Human Intention Research Articles

The Effect of Sensor Feature Inputs on Joint Angle Prediction across Simple Movements.

The use of wearable sensors, such as inertial measurement units (IMUs), and machine learning for human intent recognition in health-related areas has grown considerably. However, there is limited research exploring how IMU quantity and placement affect human movement intent prediction (HMIP) at the joint level. The objective of this study was to analyze various combinations of IMU input signals to maximize the machine learning prediction accuracy for multiple simple movements. We trained a Random Forest algorithm to predict future joint angles across these movements using various sensor features. We hypothesized that joint angle prediction accuracy would increase with the addition of IMUs attached to adjacent body segments and that non-adjacent IMUs would not increase the prediction accuracy. The results indicated that the addition of adjacent IMUs to current joint angle inputs did not significantly increase the prediction accuracy (RMSE of 1.92° vs. 3.32° at the ankle, 8.78° vs. 12.54° at the knee, and 5.48° vs. 9.67° at the hip). Additionally, including non-adjacent IMUs did not increase the prediction accuracy (RMSE of 5.35° vs. 5.55° at the ankle, 20.29° vs. 20.71° at the knee, and 14.86° vs. 13.55° at the hip). These results demonstrated how future joint angle prediction during simple movements did not improve with the addition of IMUs alongside current joint angle inputs.

Simultaneously learning intentions and preferences during physical human-robot cooperation

The advent of collaborative robots allows humans and robots to cooperate in a direct and physical way. While this leads to amazing new opportunities to create novel robotics applications, it is challenging to make the collaboration intuitive for the human. From a system’s perspective, understanding the human intentions seems to be one promising way to get there. However, human behavior exhibits large variations between individuals, such as for instance preferences or physical abilities. This paper presents a novel concept for simultaneously learning a model of the human intentions and preferences incrementally during collaboration with a robot. Starting out with a nominal model, the system acquires collaborative skills step-by-step within only very few trials. The concept is based on a combination of model-based reinforcement learning and inverse reinforcement learning, adapted to fit collaborations in which human and robot think and act independently. We test the method and compare it to two baselines: one that imitates the human and one that uses plain maximum entropy inverse reinforcement learning, both in simulation and in a user study with a Franka Emika Panda robot arm.

Designing Reward Functions Using Active Preference Learning for Reinforcement Learning in Autonomous Driving Navigation

This study presents a method based on active preference learning to overcome the challenges of designing reward functions for autonomous navigation. Results obtained from training with artificially designed reward functions may not accurately reflect human intentions. We focus on the limitations of traditional reward functions, which often fail to facilitate complex tasks in continuous state spaces. We propose the adoption of active preference learning to resolve these issues and to generate reward functions that align with human preferences. This approach leverages an individual’s subjective preferences to guide an agent’s learning process, enabling the creation of reward functions that reflect human desires. We utilize mutual information to generate informative queries and apply information gained to balance the agent’s uncertainty with the human’s response capacity, encouraging the agent to pose straightforward and informative questions. We further employ the No-U-Turn Sampler (NUTS) method to refine the belief model, which outperforms that constructed using the Metropolis algorithm. Subsequently, we retrain the agent using reward weights derived from active preference learning. As a result, our autonomous driving vehicle can navigate between random starting and ending points without dependence on high-precision maps or routing, relying solely on its forward vision. We validate our approach’s performance within the CARLA simulation environment. Our algorithm significantly improved the success rate of autonomous driving navigation tasks that originally failed due to artificially designed rewards, increasing it to approximately 60%. Experimental results show significant improvement over the baseline algorithm, providing a solid foundation for enhancing navigation capabilities in autonomous driving systems and advancing the field of autonomous driving intelligence.

User-agnostic adaptation of human locomotion intent: Leveraging Teacher-Student-Learning and ensemble modeling

Wearable robot control in complicated contexts requires an understanding of human locomotion intent and behaviors. However, signals from human–robot interfaces are typically reliant on the user, leading to poor performance of the model trained on training user when applied to new end-point users. This study aims to address this problem by developing a novel Teacher-Student-Learning (TSL) approach using Heterogeneous Ensemble Hypotheses (HEH) to achieve unsupervised user-agnostic adaptation. The motivation behind using the Teacher-Student-Learning architecture is to leverage the diverse features extracted by HEH while maintaining computational efficiency. HEH reduces the difference between labeled training users and unlabeled end-point users by incorporating multiple diverse feature generators to extract a wide range of features, thereby increasing classification accuracy and reducing variance. However, this approach sacrifices efficiency due to the ensemble nature of HEH. To address this trade-off, the knowledge from HEH is distilled into a single network using TSL, ensuring precision and efficiency. The proposed approach is evaluated on two publicly available human locomotion datasets and a 2D moon dataset. Experimental results demonstrate the effectiveness of the proposed method, outperforming all other methods. Tested with three datasets, the proposed method can classify end-point users’ data with an average accuracy of 98.9%, 96.7%, and 96.9% while yielding a low processing time (1 ms). Compared to a benchmark method, the suggested strategy improves the average accuracy by 1.5% and 7.2% for categorizing the target users’ locomotor modes and stabilizes the learning curves. The proposed method represents a significant contribution to the field of human activity recognition and human intent prediction for human–robot interaction systems, enhancing the efficiency and generalization capacity of these systems.

Human papillomavirus vaccine beliefs and intentions Post-COVID-19 vaccine release among mothers in Alabama

PurposeThe current study sought to determine if the release of COVID-19 vaccines influenced Alabama mothers’ attitudes and behaviors regarding HPV vaccination of their adolescent children. MethodsA social media survey was conducted August − September 2022 among mothers of adolescents ages 9–18, who self-identified as Alabama residents and their child(ren)’s primary medical decision maker. The survey assessed demographics, vaccine knowledge and opinions, vaccination history, influences on vaccination decisions, and how COVID-19 vaccine release affected vaccine opinions. Univariable and multivariable analyses were conducted. ResultsOf 241 responses, most participants were white (82.0 %, n = 196), non-Hispanic (96.6 %, n = 230), and privately insured (64.5 %, n = 151), with annual household incomes ≥$61,000 (45.4 %, n = 103). The majority (60.8 %) reported that their child either had or planned to receive the HPV vaccine. The release of COVID-19 vaccines did not change the majority of parental opinions towards HPV, with 78.5 % (n = 161) reporting no change. Among those who experienced a change, 25 % (n = 5) reported an increased likelihood of having their child vaccinated for HPV and 75 % (n = 15) reported a decrease in likelihood. Moderate and high HPV knowledge scores were associated in multivariable analysis with increased likelihood of having their child vaccinated for HPV (“moderate” knowledge AOR: 12.4, 95 % CI: 1.98–78.1; “high” knowledge AOR: 12.8, 95 % CI: 2.00–82.1). Positive HPV opinion scores in the univariable analysis similarly showed increased odds (AOR = 1.5). ConclusionsThese findings indicate that, in this population, COVID-19 vaccine release did not significantly impact subsequent HPV vaccination decision making. Parental perceptions regarding vaccination are critical to informing future interventions.

Modalization and Transcendence of Health in the Coming Decade: Emphasizing the Human Cognitive System in the Stories of Prophets in the Holy Quran.

In this study, the progression and importance of health knowledge is explored, projecting its future path. We do this by comparing the stages of evolution of the human cognitive system from birth to old age (inner intellect) with the stages of intellectual modalization in divine messengers (external intellect), as depicted in the stories of the prophets in the Holy Quran. This comparison aims to articulate the course of evolution, which includes sensory perception, apprehension, imaginalization, intellection, and intuition. Future medicine will consider the influence of spiritual factors (soul) in the form of human cognition and intention, as well as material effects (genes and biology). The seven medical domains of the future will encompass the field of 'sprito-epigenetico psycho-neuro-endocrino-immuno-pharmacology'. This perspective emphasizes the need for a transcendent outlook in health and medicine. This study employed a library research method, including studies in medical journals from the last forty years.

상사의 가족 친화적 행동이 여성 근로자의 조직몰입, 결혼 및 출산 의향에 미치는 영향 분석

as of 2022. While the level of human capital has been increasing, this indicator illustrates the challenging situation of juggling a career, marriage, and childbirth. Despite the implementation of various employee support systems to address the issue, relying solely on formal institutional measures has limitations in deterring the declining inclination toward marriage and childbirth. This study uses the “Korean Women Manager Panel Survey” by the Korean Women’s Development Institute, which enables an exploration of women workerss human capital, supervisor characteristics, and intentions for marriage and childbirth, examining the impact of Family Supportive Supervisor Behaviors (FSSB), a representative informal mechanism, on female workers' organizational commitment, marriage intentions, and childbearing decisions. The results indicate that higher levels of FSSB increase female workers’ organizational commitment and marriage intentions. According to Social Exchange Theory and the Social Information Processing Theory it can be interpreted that a higher FSSB level motivates employees to be more engaged with their organization and perceive the possibility of balancing work and family even after marriage, thus raising their intentions to marry. However, the effect of FSSB on childbirth intentions among married, childless employees was not significant. It can be inferred that complex socio-cultural variables exist regarding workers' intentions for childbirth.

Within or Outside the Boundaries: Exploring the Intricacies of Human-Elephant Relationship in Tania James’s Novel The Tusk That Did the Damage

Despite the fact that Human geography has long studied landscapes in relation to human identity, experience, intentions, and relationships, a change in the epistemology of spatial discourse has led to a shift in how landscapes are now defined in terms of the presence of human-animal relationships in particular locations. This study comprehends the subtle nuances of human-animal relationships by re-conceptualizing the understanding of spaces and places and negotiates the question of how the fictional elephant Gravedigger becomes known for reorganizing spaces. Based on the semiotics of literary geography and animal geography this research territorializes elephant-human geographies, delineates elephant agency inside the “animal spaces,” and examines how the elephants’ lived experiences create the “beastly places” outside the “animal spaces” in the novel The Tusk That Did the Damage (2015). It further depicts animals as powerful subjects in the wild spaces and attempts to understand how boundary breaching occurs.

A closed-loop human-computer interactive design method based on sequential human intention prediction and knowledge recommendation

Designers working on large-scale complex engineering projects may encounter design errors due to limited experience and expertise. Therefore, during the design process, accurately identifying the designer’s design intention and recommending relevant design knowledge can enhance design efficiency and reduce errors. In this paper, we propose a closed-loop human–computer interactive design method based on sequential human intention prediction and knowledge recommendation. This method leverages the Function-Behaviour-Structure (FBS) model to reduce the dimensionality of design action sequences, so as to facilitate the analysis of potential design patterns. The processed action sequences are used to train Transformer to predict design intentions. In different input sequences, Transformer achieved a highest prediction accuracy of 92.09%. We construct a Design Knowledge Recommendation Framework (DKRF) and its corresponding design knowledge matching algorithm. This framework accurately recommends design knowledge to designers, solving design stagnation and improving design efficiency. Finally, a case study of four types of mechanical model design is conducted to demonstrate the feasibility and wide applicability of the proposed closed-loop human–computer interactive design method.

Implementation of Engagement Detection for Human-Robot Interaction in Complex Environments.

This study develops a comprehensive robotic system, termed the robot cognitive system, for complex environments, integrating three models: the engagement model, the intention model, and the human-robot interaction (HRI) model. The system aims to enhance the naturalness and comfort of HRI by enabling robots to detect human behaviors, intentions, and emotions accurately. A novel dual-arm-hand mobile robot, Mobi, was designed to demonstrate the system's efficacy. The engagement model utilizes eye gaze, head pose, and action recognition to determine the suitable moment for interaction initiation, addressing potential eye contact anxiety. The intention model employs sentiment analysis and emotion classification to infer the interactor's intentions. The HRI model, integrated with Google Dialogflow, facilitates appropriate robot responses based on user feedback. The system's performance was validated in a retail environment scenario, demonstrating its potential to improve the user experience in HRIs.

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs.

Introduction: Surface electromyogram (sEMG) signals have been widely used in human upper limb force estimation and motion intention recognition. However, the electrocardiogram(ECG) artifact generated by the beating of the heart is a major factor that reduces the quality of the EMG signal when recording the sEMG signal from the muscle close to the heart. sEMG signals contaminated by ECG artifacts are difficult to be understood correctly. The objective of this paper is to effectively remove ECG artifacts from sEMG signals by a novel method. Methods: In this paper, sEMG and ECG signals of the biceps brachii, brachialis, and triceps muscle of the human upper limb will be collected respectively. Firstly, an improved multi-layer wavelet transform algorithm is used to preprocess the raw sEMG signal to remove the background noise and power frequency interference in the raw signal. Then, based on the theory of blind source separation analysis, an improved Fast-ICA algorithm was constructed to separate the denoising signals. Finally, an ECG discrimination algorithm was used to find and eliminate ECG signals in sEMG signals. This method consists of the following steps: 1) Acquisition of raw sEMG and ECG signals; 2) Decoupling the raw sEMG signal; 3) Fast-ICA-based signal component separation; 4) ECG artifact recognition and elimination. Results and discussion: The experimental results show that our method has a good effect on removing ECG artifacts from contaminated EMG signals. It can further improve the quality of EMG signals, which is of great significance for improving the accuracy of force estimation and motion intention recognition tasks. Compared with other state-of-the-art methods, our method can also provide the guiding significance for other biological signals.

Teleological Functional Explanations: A New Naturalist Synthesis.

The etiological account of teleological function is beset by several difficulties, which I propose to solve by grafting onto the etiological theory a subordinated goal-contribution clause. This approach enables us to ascribe neither too many teleofunctions nor too few; to give a unitary, one-clause analysis that works just as well for teleological functions derived from Darwinian evolution, as for those derived from human intention; and finally, to save the etiological theory from falsification, by explaining how, in spite of appearances, the theory can allow for evolutionary function loss.

Integrating regular expressions into neural networks for relation extraction

Relation extraction aims to identify semantic relationships between entities from the given sentences. The development of deep learning prompts a variety of neural-based relation extraction models. These models can learn features from sentences automatically, but controlling their learned patterns is equally difficult. Therefore, it is hard to take steps to improve their performance further. A possible way to modify the models is by utilizing human-defined rules to introduce rule-based knowledge and guiding the models to capture the desired features based on human intent. As an efficient human knowledge, regular expression (RE) is helpful in extracting particular targets from text, which is also logical and interpretable. A novel embedding method is proposed to transform REs into learnable representations available to neural networks (NNs). Then, the rule-based representations are investigated to be integrated into different levels of NNs. Three representative relation extraction models based on recurrent neural network (RNN), convolutional neural network (CNN), and Transformer encoder, respectively, are selected as baselines to incorporate our rule-based representations. The experimental results show that the models with rule-based information outperform their original ones on the F1-score by 3.75%, 4.06%, and 2.57%, and the identification accuracy of complex samples is also significantly improved, verifying the effectiveness of our approaches. Our work is easy to reproduce and can be generally applied to various neural networks, proving that data-driven relation extraction methods can take advantage of the rule-based knowledge provided by REs.

Enhancing Chatbot Intelligence Through Narrative Memory Structures

Through the use of narrative memory structures—a cutting-edge technique intended to provide chatbots context awareness and conversational coherence—we sought to improve the intelligence of chatbots. We carefully designed our process with the goal of immediately incorporating narrative data into the chatbot's memory architecture. In order to find narrative patterns, this required evaluating and classifying previous exchanges. The narrative patterns were then saved and used to guide the chatbot's answers. The incorporation of narrative memory structures into the chatbot resulted in a significant improvement in conversational coherence and relevance, as per our study's key findings. Through the use of its narrative memory, which stores earlier encounters, the chatbot shown an impressive capacity to sustain the conversation's flow and provide more contextually relevant replies. In addition, the chatbot demonstrated improved understanding of human intentions, correctly identifying underlying motives and preferences. In addition, comments produced by integrating narrative memory structures were highly customized in addition to being accurate. By utilizing the extensive history of previous exchanges, the chatbot was able to customize replies to each user's unique situation, building a closer relationship and rapport with them. All things considered; our study highlights how narrative memory processes can revolutionize chatbot intelligence. We can improve chatbots' conversational capabilities and enable more meaningful human-computer interactions by giving them the capacity to extract narrative information from previous conversations. This opens the door to a new era of chatbot designs that put the needs of users, contextuality, and empathy first, changing the game for conversational AI in the process.

Analysis of Continual Learning Techniques for Image Generative Models with Learned Class Information Management.

The advancements in deep learning have significantly enhanced the capability of image generation models to produce images aligned with human intentions. However, training and adapting these models to new data and tasks remain challenging because of their complexity and the risk of catastrophic forgetting. This study proposes a method for addressing these challenges involving the application of class-replacement techniques within a continual learning framework. This method utilizes selective amnesia (SA) to efficiently replace existing classes with new ones while retaining crucial information. This approach improves the model's adaptability to evolving data environments while preventing the loss of past information. We conducted a detailed evaluation of class-replacement techniques, examining their impact on the "class incremental learning" performance of models and exploring their applicability in various scenarios. The experimental results demonstrated that our proposed method could enhance the learning efficiency and long-term performance of image generation models. This study broadens the application scope of image generation technology and supports the continual improvement and adaptability of corresponding models.

InstructPatentGPT: training patent language models to follow instructions with human feedback

AbstractIn this research, patent prosecution is conceptualized as a system of reinforcement learning from human feedback. The objective of the system is to increase the likelihood for a language model to generate patent claims that have a higher chance of being granted. To showcase the controllability of the language model, the system learns from granted patents and pre-grant applications with different rewards. The status of “granted” and “pre-grant” are perceived as labeled human feedback implicitly. In addition, specific to patent drafting, the experiments in this research demonstrate the model’s capability to learn from adjusting claim length and inclusion of limiting terms for narrowing claim scope. As proof of concept, the experiments focus on claim ones only and the training data originates from a patent dataset tailored specifically for artificial intelligence. Although the available human feedback in patent prosecution are limited and the quality of generated patent text requires improvement, the experiments following the 3-stage reinforcement learning from human feedback have demonstrated that generative language models are capable of reflecting the human feedback or intent in patent prosecution. To enhance the usability of language models, the implementation in this research utilizes modern techniques that enable execution on a single consumer-grade GPU. The demonstrated proof of concept, which reduces hardware requirements, will prove valuable in the future as more human feedback in patent prosecution become available for broader use, either within patent offices or in the public domain.

Towards determining perceived audience intent for multimodal social media posts using the theory of reasoned action

Increasing use of social media has resulted in many detrimental effects in youth. With very little control over multimodal content consumed on these platforms and the false narratives conveyed by these multimodal social media postings, such platforms often impact the mental well-being of the users. To reduce these negative effects of multimodal social media content, an important step is to understand creators’ intent behind sharing content and to educate their social network of this intent. Towards this goal, we propose Intent-o-meter, a perceived human intent prediction model for multimodal (image and text) social media posts. Intent-o-meter models ideas from psychology and cognitive modeling literature, in addition to using the visual and textual features for an improved perceived intent prediction model. Intent-o-meter leverages Theory of Reasoned Action (TRA) factoring in (i) the creator’s attitude towards sharing a post, and (ii) the social norm or perception towards the multimodal post in determining the creator’s intention. We also introduce Intentgram, a dataset of 55K social media posts scraped from public Instagram profiles. We compare Intent-o-meter with state-of-the-art intent prediction approaches on four perceived intent prediction datasets, Intentonomy, MDID, MET-Meme, and Intentgram. We observe that leveraging TRA in addition to visual and textual features—as opposed to using only the latter–results in improved prediction accuracy by up to 7.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$7.5\\%$$\\end{document} in Top-1 accuracy and 8%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$8\\%$$\\end{document} in AUC on Intentgram. In summary, we also develop a web browser application mimicking a popular social media platform and show users social media content overlaid with these intent labels. From our analysis, around 70%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$70\\%$$\\end{document} users confirmed that tagging posts with intent labels helped them become more aware of the content consumed, and they would be open to experimenting with filtering content based on these labels. However, more extensive user evaluation is required to understand how adding such perceived intent labels mitigate the negative effects of social media.

MBCFNet: A Multimodal Brain–Computer Fusion Network for human intention recognition

Accurate recognition of human intent is crucial for effective human–computer speech interaction. Numerous intent understanding studies were based on speech-to-text transcription, which often overlook the influence of paralinguistic cues (such as speaker’s emotion, attitude, etc.), leading to misunderstandings, especially when identical textual content conveys multiple intents by means of different paralinguistic information. Considering that interaction intent is produced from the human brain, we propose a novel Multimodal Brain–Computer Fusion Network (MBCFNet) to discriminate the different intents carried by the identical textual information, in which the acoustic-textual representation was adapted by brain functional information through a cross-modal transformer. In the output module, a joint multi-task learning method was used to optimize both the primary task of intent recognition and the auxiliary task of emotion recognition of the speaker. To evaluate the model performance, we constructed a multimodal dataset CMSLIU that consists of acoustic, textual, and electroencephalograph (EEG) data of the same Chinese texts with varying intents. Experimental results on the self-constructed dataset indicate that the proposed model achieves state-of-the-art (SOTA) performance compared with other competing models. Ablation experiment results reveal that the model performance declines without information from EEG or audio modality, and also when the auxiliary task of emotion recognition is removed. All these results suggest the effectiveness of the proposed information fusion strategy in spoken language intent recognition and the brain–computer information fusion idea can also be extended to other similar fields.

Fusion inception and transformer network for continuous estimation of finger kinematics from surface electromyography.

Decoding surface electromyography (sEMG) to recognize human movement intentions enables us to achieve stable, natural and consistent control in the field of human computer interaction (HCI). In this paper, we present a novel deep learning (DL) model, named fusion inception and transformer network (FIT), which effectively models both local and global information on sequence data by fully leveraging the capabilities of Inception and Transformer networks. In the publicly available Ninapro dataset, we selected surface EMG signals from six typical hand grasping maneuvers in 10 subjects for predicting the values of the 10 most important joint angles in the hand. Our model's performance, assessed through Pearson's correlation coefficient (PCC), root mean square error (RMSE), and R-squared (R2) metrics, was compared with temporal convolutional network (TCN), long short-term memory network (LSTM), and bidirectional encoder representation from transformers model (BERT). Additionally, we also calculate the training time and the inference time of the models. The results show that FIT is the most performant, with excellent estimation accuracy and low computational cost. Our model contributes to the development of HCI technology and has significant practical value.

How to use one surface electromyography sensor to recognize six hand movements for a mechanical hand in real time: a method based on Morse code.

Surface electromyography (sEMG) signal is a kind of physiological signal reflecting muscle activity and muscle force. At present, the existing methods of recognizing human motion intention need more than two sensors to recognize more than two kinds of movements, the sensor pasting positions are special, and the hardware conditions for execution are high. In this work, a real-time motion intention recognition method based on Morse code is proposed and applied to the mechanical hand. The short-time and long-term muscle contraction signals collected by a single sEMG sensor were extracted and encoded with the Morse code method, and then the developed mapping method from Morse code to six hand movements were used to recognize hand movements. The average recognition accuracy of hand movements was 94.8704 ± 2.3556%, the average adjusting time was 34.89s for all subjects, and the execution time of a single movement was 381ms. The corresponding experiment video can be found in the attachment to show the experiment. The method proposed in this work is a method with one sensor to recognize six movements, low hardware conditions, high recognition accuracy, and insensitive to the difference of sensor pasting position.