Human Intention Research Articles

Abstract PR008: Human papillomavirus vaccination knowledge, health beliefs, recommendation receipt, and intentions among unvaccinated Spanish-speaking young adult Hispanic/Latino sexual minority men

Abstract Sexual minority men are at increased risk for human papillomavirus (HPV) infections and subsequent HPV-related health concerns, including anal cancer. HPV vaccination remains an underutilized prevention strategy among sexual minority men, including Hispanic/Latino (H/L) individuals. As part of a mixed methods project, we conducted a cross-sectional survey among Spanish-speaking H/L sexual minority men ages 18-26 to characterize awareness, knowledge, health beliefs, provider recommendation, and HPV vaccine intentions. Multiple in-person and social media strategies were used to recruit participants who self-identified as having had sex with a man and/or being attracted to men and were of H/L ethnicity; able to read, write, and understand Spanish; aged 18-26 years; living in either Florida or Puerto Rico; and with internet access. Data were collected between August 2021-August 2022 using an online survey. Following data cleaning, descriptive analyses using SAS focused on unvaccinated participants (n=102). Results indicated that most participants (86%) were living in Puerto Rico and self-identified as gay (84%). Fifty-six percent reported having heard of the HPV vaccine. Only 19% reported having received a provider recommendation for the HPV vaccine, and less than one-quarter (21%) reported strongly agreeing that people important to them would want them to receive the HPV vaccine. Most individuals (69%) correctly identified that HPV can cause genital warts. However, over half responded either incorrectly or ”do not know” to questions regarding the link between HPV and anal (54%), oral (61%), or penile (65%) cancers. Over half of participants (56%) responded incorrectly or “do not know” to an item about whether most sexually active individuals are at risk for being infected with HPV. Twenty percent responded incorrectly or “do not know” to an item assessing whether men can be infected with HPV. Regarding comparative risk, 28% reported believing themselves to be at average risk for HPV compared to people their age, whereas 14% and 27% reported being at below average risk and at much below average risk, respectively. Most participants (56%) reported they were either not at all or only a little concerned with the safety of the HPV vaccine; however, 25% responded they were very or extremely concerned. Participants reported positive attitudes toward the efficacy of the HPV vaccine in preventing genital warts, penile cancer, and anal cancer. Further, the majority (69%) indicated strongly agreeing or agreeing with an item that assessed their perceived ability to make an appointment to receive the HPV vaccine. Regarding intentions, only 12% reported being very likely to receive the HPV vaccine in the next year, whereas 30% reported being very likely to receive the vaccine at some point in the future. Findings suggest numerous potential malleable targets for future interventions to promote HPV vaccination among H/L young adult sexual minority men, including prompting provider recommendations and addressing patient education needs and health beliefs. Citation Format: Shannon M. Christy, Steven K. Sutton, Mariana Arevalo, Cyril Patra, Cathy D. Meade, Melisa Ramos, Juliana Borrego, Erin Park, Susan T. Vadaparampil, Julio C. Jimenez, Julian Sanchez, Melissa Marzan-Rodriguez. Human papillomavirus vaccination knowledge, health beliefs, recommendation receipt, and intentions among unvaccinated Spanish-speaking young adult Hispanic/Latino sexual minority men [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr PR008.

The Dance of Agencies in AI Art–Beyond the Creator-Tool Dichotomy, a Netnographic Analysis of Reddit Forums

Abstract This article analyzes the impact of generative artificial intelligence (AI) systems on contemporary AI-based art generation practices. As algorithms display nascent creativity, they catalyze philosophical questions regarding the role of technology in artistic creation. However, the influence of AI on art creation, perception, and consumption remains unexplored by empirical research. This article integrates the theoretical frameworks of post-phenomenology and actor-network theory to investigate how generative AI technologies mediate creators’ perception, agency, and imagination. It explores how human–algorithm assemblages may expand possibilities while also obscuring the anthropocentric constructs underpinning art. It uses netnography of artistic communities in the Reddit website to explore the tensions arising as human rationalities and intentions collide with algorithmic logics and constraints. The findings reveal generative AI’s potential to delegate intentionality as well as its potential influence on agency, rationality, and memory. Using empirical grounding, this study elucidates the complex entanglements among artists, algorithms, artwork, and the public in the emerging generative AI terrain.

Human Papillomavirus Vaccination Intention Among Female Students at Mazandaran University of Medical Sciences

Human Papillomavirus Vaccination Intention Among Female Students at Mazandaran University of Medical Sciences

Real-Time Deep Neurolinguistic Learning Enhances Noninvasive Neural Language Decoding for Brain-Machine Interaction.

Electroencephalogram (EEG)-based brain-machine interface (BMI) has been utilized to help patients regain motor function and has recently been validated for its use in healthy people because of its ability to directly decipher human intentions. In particular, neurolinguistic research using EEGs has been investigated as an intuitive and naturalistic communication tool between humans and machines. In this study, the human mind directly decoded the neural languages based on speech imagery using the proposed deep neurolinguistic learning. Through real-time experiments, we evaluated whether BMI-based cooperative tasks between multiple users could be accomplished using a variety of neural languages. We successfully demonstrated a BMI system that allows a variety of scenarios, such as essential activity, collaborative play, and emotional interaction. This outcome presents a novel BMI frontier that can interact at the level of human-like intelligence in real time and extends the boundaries of the communication paradigm.

Touch-text answer for human-robot interaction via supervised adversarial learning

In daily life, touch modality plays an important role in conveying human intentions and emotions. To further improve touch-based human-robot interaction, robots need to infer human emotions from touch signals and respond accordingly. Therefore, it is a major challenge to correlate the emotional state of touch gestures with text responses. At present, there are few researches on touch-text dialogue, and robots cannot respond to human tactile gestures with appropriate text, so touch-text-based human-robot interaction is not yet possible. To solve these problems, we first built a touch-text dialogue (TTD) corpus based on six basic emotions through experiments, which contains 1109 touch-text sample pairs. And then, we designed a supervised adversarial learning for touch-text answer (SATTA) model to realize the touch-text based human-robot interaction. The SATTA model correlates the data of text mode with that of touch mode by reducing the emotion discrimination loss in the public space and the feature difference between the sample pairs of two modes. At the same time, the feature representation is mapped into the label space to reduce the classification loss of samples. The experiment in the TTD corpus validates the proposed method.

Intention to use digital transformation of human resource employees in Hanoi

Digital transformation is at the heart of the fourth industrial revolution, which leads to fundamental and far-reaching changes in business operations. In an environment where all fields and industries are undergoing digital transformation, are human resources employees willing to accept and implement digitalization in their daily work, and what factors influence their intention to use digitalization in their work? The article studies 130 human resources staff at businesses in the Hanoi area. Research results confirm that perceived usefulness, perceived ease of use, and perceived self-efficacy about digital transformation have a positive influence on human resources employees' intentions to use digital transformation at work. For human resources staff to be comfortable in the process of using technology in a digital transformation environment, human resources employees need to be guided and trained on how to apply new applications and new technology in the field of human resources.

Inferring Human Intent and Predicting Human Action in Human–Robot Collaboration

Researchers in human–robot collaboration have extensively studied methods for inferring human intentions and predicting their actions, as this is an important precursor for robots to provide useful assistance. We review contemporary methods for intention inference and human activity prediction. Our survey finds that intentions and goals are often inferred via Bayesian posterior estimation and Markov decision processes that model internal human states as unobserved variables or represent both agents in a shared probabilistic framework. An alternative approach is to use neural networks and other supervised learning approaches to directly map observable outcomes to intentions and to make predictions about future human activity based on past observations. That said, due to the complexity of human intentions, existing work usually reasons about limited domains, makes unrealistic simplifications about intentions, and is mostly constrained to short-term predictions. This state of the art provides opportunity for future research that could include more nuanced models of intents, reason over longer horizons, and account for the human tendency to adapt.

Variable admittance control for safe physical human–robot interaction considering intuitive human intention

The trajectory planning of the arm is greatly facilitated by the physical direct teaching technology. Variable admittance control is a promising technology when a robot is interacting with a variable environment, such as a human whose stiffness might change during the interaction. Nevertheless, a canonical admittance controller with imperfect parameters may lead to robot oscillation, which brings more challenges to a variable admittance controller. In this paper, we propose an energy-based variable admittance controller with intrinsic oscillation suppression property. During the physical human–robot interaction (pHRI), the human intention is predicted based on the robot’s state and interaction force. The admittance parameters are tuned automatically to conform the robot’s motion to human intention. When the oscillation is detected online by the proposed wavelet module, our variable admittance model reveals oscillation suppression ability because of dissipating the energy generated by high-frequency oscillation. We compared the proposed variable admittance controller with other admittance controller approaches in both simulation and actual robot experiments. The proposed method shows significant improvement in oscillation suppression and human energy conservation in the human–robot interaction application.

Enabling collaborative assembly between humans and robots using a digital twin system

Human–robot collaboration (HRC) systems are intelligent systems that guide robots to collaborate with humans based on a cognitive understanding of human intention, ensuring safe, flexible, and efficient collaboration between humans and robots in shared workspaces. In industrial settings, the current methods for constructing a human digital twin model rely on motion capture devices that require personnel to wear cumbersome equipment, which goes against the principle of flexible interaction advocated for HRC. Furthermore, the current methods do not model humans and robots in a unified space, which is both unintuitive and inconvenient for perceiving and understanding the overall environment. To address these limitations, this paper proposes a digital twin system for HRC. This system facilitates the construction of a digital twin scene, the mapping from the real space to the virtual space, and the planning and execution of collaborative strategies from the virtual to the real space. Designed explicitly for common workstation settings, a robust human mesh recovery algorithm is introduced to address the challenge of reconstructing occluded human bodies. Additionally, uncertainty estimation is employed to enhance the action recognition algorithm, ensuring a controllable level of risk in the recognition process. Experimental results demonstrate the superiority of the proposed methods over baseline methods. Finally, the feasibility and effectiveness of the HRC system are validated through a case study involving component assembly.

Correlates of human papillomavirus vaccination intent for oropharyngeal cancer prevention among gay and bisexual men living in the United States

BackgroundGay and bisexual men (GBM) are at increased risk of developing human papillomavirus (HPV)–associated oropharyngeal cancer (OPC). Vaccination may prevent OPC in GBM; however, vaccination rates are low. The authors explored the correlates associated with HPV vaccination intent for OPC prevention among GBM. MethodsThe authors conducted a cross-sectional study in which they surveyed 1,700 adult GBM with a profile on 2 online dating sites. Eligibility criteria included self-identified GBM living in the United States, aged 18 through 45 years who had sex with a man in the past 5 years. Factors associated with participants’ HPV vaccination status and intent to vaccinate were assessed via the online questionnaire using the Health Belief Model. ResultsMost of the 1,108 eligible GBM had not received 1 dose or more of the HPV vaccine (54.2%), were aged 27 through 37 years (52.3%), were White (58.3%), identified as cisgender men (93.4%), were gay (79.3%), were in a monogamous relationship (99.4%), and had a bachelor’s degree (29.4%) or higher college education (26.1%). Among unvaccinated GBM, 25.3% reported intent to receive the vaccine. In the multivariable model, independent associations (P < .05) were found for the Health Belief Model constructs (perceived benefits and perceived barriers) with HPV vaccine intent, after adjusting for all other predictor variables in the model. ConclusionsThe benefits of HPV vaccination for the prevention of OPC is associated with intent to vaccinate among GBM. Dental care providers can use this information to educate patients in this high-risk population on prevention of HPV–associated OPC. Practical ImplicationsDentists can advocate for HPV vaccination uptake among GBM patients by means of discussing the benefits of vaccination in the prevention of HPV-associated OPC.

The power and potentials of Flexible Query Answering Systems: A critical and comprehensive analysis

The popularity of chatbots, such as ChatGPT, has brought research attention to question answering systems, capable to generate natural language answers to user’s natural language queries. However, also in other kinds of systems, flexibility of querying, including but also going beyond the use of natural language, is an important feature. With this consideration in mind the paper presents a critical and comprehensive analysis of recent developments, trends and challenges of Flexible Query Answering Systems (FQASs). Flexible query answering is a multidisciplinary research field that is not limited to question answering in natural language, but comprises other query forms and interaction modalities, which aim to provide powerful means and techniques for better reflecting human preferences and intentions to retrieve relevant information. It adopts methods at the crossroad of several disciplines among which Information Retrieval (IR), databases, knowledge based systems, knowledge and data engineering, Natural Language Processing (NLP) and the semantic web may be mentioned. The analysis principles are inspired by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework, characterized by a top-down process, starting with relevant keywords for the topic of interest to retrieve relevant articles from meta-sources And complementing these articles with other relevant articles from seed sources Identified by a bottom-up process. to mine the retrieved publication data a network analysis is performed Which allows to present in a synthetic way intrinsic topics of the selected publications. issues dealt with are related to query answering methods Both model-based and data-driven (the latter based on either machine learning or deep learning) And to their needs for explainability and fairness to deal with big data Notably by taking into account data veracity. conclusions point out trends and challenges to help better shaping the future of the FQAS field.

Hierarchical critic learning optimal control for lower limb exoskeleton robots with prescribed constraints

AbstractThis paper presents a hierarchical critic learning optimal control (H‐CLOC) strategy for a lower limb exoskeleton system with prescribed state constraints. The prescribed state constraints of the exoskeleton are mathematically modeled by means of a state error transition function, which transforms the constrained state into an equivalent new unconstrained state. A hierarchical framework including a high‐level motion trajectory planner and a low‐level critic learning (CL) controller is proposed for optimal control of the exoskeleton. The estimation of human motion intentions is achieved using dynamic movement primitives (DMPs), which are capable of accurately replicating the original training trajectories during motion. The CL algorithm is implemented on the transformed unconstrained system while enhancing the learning capability of the proposed algorithm for the exoskeleton. The optimal control with prescribed constraints is analyzed for stability using the Lyapunov theorem. Finally, simulation results validate the effectiveness of the proposed H‐CLOC strategy for lower limb exoskeletons.

Graph Convolutional Neural Network with Multi-Scale Attention Mechanism for EEG-Based Motion Imagery Classification

Recently, deep learning has been widely used in the classification of EEG signals and achieved satisfactory results. However, the correlation between EEG electrodes is rarely considered, which has been proved that there are indeed connections between different brain regions. After considering the connections between EEG electrodes, the graph convolutional neural network is applied to detect human motor intents from EEG signals, where EEG data are transformed into graph data through phase lag index, time-domain and frequency-domain features with different signal bands. Meanwhile, a multi-scale attention mechanism is proposed to the network to improve the accuracy of classification. By using the multi-scale attention-based graph convolutional neural network, the accuracy of 93.22% is achieved with 10-fold cross-validation, which is higher than the compared methods which ignore the spatial correlations of EEG signals.

Active Human-Following Control of an Exoskeleton Robot With Body Weight Support.

This article presents an active human-following control of the lower limb exoskeleton for gait training. First, to improve safety, considering the human balance, the OpenPose-based visual feedback is used to estimate the individual's pose, then, the active human-following algorithm is proposed for the exoskeleton robot to achieve the body weight support and active human-following. Second, taking the human's intention and voluntary efforts into account, we develop a long short-term memory (LSTM) network to extract surface electromyography (sEMG) to build the estimation model of joints' angles, that is, the multichannel sEMG signals can be correlated with flexion/extension (FE) joints' angles of the human lower limb. Finally, to make the robot motion adapt to the locomotion of subjects under uncertain nonlinear dynamics, an adaptive control strategy is designed to drive the exoskeleton robot to track the desired locomotion trajectories stably. To verify the effectiveness of the proposed control framework, several recruited subjects participated in the experiments. Experimental results show that the proposed joints' angles estimation model based on the LSTM network has a higher estimation accuracy and predicted performance compared with the existing deep neural network, and good simultaneous locomotion tracking performance is achieved by the designed control strategy, which indicates that the proposed control can assist subjects to perform gait training effectively.

Dynamic constraint and objective generation approach for real-time train rescheduling model under human-computer interaction

Real-time train rescheduling plays a vital role in railway transportation as it is crucial for maintaining punctuality and reliability in rail operations. In this paper, we propose a rescheduling model that incorporates constraints and objectives generated through human-computer interaction. This approach ensures that the model is aligned with practical requirements and daily operational tasks while facilitating iterative train rescheduling. The dispatcher’s empirical knowledge is integrated into the train rescheduling process using a human-computer interaction framework. We introduce six interfaces to dynamically construct constraints and objectives that capture human intentions. By summarizing rescheduling rules, we devise a rule-based conflict detection-resolution heuristic algorithm to effectively solve the formulated model. A series of numerical experiments are presented, demonstrating strong performance across the entire system. Furthermore, the flexibility of rescheduling is enhanced through secondary analysis-driven solutions derived from the outcomes of human-computer interactions in the previous step. This proposed interaction method complements existing literature on rescheduling methods involving human-computer interactions. It serves as a tool to aid dispatchers in identifying more feasible solutions in accordance with their empirical rescheduling strategies.

In the Eye of Transformer: Global–Local Correlation for Egocentric Gaze Estimation and Beyond

Predicting human’s gaze from egocentric videos serves as a critical role for human intention understanding in daily activities. In this paper, we present the first transformer-based model to address the challenging problem of egocentric gaze estimation. We observe that the connection between the global scene context and local visual information is vital for localizing the gaze fixation from egocentric video frames. To this end, we design the transformer encoder to embed the global context as one additional visual token and further propose a novel global–local correlation module to explicitly model the correlation of the global token and each local token. We validate our model on two egocentric video datasets – EGTEA Gaze + and Ego4D. Our detailed ablation studies demonstrate the benefits of our method. In addition, our approach exceeds the previous state-of-the-art model by a large margin. We also apply our model to a novel gaze saccade/fixation prediction task and the traditional action recognition problem. The consistent gains suggest the strong generalization capability of our model. We also provide additional visualizations to support our claim that global–local correlation serves a key representation for predicting gaze fixation from egocentric videos. More details can be found in our website (https://bolinlai.github.io/GLC-EgoGazeEst).

CONCEPTUAL APPROACHES TO THE STUDY OF SOCIAL-PSYCHOLOGICAL FACTORS OF PREJUDICE

The article is devoted to the study of the phenomenon of prejudice, the analysis of scientific approaches to the explanation of its role in the functioning of the individual as a subject of life. To achieve the goal, theoretical level methods (analysis, synthesis, comparison, classification) were applied.&#x0D; According to the results of the study, it was established that the prerequisite for the formation of prejudices is a social stereotype, as a product of mass behavior supported by the social environment. In contrast to the stereotype in prejudice, as the subject's tendency to perceive, evaluate and take appropriate actions in relation to social objects or a person based on the values accepted in the group, the emotional aspects of the attitude prevail.&#x0D; Among existing approaches to explaining the psychological nature of prejudices, theories of social learning, motivational and cognitive theory, and theory of social identity occupy a special place. According to the results of the analysis of these approaches, it was established that the basis of stereotyping are categorization operations. Categorization allows: to distinguish social groups into "own" and "other" groups; form one's own social identity through awareness of belonging to one's social group and perceive it as a source of self-respect; to create an image of oneself, based on the results of comparing one's group with external groups. Categorization simplifies reality, manifests itself automatically beyond human intention. Existing social norms are the categories for dividing groups in society. The choice of a target group for prejudice within a particular culture, as well as the range of appropriate behavior toward it, depend on social learning. Cultural differences will determine the frequency of manifestation of a certain prejudice, the object of prejudice and the ways of its manifestation. Activation of the influence of prejudices on human behavior occurs in situations of threat to collective self-respect.

Adaptive neuro-fuzzy inference system model driven by the non-negative matrix factorization-extracted muscle synergy patterns to estimate lower limb joint movements

Background and objectiveFor patients with movement disorders, the main clinical focus is on exercise rehabilitation to help recover lost motor function, which is achieved by relevant assisted equipment. The basis for seamless control of the assisted equipment is to achieve accurate inference of the user's movement intentions in the human-machine interface. This study proposed a novel movement intention detection technology for estimating lower limb joint continuous kinematic variables following muscle synergy patterns, to develop applications for more efficient assisted rehabilitation training. MethodsThis study recruited 16 healthy males and 16 male patients with symptomatic patellar tendinopathy (VISA-P: 59.1 ± 8.7). The surface electromyography of 12 muscles and lower limb joint kinematic and kinetic data from healthy subjects and patients during step-off landings from 30 cm-high stair steps were collected. We subsequently solved the preprocessed data based on the established recursive model of second-order differential equation to obtain the muscle activation matrix, and then imported it into the non-negative matrix factorization model to obtain the muscle synergy matrix. Finally, the lower limb neuromuscular synergy pattern was then imported into the developed adaptive neuro-fuzzy inference system non-linear regression model to estimate the human movement intention during this movement pattern. ResultsSix muscle synergies were determined to construct the muscle synergy pattern driven ANFIS model. Three fuzzy rules were determined in most estimation cases. Combining the results of the four error indicators across the estimated variables indicates that the current model has excellent estimated performance in estimating lower limb joint movement. The estimation errors between the healthy (Angle: R2=0.98±0.03; Torque: R2=0.96±0.04) and patient (Angle: R2=0.98±0.02; Torque: R2=0.96±0.03) groups are consistent. ConclusionThe proposed model of this study can accurately and reliably estimate lower limb joint movements, and the effectiveness will also be radiated to the patient group. This revealed that our models also have certain advantages in the recognition of motor intentions in patients with relevant movement disorders. Future work from this study can be focused on sports rehabilitation in the clinical field by achieving more flexible and precise movement control of the lower limb assisted equipment to help the rehabilitation of patients.

Human Posture Transition-Time Detection Based upon Inertial Measurement Unit and Long Short-Term Memory Neural Networks.

As human-robot interaction becomes more prevalent in industrial and clinical settings, detecting changes in human posture has become increasingly crucial. While recognizing human actions has been extensively studied, the transition between different postures or movements has been largely overlooked. This study explores using two deep-learning methods, the linear Feedforward Neural Network (FNN) and Long Short-Term Memory (LSTM), to detect changes in human posture among three different movements: standing, walking, and sitting. To explore the possibility of rapid posture-change detection upon human intention, the authors introduced transition stages as distinct features for the identification. During the experiment, the subject wore an inertial measurement unit (IMU) on their right leg to measure joint parameters. The measurement data were used to train the two machine learning networks, and their performances were tested. This study also examined the effect of the sampling rates on the LSTM network. The results indicate that both methods achieved high detection accuracies. Still, the LSTM model outperformed the FNN in terms of speed and accuracy, achieving 91% and 95% accuracy for data sampled at 25 Hz and 100 Hz, respectively. Additionally, the network trained for one test subject was able to detect posture changes in other subjects, demonstrating the feasibility of personalized or generalized deep learning models for detecting human intentions. The accuracies for posture transition time and identification at a sampling rate of 100 Hz were 0.17 s and 94.44%, respectively. In summary, this study achieved some good outcomes and laid a crucial foundation for the engineering application of digital twins, exoskeletons, and human intention control.

Visual-Impedance-Based Human–Robot Cotransportation With a Tethered Aerial Vehicle

Physical human-robot interaction (pHRI) in the field of aerial vehicles has received more research attention in recent years. In this work, a visual impedance control strategy for human-aerial robot cooperative transportation with a tethered vehicle is presented. Without a positioning system, the aerial vehicle is controlled to follow the human partner by using cable force and visual features of the object as feedback. Furthermore, being aware of human motion is important to improve efficiency and smoothness of the cooperation. Without measuring velocities of the aerial vehicle and the human, we propose to directly estimate relative velocity of them by a vision-based velocity observer. This estimated velocity is then integrated into a visual impedance scheme. The stability of the system is rigorously proved by Lyapunov analysis and passivity analysis. Indoor experiments where a human participant transports a long bar with a tethered aerial vehicle are conducted. Results of experiments under different human velocities and intentions demonstrate the effectiveness and reliability of the proposed method.