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Abstract\nIMPLICATIONS FOR REHABILITATIONBackground: Knowledge-to-action gap exists in delivering physical activity (PA) to people with developmental disabilities via online platforms. Although web-based platforms have great potential in facilitating the delivery of PA for this target group, the lack of knowledge regarding web accessibility poses a challenge in accessing PA-related information online.Objective: This study evaluates the delivery of PA in terms of web accessibility. It also aims to identify barriers and facilitators in delivering PA knowledge to people with developmental disabilities online to improve web accessibility for the target user group.Methods: The study employs a concurrent nested design incorporating both quantitative (web usability questionnaire) and qualitative data (in-depth interviews). Fifteen pairs of individuals consisting of a person with developmental disabilities and a primary caregiver participated in the study, and three web-based platforms were selected for web accessibility tests and in-depth interviews.Results: The nested analysis provides a quantitative comparison of web accessibility and identifies barriers and facilitators of delivering PA for the target user group from the web accessibility perspective. Conclusion: The study findings could inform the development of accessible online platforms that distribute health-related knowledge to populations with developmental disabilities. Additionally, they could help enhance the design of other platforms intended for these populations.Online platforms have significant potential to improve the delivery of physical activity information to individuals with developmental disabilities.Barriers to accessing online platforms due to poor web usability impede the process of online knowledge translation to end-users.Web usability can be enhanced by implementing appropriate interventions, such as restructuring navigation and redesigning the user interface.Improving web usability will enhance the accessibility of physical activity knowledge for individuals with developmental disabilities, which consequently will positively impact their health.Online platforms have significant potential to improve the delivery of physical activity information to individuals with developmental disabilities.Barriers to accessing online platforms due to poor web usability impede the process of online knowledge translation to end-users.Web usability can be enhanced by implementing appropriate interventions, such as restructuring navigation and redesigning the user interface.Improving web usability will enhance the accessibility of physical activity knowledge for individuals with developmental disabilities, which consequently will positively impact their health. [ABSTRACT FROM AUTHOR]

Machine learning and speech emotion recognition are rapidly evolving fields, significantly impacting human-centered computing. Machine learning enables computers to learn from data and make predictions, while speech emotion recognition allows computers to identify and understand human emotions from speech. These technologies contribute to the creation of innovative human–computer interaction (HCI) applications. Deep learning algorithms, capable of learning high-level features directly from raw data, have given rise to new emotion recognition approaches employing models trained on advanced speech representations like spectrograms and time–frequency representations. This study introduces CNN and LSTM models with GWO optimization, aiming to determine optimal parameters for achieving enhanced accuracy within a specified parameter set. The proposed CNN and LSTM models with GWO optimization underwent performance testing on four diverse datasets—RAVDESS, SAVEE, TESS, and EMODB. The results indicated superior performance of the models compared to linear and kernelized SVM, with or without GWO optimizers. [ABSTRACT FROM AUTHOR]

Speech is one of the most important human bio-signals. However, only some speech production characteristics are fully understood, which are required for a successful speech-based Brain-Computer Interface (BCI). A proper brain-to-speech system that can generate the speech of full sentences intelligibly and naturally poses a great challenge. In our study, we used the SingleWordProduction-Dutch-iBIDS dataset, in which speech and intracranial stereotactic electroencephalography (sEEG) signals of the brain were recorded simultaneously during a single word production task. We apply deep neural networks (FC-DNN, 2D-CNN, and 3D-CNN) on the ten speakers’ data for sEEG-to-Mel spectrogram prediction. Next, we synthesize speech using the WaveGlow neural vocoder. Our objective and subjective evaluations have shown that the DNN-based approaches with neural vocoder outperform the baseline linear regression model using Griffin-Lim. The synthesized samples resemble the original speech but are still not intelligible, and the results are clearly speaker dependent. In the long term, speech-based BCI applications might be useful for the speaking impaired or those having neurological disorders. [ABSTRACT FROM AUTHOR]

This research uses a real-time, human-computer interaction application to examine sign language recognition. This work develops a rule-based hand gesture approach for Indonesian sign language in order to interpret some words using a combination of hand movements, mimics, and poses. The main objective in this study is the recognition of sign language that is based on hand movements made in front of the body with one or two hands, movements which may involve switching between the left and right hand or may be combined with mimics and poses. To overcome this problem, a research framework is developed by coordinating hand gestures with poses and mimics to create features by using holistic MediaPipe. To train and test data in real time, the long short time memory (LSTM) and gated recurrent unit (GRU) approaches are used. The research findings presented in this paper show that hand gestures in real-time interactions are reliably recognized, and some words are interpreted with the high accuracy rates of 94% and 96% for the LSTM and GRU methods, respectively. [ABSTRACT FROM AUTHOR]

Farm Management Information Systems (FMIS) are an important core component of modern farming companies as they allow, e.g. to document activities, create fertilisation plans, and feed digital equipment with required data. Since the entire agricultural sector is an essential component of food production, high standards of resilience should be established in the involved companies. Accordingly, the used software should also be designed with high standards on reliability and crisis capability. Based on a literature review, we found that software for farmers with certain resilience needs is lacking. Thus, we designed and evaluated a new FMIS concept with the user-centred design method. By conducting focus groups (two rounds, total N = 57) in 2017 and 2019, we raised specific front-end and back-end requirements of farmers. Based on the requirements, we developed our concept for both front- and back-end in terms of a decentralised and offline-working FMIS. Through the evaluation with practitioners (N = 16) of the implemented concept, we derived findings and implications, highlighting the need for privacy, stability, and offline-capability, as well as the UI-requirement to be supportive, e.g. with easy to understand icons and terms. [ABSTRACT FROM AUTHOR]

This article investigates under which conditions users on Twitter engage with or react to social bots. Based on insights from human–computer interaction and motivated reasoning, we hypothesize that (1) users are more likely to engage with human-like social bot accounts and (2) users are more likely to engage with social bots which promote content congruent to the user's partisanship. In a preregistered 3 × 2 within-subject experiment, we asked N = 223 US Americans to indicate whether they would engage with or react to different Twitter accounts. Accounts systematically varied in their displayed humanness (low humanness, medium humanness, and high humanness) and partisanship (congruent and incongruent). In line with our hypotheses, we found that the more human-like accounts are, the greater is the likelihood that users would engage with or react to them. However, this was only true for accounts that shared the same partisanship as the user. [ABSTRACT FROM AUTHOR]

In navigation applications, Artificial Intelligence (AI) can improve efficiency and decision making. It is not clear, however, how designers should account for human cooperation when integrating AI systems in navigation work. In a novel empirical study, we examine the transition in the maritime domain towards higher levels of machine autonomy. Our method involved interviewing technology designers (n = 9) and navigators aboard two partially automated ferries (n = 5), as well as collecting field observations aboard one of the ferries. The results indicated a discrepancy between how designers construed human-AI collaboration compared to navigators' own accounts in the field. Navigators reflected upon their role as one of 'backup,' defined by ad-hoc control takeovers from the automation. Designers positioned navigators 'in the loop' of a larger control system but discounted the role of in-situ skills and heuristic decision making in all but the most controlled takeover actions. The discrepancy shed light on how integration of AI systems may be better aligned to human cooperation in navigation. This included designing AI systems that render computational activities more visible and that incorporate social cues that articulate human work in its natural setting. Positioned within the field of AI alignment research, the main contribution is a formulation of human-AI interaction design insights for future navigation and control room work. [ABSTRACT FROM AUTHOR]

Structured light (SL) sensing technology, based on the triangulation principle, is widely employed for depth perception in various applications such as face recognition, human‐computer interaction, machine vision, and more. In this approach, the baseline length, which refers to the distance between the projection device and the camera, is a critical parameter that affects depth measurement and system miniaturization. Recently, metasurfaces have emerged as promising devices for constructing compact optoelectronic systems due to their excellent performance in wavefront modulation of light and ultra‐thin characteristics. In this study, a metasurface‐based monocular SL depth detection scheme is proposed that incorporates a specially designed 3D holographic projection. Under this projected light field, the entire 3D space is labeled, and depth information can be obtained without considering the baseline length, which can further reduce the volume of SL systems. Additionally, a template‐matching algorithm based on correlation coefficient analysis is developed and experimentally demonstrates its feasibility by precisely positioning objects. It is believed that this work opens up a new perspective for compact, lightweight, and flexible design of SL sensing systems, and has a promising future in quantitative detection, automatic location, and industrial measurement. [ABSTRACT FROM AUTHOR]

User trust in Artificial Intelligence (AI) enabled systems has been increasingly recognized and proven as a key element to fostering adoption. It has been suggested that AI-enabled systems must go beyond technical-centric approaches and towards embracing a more human-centric approach, a core principle of the human-computer interaction (HCI) field. This review aims to provide an overview of the user trust definitions, influencing factors, and measurement methods from 23 empirical studies to gather insight for future technical and design strategies, research, and initiatives to calibrate the user-AI relationship. The findings confirm that there is more than one way to define trust. Selecting the most appropriate trust definition to depict user trust in a specific context should be the focus instead of comparing definitions. User trust in AI-enabled systems is found to be influenced by three main themes, namely socio-ethical considerations, technical and design features, and user characteristics. User characteristics dominate the findings, reinforcing the importance of user involvement from development through to monitoring of AI-enabled systems. Different contexts and various characteristics of both the users and the systems are also found to influence user trust, highlighting the importance of selecting and tailoring features of the system according to the targeted user group's characteristics. Importantly, socio-ethical considerations can pave the way in making sure that the environment where user-AI interactions happen is sufficiently conducive to establish and maintain a trusted relationship. In measuring user trust, surveys are found to be the most common method followed by interviews and focus groups. In conclusion, user trust needs to be addressed directly in every context where AI-enabled systems are being used or discussed. In addition, calibrating the user-AI relationship requires finding the optimal balance that works for not only the user but also the system. [ABSTRACT FROM AUTHOR]

Purpose: Mobile learning has seen tremendous growth over the years. Like any other software application, usability is one of the key concerns in its successful implementation. There is a lack of study that provides a comprehensive overview of usability testing of mobile learning applications. Motivated by this a mapping study is conducted. Design/methodology/approach: A systematic mapping study was conducted using 51 papers retrieved from the Scopus database published between 2005 and 2022 that reported on usability testing of mobile learning applications. Findings: The key findings suggest that research is expected to expand in the near future. User-based testing is the commonly used method, while data are collected mainly through questionnaires, observation and interviews. Testing is mainly conducted in a controlled environment. Originality/value: The study provides (1) an evidence-based discussion on usability testing of mobile learning applications, (2) an up-to-date map on state of the art on usability testing of mobile learning applications and (3) providing direction for further research to scientifically strengthen the field. [ABSTRACT FROM AUTHOR]

The dynamic recognition of human posture has very broad application prospects in fields such as human–computer interaction and virtual reality. A new method for dynamic recognition of human posture is proposed within the theoretical framework of deep learning. In our method, historical image information of human posture, current image information of human posture, and association information between each image are included as inputs in the deep learning process. Afterwards, the input information is formed into time series information and feature series information, which are then fused by the attention mechanism module. Finally, the dynamic recognition results of human posture are obtained through convolution operation. Experimental research was conducted on the AMASS dataset, and the results showed that our method can achieve better results in dynamic recognition of human posture, with both indicators superior to the other three methods. At the same time, our method has a fast convergence speed and the loss function remains low and continuously decreases during the deep learning process. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate decision-makers' views on changes that robotics will create in welfare services. The purpose was also to discover what the opportunities and challenges are in human–robot interaction during these changes and how to manage these changes. As a research method, an online survey was used. The survey was sent to Finnish decision-makers (N = 184). They were divided into three groups: Techno-positive (n = 66), Techno-neutral (n = 47), and Techno-critical (n = 71). According to the results, more than 80% of the respondents saw that robots can offer support in existing work tasks, and more than 70% saw that the robots can do existing tasks. The most often mentioned challenges were the reduction of interaction and the reduction of human touch. Further, there are various knowledge needs among the respondents. Most of the knowledge needs were not based on the technical use of the robots; rather, they were quite scattered. The results suggest that successful use and implementation of robots in welfare services require a comprehensive plan and change agents. This study suggests that techno-positive people could act as change agents, assisting in implementing the changes. In addition, to manage change in the welfare services it is essential to improve the quality of the information, solve the resistance to change, create organizational awareness, and understanding, and establish a psychological commitment to change the processes. [ABSTRACT FROM AUTHOR]

This article reviews the development of digital twin and summarizes the evolution of the concept of digital twin. The key words and abstracts of 1187 relevant papers in recent 5 years are quantitatively analyzed, and 13 key nodes are obtained. Then it analyzes the content of 9 systematic review articles and summarizes 15 core contents. Then select 115 application examples from the articles. Through the above three steps, nine descriptive general characteristics of digital twin system are summarized: Entity object; Virtual object; Twinning degree; Communication; Data; Functionality; Human-computer interaction; systematicness; and Cost control. This paper will explain these features in detail and summarize the application examples of classifying each feature. This paper aims to summarize the development of digital twin in recent years and propose a description method of system characteristics according to the application scenario of digital twin and the increasingly complex situation of the system. [ABSTRACT FROM AUTHOR]

Human–computer interaction, Artificial Intelligence, and the multilingualism of digital culture open up unexpected scenarios in the contemporary design of cultural spaces with the creation of artifacts in which the analogic and digital dimensions come together to enhance the experience. Sensors and devices track user movement in the real world and translate the inputs into commands through hand gestures, speech recognition, head movements, tangible interfaces, or a combination of these elements. Through theorical models, concepts and tools, the paper reports the evolution of "User Experience" applied to personalized enjoyment and use of cultural places. The functional-performance survey of tools and technologies for perception, narrative and augmented interaction revealed models that highlight the diversity and richness of tangible and intangible cultural heritage through new forms of interaction and knowledge transfer. In this direction, new technological tools make it possible to detect, track and evaluate the personalized user experience by processing or producing large amounts of data. Therefore, on one hand the paper explores the limitations dictated by data management and user privacy in using such systems and, on the other, it prefigures new scenarios for amplifying and personalizing the user experience. [ABSTRACT FROM AUTHOR]

AbstractMindful human–computer interaction has been increasingly used to induce emotion regulation. The Coral Morph is an artistic shape-changing textile installation designed for mindful emotion regulation in public spaces. The installation incorporates affective soft-robotic material movements, heart rate physicalization, and expressive interactive textile features conceived to enhance sensory engagement. In an exhibition, 55 participants were recruited to interact with Coral Morph, allowing the evaluation of its sensory engagement, somesthetic appreciation, and lifelikeness perception. Participants’ facial expressions during interaction were triangulated with their questionnaire responses, and short interviews were conducted enquiring on the somatic feedback after the interaction. The empirical findings confirmed that participants perceived the installation as emotionally intelligent, animate, likable, safe, and interesting. Participants regarded the experience as pleasant, calm, positive, and relaxed, appreciating the somesthetic aspects, thus highlighting the effectiveness of the design elements. Furthermore, we highlighted the significance of individual differences in shaping the interactive encounter. These findings inform well-being and human–computer interaction researchers on designing future responsive installations for mindfulness and identifying future research avenues, encompassing the nuanced exploration of interactive material behaviors and advancements in evaluation methodologies. [ABSTRACT FROM AUTHOR]

AbstractThe metaverse has emerged as an exciting new paradigm for human-computer interaction (HCI) and virtual collaboration. This paper presents a comprehensive review of the metaverse to address the gap in the existing literature where there is a lack of a survey that reviews the nature of the metaverse and its building blocks from a human-centric perspective. We first synthesize a definition of the metaverse from existing literature and delineate key affordances. We then introduce a detailed framework encompassing the metaverse’s nature, infrastructure technologies, and input/output technologies that facilitate multi-sensory HCI, alongside applications across diverse domains. The components within this framework are explained in depth, offering insights into the metaverse’s nature and the readiness level of current technologies. Based on this comprehensive analysis, we outline major open challenges and propose promising directions demanding further exploration and investigation. By clarifying the vision for the metaverse and characterizing the building blocks required to realize it, this review provides essential insights and serves as an invaluable resource for metaverse developers and researchers working to advance this transformative new medium. [ABSTRACT FROM AUTHOR]

The speech signal is one of the most effective data sources used in human–computer interaction and is widely used in many applications such as speech/speaker recognition, emotion recognition, language recognition, and age and gender recognition. In this study, two convolutional neural networks, 1D and 2D, are designed to recognize the age and gender class of the speaker. These models are created by stacking four feature learning blocks (FLBs) and one classification block. Two different feature vectors are used in their inputs, which are formed with mel-frequency cepstrum coefficients. Each FLB consists of a convolution layer, a batch normalization layer, a ReLU layer, a max pooling layer, and a dropout layer, while the classification block consists of a flatten layer, two fully connected layers, and a softmax layer. In the study, besides the parameter optimization made by manual search method, model optimization is also carried out by trying different combinations of the basic components that make up the FLBs. In the experiments with the Common Voice Turkish dataset, the highest validation accuracy is obtained as 66.26% for the 1D model and 94.40% for the 2D model. These results reveal the effectiveness of the proposed 2D model in age and gender recognition. [ABSTRACT FROM AUTHOR]

Navigation services play a crucial role in everyday wayfinding. One of their key features is the turn-by-turn guidance, enabling wayfinders to navigate efficiently between locations. However, over-reliance on turn-by-turn guidance can hinder wayfinders' ability to perceive their surroundings and develop spatial awareness, negatively impacting user experience. Previous research has addressed these issues by modifying instructions, user interaction, or both. However, the applicability of these methods beyond specific studies remains unclear. This paper presents a human-centred study by evaluating an algorithm that automatically generates navigation instructions. The instructions are generated by adding route-defining locations to ‘standard’ turn-by-turn instructions to enhance wayfinder's spatial knowledge acquisition, and improve user experience. Route-defining locations, identified using a previously proposed algorithm, serve as anchors for the wayfinder to form a spatial understanding of the environment. The study involved 36 participants divided into two groups: one received ‘standard’ turn-by-turn (TBT) instructions, while the other received instructions that included route-defining locations (RDL). Participants navigated in an unfamiliar virtual environment, and their wayfinding and route learning were compared between the two groups. Results show that RDL instructions led to better wayfinding and route learning performance compared to TBT instructions, suggesting the potential to improve navigation systems and user experiences. [ABSTRACT FROM AUTHOR]

Flexible pressure sensor is widely used in fields such as healthcare, intelligent prosthetics, and patient rehabilitation. However, how to ensure high sensitivity of the sensor while having a wide detection range remains a challenge. In order to address this challenge, a flexible pressure sensor with a wide detection range based on capacitive‐piezoresistive dual mode conversion is proposed. By manufacturing circular through‐holes on the dielectric layer of the sensor, the microstructure of the upper and lower electrode layers can achieve non‐contact and contact switching under different pressures through the through holes. This means that the sensor remains in capacitive mode at low pressure and then in piezoresistive mode at high pressure. By cleverly combining the two modes, the measurement range can reach 2 000 kPa and high sensitivity can be maintained during mode switching. In addition, its production process is simple, the cost is low, and it has a fast response time (120 ms) and good repeatability stability. The experimental results indicate that the sensor can achieve ideal performance in proximity detection, human behavior monitoring, and ultra high‐pressure detection, and has broad application prospects in providing a more powerful human‐computer interaction interface and expanding more comprehensive health monitoring. [ABSTRACT FROM AUTHOR]

This paper presents a multimodal dialogue system with user personality adaptation and multiple nonverbal behavior generation for a conversational android robot that plays a customer service role in recommending travel plans. It is useful to estimate the user's personality to realize a dialogue system that adapts the dialogue strategy to individual users. To improve the user's impression of the conversation robot as a customer service agent, we must control the nonverbal behavior of the robot, such as its facial expressions and motions. Moreover, we need to design the dialogue strategy to provide information and make users enjoy the conversation by adding an ice-breaking step. Against this background, we develop a dialogue system prototype that adjusts the dialogue strategy based on the user's personality, as estimated by a pre-trained multimodal sensing model. Furthermore, we implement appropriate nonverbal behavior patterns for each situation, including the voice, motion, and facial expressions of the android robot, to provide it appropriate and polite service agent behaviors. A personality assessment game is introduced into the dialogue to prevent the user from becoming bored. The game plays an ice-breaking role in the dialogue. To let the user engage with the robot, we implement a complement to the user as a dialogue act. We evaluate the proposed multimodal dialogue system for customer service by means of participation in a dialogue robot competition. The fair evaluation results from 26 dialogue users who conversed with multiple systems show that the proposed dialogue system achieved the best score among 13 teams participating in the preliminary round of the competition, indicating that the user personality adaptation and various elements implemented in our dialogue system improve the dialogue experience of general users. The results of a third-party evaluation and multiple regression analysis show that natural response is the most important factor contributing to user impression. [ABSTRACT FROM AUTHOR]

Any machine learning (ML) model is highly dependent on the data it uses for learning, and this is even more important in the case of deep learning models. The problem is a data bottleneck, i.e. the difficulty in obtaining an adequate number of cases and quality data. Another issue is improving the learning process, which can be done by actively introducing experts into the learning loop, in what is known as human-in-the-loop (HITL) ML. We describe an ML model based on a neural network in which HITL techniques were used to resolve the data bottleneck problem for the treatment of pancreatic cancer. We first augmented the dataset using synthetic cases created by a generative adversarial network. We then launched an active learning (AL) process involving human experts as oracles to label both new cases and cases by the network found to be suspect. This AL process was carried out simultaneously with an interactive ML process in which feedback was obtained from humans in order to develop better synthetic cases for each iteration of training. We discuss the challenges involved in including humans in the learning process, especially in relation to human–computer interaction, which is acquiring great importance in building ML models and can condition the success of a HITL approach. This paper also discusses the methodological approach adopted to address these challenges. [ABSTRACT FROM AUTHOR]

In this work, the flexible piezoresistive films were synthesized through in-situ dispersing perovskite MASnBrI 2 nanocrystals (NCs) into the elastic thermoplastic polyurethanes (TPU) substrate. Then, the flexible piezoresistive sensors were fabricated by sandwiching the NCs/TPU nanocomposite layer between interdigitated Ag electrodes. The piezoresistive behavior was attributed to the perovskite NCs which were distributed uniformly inside the TPU film. Specifically, it manifests as the change in electron cloud density of perovskite NCs under a pressure. The sensor made with a NCs/TPU composite membrane shows a pressure sensitivity of 0.796 kPa−1 over a wide linear range of 0∼0.7 kPa, an ultralow detect limitation of 0.2 Pa, a rapid response/recover time of 400 ms/370 ms and a good bending ability (90°). Additionally, the strain sensor also shows excellent performance in detecting the respiratory airflow, wrist pulse and finger joint curvature. We believe that this work might open up new opportunities for flexible strain sensors in the areas such as health monitoring, human-computer interaction, and smart electronic wearables. [ABSTRACT FROM AUTHOR]

AbstractA flexible production model in which humans and robots collaborate has become an urgent need for industrial production. Therefore, research on human-machine integration systems has received more and more attention in recent years. Human-computer interaction is widely used in industrial production and entertainment life, such as industrial control, VR games, etc. In this article, we designed and implemented a human-machine fusion system that meets different interactive tasks. Human movement estimation and robot interactive control are the key technologies. First, we propose a human posture solution method based on information fusion of multiple cameras and inertial measurement units. This method constructs an optimization problem, fuses the 2D joint detection information under multiple cameras and the measurement information of the worn inertial measurement unit, and optimizes the estimation of human kinematic posture, which improves the problems of incomplete posture information and sensitivity to noise under a single sensor. Therefore, this method improves the accuracy of pose estimation. Secondly, combining the kinematic characteristics of the robot and the characteristics of human-computer interaction, we designed a robot control strategy based on target point tracking and model predictive control, so that the robot can adapt to dynamic environments and different interaction needs by adjusting control parameters. At the same time, our design ensures the safety of robots and operators. Finally, we conducted human-computer interaction experiments such as action following, item delivery, and active obstacle avoidance. The experimental results show the effectiveness and reliability of the designed robot interaction system in a human-machine fusion environment. [ABSTRACT FROM AUTHOR]

Flexible, stretchable, and sensitive multidirectional sensing systems that can decouple different mechanical inputs and identify multidirectional signals are crucial for dynamic human signal perception and intelligent human–computer interaction. Most reported multidirectional sensors are suitable for discriminating in‐plane deformation directions, and the sensing materials are difficult to balance between stretchability and mechanical strength. Here, a segmented embedded structure strategy inspired by the interlaced structure of cartilage is proposed. This strategy combines soft and hard materials in a topological and zipper‐shear chain manner and balances the performance of reinforced composites with flexibility and high toughness. In the case of segmented embedded hydrogels (SEHs), a wearable multidirectional sensing system that can decouple and identify planar strain/pressure is constructed. The multidirectional sensing system exploits the inherent anisotropy and layered structure design of composites to decouple the sensing functions. Supported by machine learning algorithms, the high accuracy demonstration of the multidirectional sensors in typical multidirectional motion joint posture monitoring and recognition confirms their potential in practical applications such as personal health sensing and human–computer interaction. [ABSTRACT FROM AUTHOR]

Intelligent sensor devices are widely used in the fields of aerospace, automobile, biomedicine, and electronics. However, using traditional bulk sensors, it is challenging to meet the needs of health monitoring, human‐computer interaction, environmental monitoring, and wearable portable electronic devices because of the limitations of flexibility and wearability. Novel flexible sensors should be characterized with stretchable, bendable, breathability, and distributed sensing properties. Herein, a flexible multi‐functional fiber sensor for stress and strain monitoring is prepared using thermal drawing technology. The as‐developed sensor is used for real‐time monitoring of small stress and strain with an accuracy of 1 g (1.7 kPa), positioning spatial resolution of 2 cm, and a strain range of 30 000 µɛ with an accuracy of 86 µɛ. It can sense single‐ and multi‐points weak pressure at the same time during keyboard testing. Furthermore, pulse and breath monitoring are also demonstrated. These results reveal the high sensitivity, lightweight, and good flexibility of this multi‐functional fiber sensor, promoting its applications in fields such as biosensing, aircrafts, and intelligent robot. [ABSTRACT FROM AUTHOR]

Throughout the last few decades, researchers have developed standard usability questionnaires to evaluate usability and present a single score that represents a product's overall level of ease of use. One of the most notable questionnaires is the System Usability Scale (SUS) (Sauro & Lewis, 2009). However, since the SUS was introduced in 1986, products and services have not only undergone monumental advancements in technology, but Human-Computer Interaction and user experience research practices have matured. These changes are also true in the enterprise space. In this paper, we present preliminary evidence concerning the construct validity of a new usability questionnaire with three advantages for enterprise applications over the original 10-item SUS questionnaire. The Enterprise System Usability Scale (ESUS) offers better measurement of usability for technical products/services, reduced questionnaire items, and alignment with enterprise environments. Results indicate that the ESUS exhibits a similarly strong correlation with satisfaction as the SUS and is strongly correlated with SUS for enterprise and enterprise data products/services. [ABSTRACT FROM AUTHOR]

This paper presents a model based on hand anatomy and neural network for the recognition of sign language. Feature extraction is done by using FAST and SIFT techniques. Out of these extracted features, only essential hand landmarks are selected using hand anatomy. NN is then used for the training and testing of the model. The proposed model is evaluated on sign language gestures used for medical purposes, general purposes, and family & relative purposes. The results prove that the proposed model has achieved fast and highly accurate results when compared with other available models. The model has achieved a recognizable accuracy of 99.85%, 97.55%, and 98.85%, on medical purposes, general purposes, and family & relative purposes, respectively. [ABSTRACT FROM AUTHOR]

Eye-tracking and head-tracking techniques have been applied in many fields, including human–computer interaction, gaming, virtual reality (VR), and the medical. In these applications, users must wear special hardware devices such as eye trackers and head-mounted devices. However, these devices are high-priced, operating them may be complicated, and users may feel uncomfortable wearing them. Then, how can we track eye movements and head movements in real time without these devices? In this paper, we present a real-time camera-based gaze-tracking system that provides two interactive modes: eye gaze and head gaze. The system uses the same calibration procedures to project the gaze directions of the eyes or head to the screen coordinates. Then, we designed a 9-point circular interface to examine the accuracy. Eye gaze and head gaze achieved a visual angle error of 1.76 and 2.65 degrees, respectively. They were comparable to commercial eye-trackers. We also applied the system to a game and verified its effectiveness in the realm of interaction by analyzing the user experience and game score under different interactive modes. The experimental results showed users could get a similar score as the keyboard using eye gaze and feel more immersive under head gaze. Our findings can help to provide more funny choices for users to interact with computers. [ABSTRACT FROM AUTHOR]

Assistive aerial teleoperation in unknown environments can be challenging due to the limited sensing range of cameras and the insufficient human–computer interaction. Obstacles occluding the field of view (FOV) of camera reduce the visibility of unknown environments, and it coupled with insufficient pilot instructions directly affect human intention recognition. To address this issue, the planned trajectory generated by assistive teleoperation should align with human intention while considering perception awareness (PA) of the FOV. However, most of the existing works considering PA solely increase the FOV visibility while neglect the human intention constraints. As a result, the PA constraints can cause the heading of the quadrotor to deviate from human expectation, requiring more human–computer interactions to correct it. In this paper, we propose a human-guided motion planner with perception awareness to solve these issues. Specifically, in the path-finding stage, we introduce a spatio-temporal path similarity metric method. This method, combined with motion primitive propagation, finds an initial path that is collision-free and roughly aligned with human intention. In the subsequent trajectory optimization stage, we jointly consider PA with respect to occlusion and human intention constraints, effectively optimizing FOV visibility without deviating the heading of quadrotor from human intention. Extensive experiments and benchmark comparisons demonstrate the effectiveness of our proposed method in solving assistive aerial teleoperation for even novice operators. [ABSTRACT FROM AUTHOR]

With the rapid development of unmanned aerial vehicle (UAV) technology, UAV has been widely used in agricultural plant protection, electric power inspection, security patrols, and other fields. However, the control system of the UAV is a complex human–computer interaction system, which requires higher requirements in practical applications. Due to differences in hardware design, software development, and other aspects among different manufacturers, these UAV control systems require high hardware requirements, resulting in a long development cycle. At the same time, in practical applications, due to various reasons, there are equipment failures that are difficult to detect and eliminate in a timely manner. This paper used the UAV graphical control strategy identification method based on cloud server technology, and used the support vector machine (SVM) algorithm to analyze its identification accuracy. The research results showed that when other conditions were the same, the number of researchers and experts who were satisfied with the drone trial effect of the cloud server was 42 and 10, respectively, accounting for 84% and 100%. It indicates that they believe that the cloud server can effectively improve the effectiveness of the drone graphical control strategy recognition method, indicating a positive relationship between the two. [ABSTRACT FROM AUTHOR]

This study presents a human-computer interaction combined with a brain-machine interface (BMI) and obstacle detection system for remote control of a wheeled robot through movement imagery, providing a potential solution for individuals facing challenges with conventional vehicle operation. The primary focus of this work is the classification of surface EEG signals related to mental activity when envisioning movement and deep relaxation states. Additionally, this work presents a system for obstacle detection based on image processing. The implemented system constitutes a complementary part of the interface. The main contributions of this work include the proposal of a modified 10–20-electrode setup suitable for motor imagery classification, the design of two convolutional neural network (CNNs) models employed to classify signals acquired from sixteen EEG channels, and the implementation of an obstacle detection system based on computer vision integrated with a brain-machine interface. The models developed in this study achieved an accuracy of 83% in classifying EEG signals. The resulting classification outcomes were subsequently utilized to control the movement of a mobile robot. Experimental trials conducted on a designated test track demonstrated real-time control of the robot. The findings indicate the feasibility of integration of the obstacle detection system for collision avoidance with the classification of motor imagery for the purpose of brain-machine interface control of vehicles. The elaborated solution could help paralyzed patients to safely control a wheelchair through EEG and effectively prevent unintended vehicle movements. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) based on wearable sensors has emerged as a low-cost key-enabling technology for applications such as human–computer interaction and healthcare. In wearable sensor-based HAR, deep learning is desired for extracting human active features. Due to the spatiotemporal dynamic of human activity, a special deep learning network for recognizing the temporal continuous activities of humans is required to improve the recognition accuracy for supporting advanced HAR applications. To this end, a residual multifeature fusion shrinkage network (RMFSN) is proposed. The RMFSN is an improved residual network which consists of a multi-branch framework, a channel attention shrinkage block (CASB), and a classifier network. The special multi-branch framework utilizes a 1D-CNN, a lightweight temporal attention mechanism, and a multi-scale feature extraction method to capture diverse activity features via multiple branches. The CASB is proposed to automatically select key features from the diverse features for each activity, and the classifier network outputs the final recognition results. Experimental results have shown that the accuracy of the proposed RMFSN for the public datasets UCI-HAR, WISDM, and OPPORTUNITY are 98.13%, 98.35%, and 93.89%, respectively. In comparison with existing advanced methods, the proposed RMFSN could achieve higher accuracy while requiring fewer model parameters. [ABSTRACT FROM AUTHOR]

Objective: To explore whether the optimal objective function weightings change when using a digital human model (DHM) to predict origin and destination lifting postures under unfatigued and fatigued states. Background: The ability to predict human postures can depend on state-based influences (e.g., fatigue). Altering objective function weightings within a predictive DHM could improve the ability to predict tasks specific lifting postures under unique fatigue states. Method: A multi-objective optimization-based DHM was used to predict origin and destination lifting postures for ten anthropometrically scaled avatars by using different objective functions weighting combinations. Predicted and measured postures were compared to determine the root mean squared error. A response surface methodology was used to identify the optimal objective function weightings, which was found by generating the posture that minimized error between measured and predicted lifting postures. The resultant weightings were compared to determine if the optimal objective function weightings changed for different lifting postures or fatigue states. Results: Discomfort and total joint torque weightings were affected by posture (origin/destination) and fatigue state (unfatigued/fatigued); however, post-hoc differences between fatigue states and lifting postures were not sufficiently large to be detected. Weighting the discomfort objective function alone tended to predict postures that generalized well to both postures and fatigue states. Conclusion: Lift postures were optimal predicted using the minimization of discomfort objective function regardless of fatigue state. Application: Weighting the discomfort objective can predict unfatigued postures, but more research is needed to understand the optimal objective function weightings to predict postures during a fatigued state. [ABSTRACT FROM AUTHOR]

Background: Learning to use software using screencast videos with worked examples in the corresponding practice files presents a classic split‐attention problem that requires learners to mentally integrate information from the video with a target application. While there is evidence that splitting attention either temporally or spatially adversely impacts learning, it is unclear if there is a meaningful difference in the load imposed by splitting attention in different ways. Furthermore, while much is known about effective screencast design, little is known about how learning environment features, such as the monitor configuration, influence learning from this form of instruction. Objective: An experiment was conducted with 42 novice learners to compare the effects of two common monitor configurations that divided attention differently on three physiological measures of cognitive load. Methods: Effects due to the monitor setup were assessed using a 2 × 2 study design that controlled for task order and working memory capacity. In one condition, subjects split attention temporally by toggling back and forth between the video and target application on one monitor. In contrast, the other condition required subjects to split attention spatially by shifting their gaze between the video and target application displayed on two side‐by‐side monitors. Results and Conclusions: Results indicated that cognitive load as measured through task‐evoked pupil response was significantly higher, p < 0.05, for groups with two monitors during both instruction and testing, even after controlling for working memory capacity. Lay Description: What is currently known about the subject matter: Splitting attention between information sources negatively impacts learningInstructional media that splits attention temporally or spatially increases cognitive loadEnvironmental factors such as monitor configuration also contribute to cognitive loadPrior research suggests integrating the mutually referring information sources What the paper adds: Compares cognitive load in two monitor configurations designed to split attention differently – temporally and spatiallyCognitive load increased when attention was split spatially across two monitors during an encoding phaseCognitive load also increased when attention was split spatially across two monitors during a retrieval phaseWorking memory capacity did not explain variances in cognitive load Implications for practice: To reduce cognitive load, learners should consider working on one monitor while engaging with screencast software tutorials and the related practice filesFocus on studying worked examples in the video before attempting to replicate them in the practice filesWhen designing instructional videos, structure opportunities to practice closest to the point where they are taught in the video to avoid added cognitive load [ABSTRACT FROM AUTHOR]

Researchers examining the social relationship between humans and machine agents have been faced with a series of obstacles, mainly due to the lack of appropriate study tools. To address this need for measurement toolkits, this article examines the development and validation of the Machines As Social Entities (MASE) scale. MASE was created to measure people's beliefs in machine agents as social entities. Together, the results from a series of studies, including exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), demonstrate that the MASE is a reliable and valid measure. Potential uses of the scales are then discussed. [ABSTRACT FROM AUTHOR]

This study attempts to determine a correlation effect between people's perception and awareness of the operationalization of artificial intelligence in their everyday lives and in the production, presentation, and publication of news media in the U.S. By looking at the effect individual characteristics may have on a person's perception and awareness of AI operationalized for news media and looking at whether perception and/or awareness of AI operationalized in a person's daily life affects their perception and awareness of AI operationalized for news media, we seek to find correlation between these two factors. The research relies on Actor-network theory, the MAIN (Modality, Agency, Interactivity, Navigability) Model, and utilizes a convenience sample survey method using the MTurk participant platform. [ABSTRACT FROM AUTHOR]

Accurately localizing bare-hands is crucial for human–computer interaction systems. In real-time systems, computational time plays a pivotal role in achieving this task within a specified timeframe. The localization process may involve detection, tracking, or both, depending on the framework's needs. Most studies on bare-hand detection and tracking have been conducted in controlled environments. However, localizing bare-hands in uncontrolled environments are challenging due to the complexity of variations, such as changes in illumination, rotation, occlusion, scale, pose, speed, and impostor bare-hands. These factors can significantly impact the performance of the models, along with background feature domination effect and motion blur, which further complicate localization. To address these challenges, this paper presents a comprehensive analysis of the most significant deep learning-based hand localization models. We have customized an object detector as a bare-hand localization model by incorporating detection and tracking modules, providing computationally efficient performance while addressing the variations. To evaluate the models' efficiency across a range of scales, we have proposed a scale-based bare-hand database with varying scales from 20 to 200 cm. We have also evaluated these models on various bare-hand benchmark databases. [ABSTRACT FROM AUTHOR]

The ability to identify human actions in uncontrolled surroundings is important for Human–Computer Interaction (HCI), especially in the sports areas to offer athletes, coaches, and analysts valuable knowledge about movement techniques and aid referees in making well-informed decisions regarding sports movements. Noteworthy, recognizing human actions in the context of basketball sports remains a difficult task due to issues like intricate backgrounds, obstructed actions, and inconsistent lighting conditions. Accordingly, a method based on the combination of YOLO and deep fuzzy LSTM network is proposed in this paper. YOLO is utilized for detecting players in the frame and the combination of LSTM and Fuzzy layer is used to perform the final classification. The reason behind using LSTM along with fuzzy logic refers to its inability in coping with uncertainty which led to the creation of a more transparent, interpretable, and accurate predictive system. The proposed model was validated on SpaceJam and Basketball-51 datasets. Based on the empirical results, the proposed model outperformed all baseline models on both datasets which obviously confirms the priority of our combinational model for basketball action recognition. [ABSTRACT FROM AUTHOR]

In this work, the flexible piezoresistive films were synthesized through in-situ dispersing perovskite MASnBrI 2 nanocrystals (NCs) into the elastic thermoplastic polyurethanes (TPU) substrate. Then, the flexible piezoresistive sensors were fabricated by sandwiching the NCs/TPU nanocomposite layer between interdigitated Ag electrodes. The piezoresistive behavior was attributed to the perovskite NCs which were distributed uniformly inside the TPU film. Specifically, it manifests as the change in electron cloud density of perovskite NCs under a pressure. The sensor made with a NCs/TPU composite membrane shows a pressure sensitivity of 0.796 kPa−1 over a wide linear range of 0∼0.7 kPa, an ultralow detect limitation of 0.2 Pa, a rapid response/recover time of 400 ms/370 ms and a good bending ability (90°). Additionally, the strain sensor also shows excellent performance in detecting the respiratory airflow, wrist pulse and finger joint curvature. We believe that this work might open up new opportunities for flexible strain sensors in the areas such as health monitoring, human-computer interaction, and smart electronic wearables. [ABSTRACT FROM AUTHOR]

HCI is an eclectic intellectual community comprised of researchers from different academic backgrounds applying diverse methodologies to an array of research questions. What is it that unites this community? What makes HCI an academic discipline? Researchers have proposed different frames—science and problem-solving, for example—to make sense of HCI as a unified, cohesive discipline. In this paper, we investigate whether a configurational theory of HCI can contribute to the discourse. As part of our investigation, we propose a configurational theory that is potentially suitable for HCI research. Our examination has led to an understanding of HCI that emphasizes its core object of study rather than its methodologies or tendency towards problem solving. We explore how this approach complements and extends existing discussions of HCI's disciplinary identity. We end by discussing some potential benefits that a configurational theory might have, such as its ability to serve as a frame of reference for researchers, help researchers position their work in relation to existing work, identify and evaluate research claims and evidence, and assess contribution and knowledge growth in the field. [ABSTRACT FROM AUTHOR]

Interactive digital exhibits (IDEs) in science museums are widely used to improve visitor engagement. The human–computer interaction characteristics of such exhibits play a key role in holding the attention of visitors, especially children. To determine these characteristics of IDEs and their effects on child engagement, 60 representative digital exhibits in two science museums were selected and analyzed, and eight characteristics were identified and scored. The holding time of the children was measured at each exhibit, and the average holding time (AHT) across exhibits was calculated. Then, the correlations between the IDE characteristics and engagement levels of the children were evaluated. The results show that three characteristics, namely, competition, social interaction, and visitor control, of IDEs have important effects on child engagement. The reasons for these results were also analyzed, and design considerations and principles for IDEs were identified. [ABSTRACT FROM AUTHOR]

Heuristic evaluation provides usability auditors with a structured flow of activities that enables them to obtain reliable, comparable and cost-effective usability evaluation results. Tools supporting heuristic evaluation are scarce in literature. Usevalia is an Internet-based tool that manages usability audits on the basis of heuristic evaluation. This article presents the main features of Usevalia, together with a validation of the software by following the Technology Acceptance Model, which verified the users' perceptions of Usevalia as regards usefulness, ease of use, attitude and intention to use. A total of 22 students enrolled on a human–computer interaction course were involved in the validation. According to the results of the survey conducted with them, the proposed tool is easy to use (MD = 4.00 out of 5) and useful for conducting usability audits based on heuristic evaluation (MD = 4.00 out of 5). An expert-based validation was also carried out in order to thoroughly compare Usevalia's features with those of a spreadsheet-based tool that performs usability audits in the traditional manner. This comparative analysis made it possible to conclude that the Usevalia tool has a higher perceived usefulness (M = 4.43) and perceived ease of use (M = 4.13) than a traditional audit tool, such as the Usability Datalogger spreadsheet (M = 2.43 and M = 3.03, respectively). [ABSTRACT FROM AUTHOR]

As new technologies constantly change what we understand as a computer, Human–Computer Interaction (HCI) educators need to stay updated and prepare their students to work with an ever-growing number of socio-technical situations. This phenomenon constitutes a challenge for HCI syllabuses and practices, demanding diversified approaches to HCI education. In this article, we articulate Papert's constructionism with Dewey's theory of experience to propose an experience-based constructionism approach to practices in HCI education. We illustrate our approach in a case study with 55 computing undergraduate students engaged in the design and construction of open-ended, physical interactive artworks. Nine interactive artifacts were created. The students have shown an effective experience with our approach, reporting an expanded view of HCI, and demonstrating competence in appropriating new methods and instruments, suggesting the effectiveness of our approach to HCI education. Results of this work may encourage other HCI educators and practitioners to experience the proposed approach in their specific contexts. [ABSTRACT FROM AUTHOR]

This article provides a systematic review of research related to Human–Computer Interaction techniques supporting training and learning in various domains including medicine, healthcare, and engineering. The focus is on HCI techniques involving Extended Reality (XR) technology which encompasses Virtual Reality, Augmented Reality, and Mixed Reality. HCI-based research is assuming more importance with the rapid adoption of XR tools and techniques in various training and learning contexts including education. There are many challenges in the adoption of HCI approaches, which results in a need to have a comprehensive and systematic review of such HCI methods in various domains. This article addresses this need by providing a systematic literature review of a cross-section of HCI approaches involving proposed so far. The PRISMA-guided search strategy identified 1156 articles for abstract review. Irrelevant abstracts were discarded. The whole body of each article was reviewed for the remaining articles, and those that were not linked to the scope of our specific issue were also eliminated. Following the application of inclusion/exclusion criteria, 69 publications were chosen for review. This article has been divided into the following sections: Introduction; Research methodology; Literature review; Threats of validity; Future research and Conclusion. Detailed classifications (pertaining to HCI criteria and concepts, such as affordance; training, and learning techniques) have also been included based on different parameters based on the analysis of research techniques adopted by various investigators. The article concludes with a discussion of the key challenges for this HCI area along with future research directions. A review of the research outcomes from these publications underscores the potential for greater success when such HCI-based approaches are adopted during such 3D-based training interactions. Such a higher degree of success may be due to the emphasis on the design of user-friendly (and user-centric) training environments, interactions, and processes that positively impact the cognitive abilities of users and their respective learning/training experiences. We discovered data validating XR-HCI as an ascending method that brings a new paradigm by enhancing skills and safety while reducing costs and learning time through replies to three exploratory study questions. We believe that the findings of this study will aid academics in developing new research avenues that will assist XR-HCI applications to mature and become more widely adopted. [ABSTRACT FROM AUTHOR]

AbstractWith the formation and growth of the company, corporate law is continuously established and enhanced. It significantly contributes to the company’s healthy growth and brings business operations inside the legal framework. As a result, corporate law education is crucial for employees, particularly directors. Fundamentally, corporate law education is a learning process. The metaverse period has increased learners’ needs for learning environments, and human-computer interaction technology will offer all-encompassing support for the smart learning environment. The board of directors needs to adequately monitor each director’s learning level as they study corporate law, which will inevitably be detrimental to the company’s long-term growth. The most efficient method to address this issue is to use eye movement data to mine different eye movement patterns, followed by an analysis of the learning state of corporate law of directors. The scanning path analysis is used to examine the similarities and differences of the directors’ eye movement behaviors during the study of corporate law to enhance the state of corporate law learning. However, the learning status of corporate law cannot be determined only by the eye tracking of directors. We employ the convolutional neural networks (CNN) -based emotion recognition model to provide the directors constructive criticism about their learning state and offer suggestions for the learning mode. The experimental results demonstrate that time series-based eye movement pattern mining can identify directors’ viewing habits, and clustering can reveal different learning strategies that can be used to evaluate directors’ corporate law learning status. Additionally, the CNN-based emotion recognition model experiment also shows that the established model has an accuracy of 97.0035% and an F1 of 0.9412 in the CASIA-FaceV5 dataset, which helps evaluate the emotions of directors when learning company law. [ABSTRACT FROM AUTHOR]

Continuous usage behavior among millennials is crucial, yet factors influencing this behavior are not well understood. This study integrates human-computer interaction and marketing research to investigate the relationship between persuasive dialogue support (PDS), attitudinal loyalty (AL), and continuous-use intention (CI) in the context of WhatsApp. The study also examines the moderating role of four cultural dimensions: uncertainty avoidance (UA), power distance (PD), masculinity (MAS), and individualism (IND) in these relationships. Data was collected from 287 WhatsApp users in four countries, and the proposed theoretical model was tested using structural equation modelling (SEM-PLS). The results indicate that PDS has a direct positive impact on AL, which in turn leads to CI. The study also found that cultural differences can moderate the relationship between PDS and AL and between AL and CI. Therefore, app developers need to consider cultural differences when designing their products and implement persuasive design aspects to boost user loyalty and intention to use. To ensure optimal app performance in diverse cultural settings, developers should strike an appropriate balance between universal design principles and culturally sensitive adaptation. However, the study's limitations, such as reliance on self-reported data and a relatively small sample size, suggest the need for future research to expand its scope to include participants from a broader range of cultural backgrounds. [ABSTRACT FROM AUTHOR]

The advancements in computer vision and image processing techniques have led to emergence of new application in the domain of visual surveillance, targeted advertisement, content-based searching, human–computer interaction, etc. Out of the various techniques in computer vision, face analysis, in particular, has gained much attention. Several previous studies have tried to explore different applications of facial feature processing for a variety of tasks, including age and gender classification. However, despite several previous studies having explored the problem, the age and gender classification of in-wild human faces is still far from achieving the desired levels of accuracy required for real-world applications. This paper, therefore, attempts to bridge this gap by proposing a hybrid model that combines self-attention and BiLSTM approaches for age and gender classification problems. The proposed model's performance is compared with several state-of-the-art models proposed so far. An improvement of approximately 10% and 6% over the state-of-the-art implementations for age and gender classification, respectively, is noted for the proposed model. The proposed model is thus found to achieve superior performance and is found to provide a more generalized learning. The model can, therefore, be applied as a core classification component in various image processing and computer vision problems. [ABSTRACT FROM AUTHOR]

Flexible sensors are becoming the focus of research because they are very vital for intelligent products, real-time data monitoring, and recording. The flat silk cocoon (FSC), as a special form of cocoon, has all the advantages of silk, which is an excellent biomass carbon-based material and a good choice for preparing flexible sensors. In this work, a flexible piezoresistive sensor was successfully prepared by encapsulating carbonized flat silk cocoons (CFSCs) using an elastic matrix polydimethylsiloxane (PDMS). The sensing performance of the material is 0.01 kPa−1, and the monitoring range can reach 680.57 kPa. It is proved that the sensor can detect human motion and has excellent durability (>800 cycles). In addition, a sensor array for a keyboard based on CFSCs was explored. The sensor has a low production cost and a simple preparation process, and it is sustainable and environmentally friendly. Thus, it may have potential applications in wearable devices and human–computer interactions. [ABSTRACT FROM AUTHOR]

While virtual reality (VR) technologies enable remote communication through the use of 3D avatars, it is often difficult to foster engaging group discussions without addressing the limitations to the non-verbal communication among distributed participants. In this paper, we discuss a technique to detect the intentions to speak in group discussions by tapping into intricate sensor data streams from VR headsets and hand-controllers. To this end, we developed a prototype VR group discussion app equipped with comprehensive sensor data-logging functions and conducted an experiment of VR group discussions (N = 24). We used the quantitative and qualitative experimental data to analyze participants' experiences of group discussions in relation to the temporal patterns of their different speaking intentions. We then propose a sensor-based mechanism for detecting speaking intentions by employing a sampling strategy that considers the temporal patterns of speaking intentions, and we verify the feasibility of our approach in group discussion settings. [ABSTRACT FROM AUTHOR]