities with advanced natural language processing (NLP) and machine learning algorithms. The system is designed to provide personalized learning paths based on learners' performance data, ensuring tailored resources and guidance. Emphasizing user experience and interactive design, it aims to stimulate learner interest and motivation. Research findings indicate significant improvements in students' pronunciation, fluency, and grammar, alongside high levels of user satisfaction. However, challenges remain in fully replicating genuine human interactions and addressing technical limitations. Future work will focus on enhancing conversational abilities, personalization, and multimodal feedback mechanisms to better prepare students for real-world communication scenarios.

Flexible hybrid self-powered piezo-triboelectric nanogenerator based on BTO-PVDF/PDMS nanocomposites for human machine interaction

As flexible and wearable electronics play more and more important role in smart watches, smart glass and virtual reality, and the power supply to the wearable electronics have been revealed more attentions for long-term usage and continuous healthy monitoring. To overcome the challenge, flexible self-powered BTO-PVDF/PDMS piezoelectric-triboelectric electric hybrid generators (BPP-HNG) are developed to human gesture monitoring and human machine interaction (HMI) application without external power supply. BPP-HNG based on BTO-PVDF and PDMS films are prepared by sol–gel and spin-coating method. When the BTO content is 20 wt.%, BPP-HNG exhibits better electrical performance with an output voltage of 20.51 V. A real-time gesture monitoring system is designed and developed to human machine interaction, which is able to control the motion of robot finger through BPP-HNG. BPP-HNG could monitor and recognize various gestures in real time, enabling synchronization between the human hand and the robot’s hand. With the convergence of AI technology and big data, BPP-HNG based HMI technology is expected to realize the potential of smarter and more intuitive interactions.

Determinants of human-machine interaction technology usage: An automated machine learning approach

Determinants of human-machine interaction technology usage: An automated machine learning approach

Defect Engineered Few Layered MoS2 for Human-Machine Interface.

Ultrasensitive flexible devices have huge applications in many areas, like healthcare monitoring, human-machine interaction, and wearable technology. However, improving the sensitivity of these devices is still challenging. In the current study, a flexible non-contact sensing system is designed with a human-machine interface using defect-engineered, few-layered Molybdenum disulfide (MoS2). The fabricated sensors show high sensitivity when monitoring proximity, humidity, and in-plane applied strain. The output performance demonstrates the influence of surface defects, which greatly impact the average surface charge of the nanosheets. The experimental measurements and in-detail density functional theoretical (DFT) calculation further reveal surface charge variations on the basal planes that correlate with topographic defects and increase sensitivity. The electrical signals for different gestures of human hands are used to illustrate the identification of multidirectional bending and sliding events. These findings will contribute to understanding the effect of surface defects that play an important role in sensing applications with human-machine interface.

Digital twin technology in biomechanics: Revolutionizing human movement analysis and rehabilitation practices

Lower limb rehabilitation exoskeletons are wearable assistive rehabilitation devices designed to protect and aid patients in rehabilitation training. However, traditional lower limb rehabilitation exoskeleton systems are limited by information acquisition technology, mostly adopting passive training with fixed trajectories and lacking real-time motion data interaction, resulting in deficiencies in the overall system’s safety and autonomy. Based on this, this study proposes a lower limb rehabilitation exoskeleton system based on digital twin technology. By leveraging digital twin technology, the system achieves a deep integration of virtual and physical spaces, improves human-machine information interaction technology, and enhances the effectiveness of rehabilitation training. Experimental results demonstrate that the system can achieve personalized gait trajectory planning and real-time motion data interaction, providing a new solution for lower limb rehabilitation.

Gesture-controlled reconfigurable metasurface system based on surface electromyography for real-time electromagnetic wave manipulation.

Gesture recognition plays a significant role in human-machine interaction (HMI) system. This paper proposes a gesture-controlled reconfigurable metasurface system based on surface electromyography (sEMG) for real-time beam deflection and polarization conversion. By recognizing the sEMG signals of user gestures through a pre-trained convolutional neural network (CNN) model, the system dynamically modulates the metasurface, enabling precise control of the deflection direction and polarization state of electromagnetic waves. Experimental results demonstrate that the proposed system achieves high-precision electromagnetic wave manipulation, in response to different gestures. This system has significant potential applications in intelligent device control, virtual reality systems, and wireless communication technology, and is expected to contribute to the advancement and innovation of HMI technology by integration of more advanced metasurfaces and sEMG processing technologies.

How to determine hands’ vibration perception thresholds – a systematic review

The vibration perception threshold (VPT) is the minimum amplitude required for conscious vibration perception. VPT assessments are essential in medical diagnostics, safety, and human–machine interaction technologies. However, factors like age, health conditions, and external variables affect VPTs. Various methodologies and distinct procedures have been used to access VPTs, leading to challenges in establishing standardized protocols. Following the PRISMA methodology, this systematic review was conducted to answer the research question: “How are vibration perception thresholds assessed on the glabrous skin of the hands and fingers of healthy humans?” Searches were conducted across five databases to locate recent studies reporting data from VPT assessments, published in English, between 2012 and 2023. Thirty-nine studies met the inclusion criteria. Data on study goals and various methodological aspects was categorized and analyzed. Information gaps were identified, and this review offers recommendations for future studies to enhance standardization and facilitate data comparison. This review also suggests directions for future research, aiming to improve our understanding of how humans perceive haptic information.

Self‐Powered, Raw Material‐Based Sensor Arrays with Minimized Signal Channels for Human‐Machine Interaction

Abstract Sensor arrays are essential for effective human‐machine interaction (HMI), enabling systems to perceive, interpret, and respond to human actions. However, traditional sensor arrays often involve complex manufacturing processes and excessive wiring due to point‐to‐point connections. To address these challenges, a type of self‐powered, low‐cost sensor array is described that minimizes these issues. Utilizing the principles of contact electrification and electrostatic induction, the array consists of two or more interconnected electrostatic induction units. When a unit is triggered, it generates a unique voltage signal relative to other connected units, resulting in diverse signals with specific waveforms and peak magnitudes. This diversity allows the sensor array to output multiple addressable signals through a limited number of channels or a single channel, enhancing efficiency and simplicity. The versatility of these sensor arrays in various HMI applications is demonstrated, including game joysticks, electronic skins, robot controllers, and leaf‐based electronic piano keyboards, showcasing their potential to transform HMI technology.

Integrated Mechanical and Electronic Design and Comfort Optimization in Smart Furniture

Smart furniture, as an important component of modern home environments, integrates advanced mechanical design and electronic control technologies, enhancing the quality of life for users through automatic sensing and adjustment. With the development of technology, the functionality of smart furniture has continuously expanded, and comfort has become a central focus in its design. This paper explores the integrated mechanical and electronic design in smart furniture, examining its structural design, electronic system integration, and the application of human-machine interaction technology in comfort optimization. The study indicates that the high integration of mechanical and electronic systems not only improves the functionality of the furniture but also optimizes the user experience through technologies such as smart adjustments and personalized control. By using comfort evaluation standards and optimization models, combined with innovative applications of material selection and environmental sensing technologies, smart furniture can adaptively adjust according to different user needs and environmental conditions, thereby achieving optimal comfort. This paper provides a theoretical basis for the design and optimization of smart furniture and points out the direction for future development.

Enhancing UAV Crew Performance and Safety: A Technology and Innovation Management Perspective

The integration of Unmanned aerial vehicles (UAVs) into various sectors underscores the importance of optimizing human factors to ensure operational efficiency, safety, and mission success. This study presents a comprehensive bibliometric analysis of the literature on human factors in UAV operations, focusing on cognitive workload, situational awareness, decision-making, ergonomic design, and human-machine interaction. The analysis was conducted using the WoS, covering publications from 2000 to 2023. Key findings include a significant increase in research output over the last decade, highlighting the growing interest and investment in UAV technology and human factors. Influential authors such as Rosenstein (2006), Patterson (2010), Reason (1990), Wiegmann (2001), and Shappell (2007), along with institutions like Beijing University of Posts and Telecommunications, Southeast University China, Xidian University, and Nanjing University of Aeronautics and Astronautics, have emerged as leaders in this field, contributing to advancements in ergonomic design and decision-making processes. Notably, there is a lack of comprehensive studies addressing the long-term cognitive workload effects on UAV operators and the development of standardized ergonomic guidelines tailored specifically for UAV operation environments. The integration of advanced human-machine interaction technologies remains underexplored, indicating a need for further research in this area. By highlighting these gaps, the analysis provides a nuanced understanding of current research dynamics, offering valuable implications for UAV operators, regulators, and policymakers. These findings are pivotal for advancing the field and guiding future research initiatives aimed at enhancing crew performance and safety in UAV operations.

Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception.

In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.

Communication Styles of Human-Machine Interaction in the ChatGPT

This research aims to analyze the communication style of Human-machine interaction based on ChatGPT 3.5 to understand the pros and cons, as well as potential improvements in the implementation of this technology. Humans have limitations in processing complex information. The emergence of machines helps humans complete work, and many machine features provide solutions. This research found out human-machine interaction, focuses on communication and using cooperative principles that have four maxims: maxim quantity, maxim quality, maxim relation, and maxim manner are some of the divisions that will be sought in human-machine interaction. The research uses a qualitative method by using a chatbot as a GPT version 3.5 chat that is then given a command. Then, the data is collected and analyzed. From this analysis, human-machine interaction qualitatively has good performance but has a deficiency in maximum quantity due to too much information given. The outcome of the violation of the maxim quantity that stands out requires self-interpretation where the statements correspond to the question. This research is expected to help develop human-machine interaction technology primarily in business.

ANKLE JOINT MOTION RECOGNITION SYSTEM AND ALGORITHM OPTIMIZATION BASED ON PLANTAR PRESSURE

Due to the current focus of research on ankle rehabilitation robots on structural design, there is still limited research on ankle human–machine interaction technology. In order to enable rehabilitation robots to conduct personalized rehabilitation training based on patients’ ankle movement intentions, we propose a new ankle motion recognition method based on plantar pressure. First, we designed a plantar pressure collection system based on array sensors. Then, we collected nine types of ankle joint motion pressure data from five volunteers and conducted algorithm selection, data processing, and algorithm optimization. Finally, we proposed a small sample optimization algorithm based on support vector machine, with an average recognition rate of 93.16%. The recognition method proposed in this paper can be combined with ankle rehabilitation robots to achieve active rehabilitation functions, laying the foundation for the clinical application of active rehabilitation technology.

Frequency-encoded eye tracking smart contact lens for human–machine interaction

Eye tracking techniques enable high-efficient, natural, and effortless human-machine interaction by detecting users’ eye movements and decoding their attention and intentions. Here, a miniature, imperceptible, and biocompatible smart contact lens is proposed for in situ eye tracking and wireless eye-machine interaction. Employing the frequency encoding strategy, the chip-free and battery-free lens successes in detecting eye movement and closure. Using a time-sequential eye tracking algorithm, the lens has a great angular accuracy of <0.5°, which is even less than the vision range of central fovea. Multiple eye-machine interaction applications, such as eye-drawing, Gluttonous Snake game, web interaction, pan-tilt-zoom camera control, and robot vehicle control, are demonstrated on the eye movement model and in vivo rabbit. Furthermore, comprehensive biocompatibility tests are implemented, demonstrating low cytotoxicity and low eye irritation. Thus, the contact lens is expected to enrich approaches of eye tracking techniques and promote the development of human-machine interaction technology.

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.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications.

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

A novel online prediction method for vehicle cabin temperature and passenger thermal sensation

A novel online prediction method for vehicle cabin temperature and passenger thermal sensation

Perceived interactivity in real estate APP increases consumers' psychological well-being: a moderated mediation model

PurposeThis study aims to empirically analyse the influence mechanism of perceived interactivity in real estate APP which affects consumers' psychological well-being. With the growing application of human–machine interaction in real estate APP, it is crucial to utilize human–machine interaction to stimulate perceived interactivity between humans and machines to positively impact consumers' psychological well-being and sustainable development of real estate APP. However, it is unclear whether perceived interactivity improves consumers' psychological well-being.Design/methodology/approachThis study proposes and examines a theoretical model grounded in the perceived interactivity theory, considers the relationship between perceived interactivity and consumers' psychological well-being and explores the mediating effect of perceived value and the moderating role of privacy concerns. It takes real estate APP as the research object, analyses the data of 568 consumer samples collected through questionnaires and then employs structural equation modelling to explore and examine the proposed theoretical model of this study.FindingsThe findings are that perceived interactivity (i.e. human–human interaction and human–information interaction) positively influences perceived value, which in turn affects psychological well-being, and that perceived value partially mediates the effect of perceived interaction on psychological well-being. More important findings are that privacy concerns not only negatively moderate human–information interaction on perceived value, but also negatively moderate the indirect effects of human–information interaction on users' psychological well-being through perceived value.Originality/valueThis study expands the context on perceived interaction and psychological well-being in the field of real estate APP, validating the mediating role and boundary conditions of perceived interactivity created by human–machine interaction on consumers' psychological well-being, and suggesting positive implications for practitioners exploring human–machine interaction technologies to improve the perceived interaction between humans and machines and thus enhance consumer psychological well-being and span sustainable development of real estate APP.

High-Performance Hydrogel Sensors Enabled Multimodal and Accurate Human-Machine Interaction System for Active Rehabilitation.

Human-machine interaction (HMI) technology shows an important application prospect in rehabilitation medicine, but it is greatly limited by the unsatisfactory recognition accuracy and wearing comfort. Here, this work develops a fully flexible, conformable, and functionalized multimodal HMI interface consisting of hydrogel-based sensors and a self-designed flexible printed circuit board. Thanks to the component regulation and structural design of the hydrogel, both electromyogram (EMG) and forcemyography (FMG) signals can be collected accurately and stably, so that they are later decoded with the assistance of artificial intelligence (AI). Compared with traditional multichannel EMG signals, the multimodal human-machine interaction method based on the combination of EMG and FMG signals significantly improves the efficiency of human-machine interaction by increasing the information entropy of the interaction signals. The decoding accuracy of the interaction signals from only two channels for different gestures reaches 91.28%. The resulting AI-powered active rehabilitation system can control a pneumatic robotic glove to assist stroke patients in completing movements according to the recognized human motion intention. Moreover, this HMI interface is further generalized and applied to other remote sensing platforms, such as manipulators, intelligent cars, and drones, paving the way for the design of future intelligent robot systems.