Готові списки джерел за темами / Human intention prediction

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Автор: Grafiati
Опубліковано: 19 січня 2023

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Статті в журналах з теми "Human intention prediction":

1

Keshinro, Babatunde, Younho Seong, and Sun Yi. "Deep Learning-based human activity recognition using RGB images in Human-robot collaboration." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (September 2022): 1548–53. http://dx.doi.org/10.1177/1071181322661186.

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In human-robot interaction, to ensure safety and effectiveness, robots need to be able to accurately predict human intentions. Hidden Markov Model, Bayesian Filtering, and deep learning methods have been used to predict human intentions. However, few studies have explored deep learning methods to predict variant human intention. Our study aims to evaluate the performance of the human intent recognition inference algorithm, and its impact on the human-robot team for collaborative tasks. Two deep learning algorithms ConvLSTM and LRCN were used to predict human intention. A dataset of 10 participants performing Pick, Throw, Wave, and Carry actions was used. The ConvLSTM method had a prediction accuracy of 74%. The LRCN method had a lower prediction accuracy of 25% compared to ConvLSTM. This result shows that deep learning methods using RGB images can predict human intent with high accuracy. The proposed method is successful in predicting human intents underlying human behavior.
2

Archetti, Leonardo, Federica Ragni, Ludovic Saint-Bauzel, Agnès Roby-Brami, and Cinzia Amici. "Inclusive Human Intention Prediction with Wearable Sensors: Machine Learning Techniques for the Reaching Task Use Case." Engineering Proceedings 2, no. 1 (November 14, 2020): 13. http://dx.doi.org/10.3390/ecsa-7-08234.

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Human intentions prediction is gaining importance with the increase in human–robot interaction challenges in several contexts, such as industrial and clinical. This paper compares Linear Discriminant Analysis (LDA) and Random Forest (RF) performance in predicting the intention of moving towards a target during reaching movements on ten subjects wearing four electromagnetic sensors. LDA and RF prediction accuracy is compared to observation-sample dimension and noise presence, training and prediction time. Both algorithms achieved good accuracy, which improves as the sample dimension increases, although LDA presents better results for the current dataset.
3

Soratana, Teerachart, X. Jessie Yang, and Yili Liu. "Human Prediction of Robot’s Intention in Object Handling Tasks." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 65, no. 1 (September 2021): 1190–94. http://dx.doi.org/10.1177/1071181321651100.

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Trained human workers can predict the intentions of other workers from observed movement patterns when working collaboratively. The intentions prediction is crucial to identify their future actions. In human-machine teams, predictable movement patterns can enhance the interaction and improve team performance. In this article, we investigated the effects of different robot trajectory characteristics on the early prediction performance in human-machine teaming and on perceived robot’s human-likeness. The results showed that humans can predict the robot’s intention quicker and more accurately when the observed robot’s trajectory was generated with relatively lower energy expenditure. We found that the amount of jerk and acceleration in the robot’s joint-space affected perceived robot’s human-likeness.
4

Thang. "HUMAN ROBOT INTERACTIVE INTENTION PREDICTION USING DEEP LEARNING TECHNIQUES." Journal of Military Science and Technology, no. 72A (May 10, 2021): 1–12. http://dx.doi.org/10.54939/1859-1043.j.mst.72a.2021.1-12.

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In this research, we propose a method of human robot interactive intention prediction. The proposed algorithm makes use of a OpenPose library and a Long-short term memory deep learning neural network. The neural network observes the human posture in a time series, then predicts the human interactive intention. We train the deep neural network using dataset generated by us. The experimental results show that, our proposed method is able to predict the human robot interactive intention, providing 92% the accuracy on the testing set.
5

Ding, Zhen, Chifu Yang, Zhipeng Wang, Xunfeng Yin, and Feng Jiang. "Online Adaptive Prediction of Human Motion Intention Based on sEMG." Sensors 21, no. 8 (April 20, 2021): 2882. http://dx.doi.org/10.3390/s21082882.

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Accurate and reliable motion intention perception and prediction are keys to the exoskeleton control system. In this paper, a motion intention prediction algorithm based on sEMG signal is proposed to predict joint angle and heel strike time in advance. To ensure the accuracy and reliability of the prediction algorithm, the proposed method designs the sEMG feature extraction network and the online adaptation network. The feature extraction utilizes the convolution autoencoder network combined with muscle synergy characteristics to get the high-compression sEMG feature to aid motion prediction. The adaptation network ensures the proposed prediction method can still maintain a certain prediction accuracy even the sEMG signals distribution changes by adjusting some parameters of the feature extraction network and the prediction network online. Ten subjects were recruited to collect surface EMG data from nine muscles on the treadmill. The proposed prediction algorithm can predict the knee angle 101.25 ms in advance with 2.36 degrees accuracy. The proposed prediction algorithm also can predict the occurrence time of initial contact 236±9 ms in advance. Meanwhile, the proposed feature extraction method can achieve 90.71±3.42% accuracy of sEMG reconstruction and can guarantee 73.70±5.01% accuracy even when the distribution of sEMG is changed without any adjustment. The online adaptation network enhances the accuracy of sEMG reconstruction of CAE to 87.65±3.83% and decreases the angle prediction error from 4.03∘ to 2.36∘. The proposed method achieves effective motion prediction in advance and alleviates the influence caused by the non-stationary of sEMG.
6

Li, Shengchao, Lin Zhang, and Xiumin Diao. "Deep-Learning-based Human Intention Prediction with Data Augmentation." International Journal of Artificial Intelligence & Applications 13, no. 1 (January 31, 2022): 1–18. http://dx.doi.org/10.5121/ijaia.2022.13101.

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Data augmentation has been broadly applied in training deep-learning models to increase the diversity of data. This study ingestigates the effectiveness of different data augmentation methods for deep-learningbased human intention prediction when only limited training data is available. A human participant pitches a ball to nine potential targets in our experiment. We expect to predict which target the participant pitches the ball to. Firstly, the effectiveness of 10 data augmentation groups is evaluated on a single-participant data set using RGB images. Secondly, the best data augmentation method (i.e., random cropping) on the single-participant data set is further evaluated on a multi-participant data set to assess its generalization ability. Finally, the effectiveness of random cropping on fusion data of RGB images and optical flow is evaluated on both single- and multi-participant data sets. Experiment results show that: 1) Data augmentation methods that crop or deform images can improve the prediction performance; 2) Random cropping can be generalized to the multi-participant data set (prediction accuracy is improved from 50% to 57.4%); and 3) Random cropping with fusion data of RGB images and optical flow can further improve the prediction accuracy from 57.4% to 63.9% on the multi-participant data set.
7

Wang, Shoujin, Liang Hu, Yan Wang, Quan Z. Sheng, Mehmet Orgun, and Longbing Cao. "Intention Nets: Psychology-Inspired User Choice Behavior Modeling for Next-Basket Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6259–66. http://dx.doi.org/10.1609/aaai.v34i04.6093.

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Human behaviors are complex, which are often observed as a sequence of heterogeneous actions. In this paper, we take user choices for shopping baskets as a typical case to study the complexity of user behaviors. Most of existing approaches often model user behaviors in a mechanical way, namely treating a user action sequence as homogeneous sequential data, such as hourly temperatures, which fails to consider the complexity in user behaviors. In fact, users' choices are driven by certain underlying intentions (e.g., feeding the baby or relieving pain) according to Psychological theories. Moreover, the durations of intentions to drive user actions are quite different; some of them may be persistent while others may be transient. According to Psychological theories, we develop a hierarchical framework to describe the goal, intentions and action sequences, based on which, we design Intention Nets (IntNet). In IntNet, multiple Action Chain Nets are constructed to model the user actions driven by different intentions, and a specially designed Persistent-Transient Intention Unit models the different intention durations. We apply the IntNet to next-basket prediction, a recent challenging task in recommender systems. Extensive experiments on real-world datasets show the superiority of our Psychology-inspired model IntNet over the state-of-the-art approaches.
8

Ragni, Federica, Leonardo Archetti, Agnès Roby-Brami, Cinzia Amici, and Ludovic Saint-Bauzel. "Intention Prediction and Human Health Condition Detection in Reaching Tasks with Machine Learning Techniques." Sensors 21, no. 16 (August 4, 2021): 5253. http://dx.doi.org/10.3390/s21165253.

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Detecting human motion and predicting human intentions by analyzing body signals are challenging but fundamental steps for the implementation of applications presenting human–robot interaction in different contexts, such as robotic rehabilitation in clinical environments, or collaborative robots in industrial fields. Machine learning techniques (MLT) can face the limit of small data amounts, typical of this kind of applications. This paper studies the illustrative case of the reaching movement in 10 healthy subjects and 21 post-stroke patients, comparing the performance of linear discriminant analysis (LDA) and random forest (RF) in: (i) predicting the subject’s intention of moving towards a specific direction among a set of possible choices, (ii) detecting if the subject is moving according to a healthy or pathological pattern, and in the case of discriminating the damage location (left or right hemisphere). Data were captured with wearable electromagnetic sensors, and a sub-section of the acquired signals was required for the analyses. The possibility of detecting with which arm (left or right hand) the motion was performed, and the sensitivity of the MLT to variations in the length of the signal sub-section were also evaluated. LDA and RF prediction accuracies were compared: Accuracy improves when only healthy subjects or longer signals portions are considered up to 11% and at least 10%, respectively. RF reveals better estimation performance both as intention predictor (on average 59.91% versus the 62.19% of LDA), and health condition detector (over 90% in all the tests).
9

Zhang, Lin, Shengchao Li, Hao Xiong, Xiumin Diao, and Ou Ma. "An Application of Convolutional Neural Networks on Human Intention Prediction." International Journal of Artificial Intelligence & Applications 10, no. 5 (September 30, 2019): 1–11. http://dx.doi.org/10.5121/ijaia.2019.10501.

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10

Chereshnev, Roman, and Attila Kertész-Farkas. "GaIn: Human Gait Inference for Lower Limbic Prostheses for Patients Suffering from Double Trans-Femoral Amputation." Sensors 18, no. 12 (November 26, 2018): 4146. http://dx.doi.org/10.3390/s18124146.

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Several studies have analyzed human gait data obtained from inertial gyroscope and accelerometer sensors mounted on different parts of the body. In this article, we take a step further in gait analysis and provide a methodology for predicting the movements of the legs, which can be applied in prosthesis to imitate the missing part of the leg in walking. In particular, we propose a method, called GaIn, to control non-invasive, robotic, prosthetic legs. GaIn can infer the movements of both missing shanks and feet for humans suffering from double trans-femoral amputation using biologically inspired recurrent neural networks. Predictions are performed for casual walking related activities such as walking, taking stairs, and running based on thigh movement. In our experimental tests, GaIn achieved a 4.55 prediction error for shank movements on average. However, a patient’s intention to stand up and sit down cannot be inferred from thigh movements. In fact, intention causes thigh movements while the shanks and feet remain roughly still. The GaIn system can be triggered by thigh muscle activities measured with electromyography (EMG) sensors to make robotic prosthetic legs perform standing up and sitting down actions. The GaIn system has low prediction latency and is fast and computationally inexpensive to be deployed on mobile platforms and portable devices.
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Статті в журналах

Дисертації з теми "Human intention prediction":

1

Conte, Dean Edward. "Autonomous Robotic Escort Incorporating Motion Prediction with Human Intention." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/102581.

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This thesis presents a framework for a mobile robot to escort a human to their destination successfully and efficiently. The proposed technique uses accurate path prediction incorporating human intention to locate the robot in front of the human while walking. Human intention is inferred by the head pose, an effective past-proven implicit indicator of intention, and fused with conventional physics-based motion prediction. The human trajectory is estimated and predicted using a particle filter because of the human's nonlinear and non-Gaussian behavior, and the robot control action is determined from the predicted human pose allowing for anticipative autonomous escorting. Experimental analysis shows that the incorporation of the proposed human intention model reduces human position prediction error by approximately 35% when turning. Furthermore, experimental validation with an omnidirectional mobile robotic platform shows escorting up to 50% more accurate compared to the conventional techniques, while achieving 97% success rate.
Master of Science
This thesis presents a method for a mobile robot to escort a human to their destination successfully and efficiently. The proposed technique uses human intention to predict the walk path allowing the robot to be in front of the human while walking. Human intention is inferred by the head direction, an effective past-proven indicator of intention, and is combined with conventional motion prediction. The robot motion is then determined from the predicted human position allowing for anticipative autonomous escorting. Experimental analysis shows that the incorporation of the proposed human intention reduces human position prediction error by approximately 35% when turning. Furthermore, experimental validation with an mobile robotic platform shows escorting up to 50% more accurate compared to the conventional techniques, while achieving 97% success rate. The unique escorting interaction method proposed has applications such as touch-less shopping cart robots, exercise companions, collaborative rescue robots, and sanitary transportation for hospitals.
2

Kurt, Ugur Halis. "Anticipation of Human Movements : Analyzing Human Action and Intention: An Experimental Serious Game Approach." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15777.

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What is the difference between intention and action? To start answering this complex question, we have created a serious game that allows us to capture a large quantity of experimental data and study human behavior. In the game, users catch flies, presented to the left or to the right of the screen, by dragging the tongue of a frog across a touchscreen monitor. The movement of interest has a predefined starting point (the frog) and necessarily transits through a via-point (a narrow corridor) before it proceeds to the chosen left/right direction. Meanwhile, the game collects data about the movement performed by the player. This work is focused on the analysis of such movements. We try to find criteria that will allow us to predict (as early as possible) the direction (left/right) chosen by the player. This is done by analyzing kinematic information (e.g. trajectory, velocity profile, etc.). Also, processing such data according to the dynamical movement primitives approach, allows us to find further criteria that support a classification of human movement. Our preliminary results show that individually considered, participants tend to create and use stereotypical behaviors that can be used to formulate predictions about the subjects’ intention to reach in one direction or the other, early after the onset of the movement.
3

Casallas, suarez Juan Sebastian. "Prediction of user action in moving-target selection tasks." Thesis, Paris, ENSAM, 2015. http://www.theses.fr/2015ENAM0018/document.

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La sélection de cibles en mouvement est une tâche courante et complexe dans l'interaction homme-machine (IHM) en général et en particulier dans le domaine de la réalité virtuelle (RV). La prédiction de l'action est une solution intégrale pour aborder les problèmes liés à l'interaction. Cependant, les techniques actuelles de prédiction sont basées sur le suivi continu des actions de l'utilisateur sans prendre en compte la possibilité que les actions d'atteinte d'une cible puissent avoir une composante importante préprogrammée—cette théorie est appelé la théorie du contrôle préprogrammé.En se basant sur la théorie du contrôle préprogrammé, cette thèse explore la possibilité de prédire les actions, avant leur exécution, de sélection d'objets en mouvement. Plus spécifiquement, trois niveaux de prédiction d'action sont étudiés : 1) la performance des actions, mesurée par le temps de mouvement (TM) nécessaire pour atteindre une cible, 2) la difficulté prospective (DP), qui représente la difficulté subjective de la tâche estimée avant son exécution, 3) l'intention de l'utilisateur, qui indique la cible visée par l'utilisateur.Dans le cadre de cette thèse, des modèles de prédiction d'intention sont développés à l'aide des arbres de décision ainsi que des fonctions de classement—ces modèles sont évalués dans deux expériences en RV. Des modèles 1-D et 2-D de DP pour des cibles en mouvement basés sur la loi de Fitts sont développés et évalués dans une expérience en ligne. Enfin, des modèles de TM avec les mêmes caractéristiques structurelles des modèles de DP sont évaluées dans une expérience 3-D en RV
Selection of moving targets is a common, yet complex task in human–computer interaction (HCI), and more specifically in virtual reality (VR). Action prediction has proven to be the most comprehensive enhancement to address moving-target selection challenges. Current predictive techniques, however, heavily rely on continuous tracking of user actions, without considering the possibility that target-reaching actions may have a dominant pre-programmed component—this theory is known as the pre-programmed control theory.Thus, based on the pre-programmed control theory, this research explores the possibility of predicting moving-target selection prior to action execution. Specifically, three levels of action prediction are investigated: 1) action performance measured as the movement time (MT) required to reach a target, 2) prospective difficulty (PD), i.e., subjective assessments made prior to action execution; and 3) intention, i.e., the target that the user plans to reach.In this dissertation, intention prediction models are developed using decision trees and scoring functions—these models are evaluated in two VR studies. PD models for 1-D, and 2-D moving- target selection tasks are developed based on Fitts' Law, and evaluated in an online experiment. Finally, MT models with the same structural form of the aforementioned PD models are evaluated in a 3-D moving-target selection experiment deployed in VR
4

Guda, Vamsi Krishna. "Contributions à l'utilisation de cobots comme interfaces haptiques à contact intermittent en réalité virtuelle." Thesis, Ecole centrale de Nantes, 2022. http://www.theses.fr/2022ECDN0033.

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La réalité virtuelle (RV) est de plus en plus utilisée dans des simulations industrielles mais la possibilité de toucher les objets manque rapidement par exemple pour juger de la qualité perçue dans la conception de véhicule automobile. Les interfaces haptiques actuels ne permettent de restituer aisément la notion de texture, l’approche envisagée est donc une interface à contact intermittent. Un cobot vient positionner une surface mobile à l’endroit du contact avec un objet virtuel pour permettre un contact physique avec la main de l’opérateur.Les contributions de cette thèse portent sur plusieurs aspects : le placement du robot, la modélisation de l’opérateur, la gestion du déplacement et de la vitesse du robot et la détection des intentions de l’opérateur. Le placement du robot est choisi pourpermettre d’atteindre les différentes zones de travail et pour assurer une sécurité passive en rendant impossible au robot de heurter la têteet le buste de l’opérateur en position normale de travail, i.e. assis dans un fauteuil. Un modèle de l'utilisateur, incluant un torse et desbras, est conçu et testé pour suivre les mouvements de l'utilisateur en temps réel. L’interaction est possible sur un ensemble de pose prédéfinies que l’utilisateur enchaine comme il le désire. Différentes stratégies sont proposées pour prédire les intentions de l'utilisateur. Les aspects clés de la prédiction sont basés sur la direction du regard et la position de la main de l'utilisateur. Une étudeexpérimentale ainsi que l'analyse qui en découle montrent l’apport de la prise en compte de la direction du regard. L’intérêt d’introduire des points dit « de sécurité » pour éloigner le robot de l’opérateur et permettre des déplacements rapides du robot est mis en évidence
Virtual reality (VR) is evolving and being used in industrial simulations but the possibility to touch objects is missing, for example to judge the perceived quality in the design of a car. The current haptic interfaces do not allow to easily restore the notion of texture, therefore an approach is considered “intermittent contact interface” to achieve this. A cobot positions a mobile surface at the point of contact with a virtual object to allow physical contact with the operator's hand. The contributions of this thesis concern several aspects: the placement of the robot, the modeling of the operator, the management of the displacement and the speed of the robot and the detection of the operator's intentions. The placement of the robot is chosen to allow reaching the different working areas and to ensure passive safety by making it impossible for the robot to hit the head and chest of the operator in a normal working position, i.e. sitting in a chair. A model of the user, including a torso and arms, is designed and tested to follow the user's movements in real time Interaction is possible on a set of predefined poses that the user chains together as desired. Different strategies are proposed to predict the user's intentions. The key aspects of the prediction are based on the gaze direction and the hand position of the user. An experimental study as well as the resulting analysis show the contribution of taking into account the gaze direction. The interest of introducing "safety" points to move the robot away from the operator and allow fast robot movements is highlighted
5

Brinkerhoff, Bobbie. "Predicting intentions to donate to human service nonprofits and public broadcasting organizations using a revised theory of planned behavior." Master's thesis, University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4858.

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Different types of nonprofit organizations including human service nonprofits like homeless shelters, public broadcasting organizations, and the like thrive on donations. Effective fundraising techniques are essential to a nonprofit's existence. This research study explored a revised theory of planned behavior to include guilt and convenience in order to understand whether these factors are important in donors' intentions to give. This study also examined the impact of two different kinds of guilt; anticipated guilt and existential guilt to determine if there was any difference between the types of guilt and the roles that they play as predicting factors in a revised TPB model. This study also explored how human service nonprofits and public broadcasting organizations compare in the factors that help better predict their donating intentions. An online survey was administered to a convenience sample, and hierarchical regression analysis was used to determine significant predicting factors within each revised TPB model. This study confirmed that the standard theory of planned behavior model was a significant predictor of intentions to donate for donors of both human service nonprofits and public broadcasting organizations. However, in both contexts, not all traditional factors of the TPB model contributed to the donation intentions. This study also provides further evidence that guilt can increase the predictive value of the standard TPB model for both types of nonprofits. Anticipated guilt more specifically, was a significant predicting factor for donors' intentions to give to public broadcasting organizations. In contrast, convenience did not affect the explanatory power of the TPB model in either context. The TPB models for the two nonprofits are compared and theoretical and practical explanations are discussed.
ID: 030423371; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (M.A.)--University of Central Florida, 2011.; Includes bibliographical references (p. 88-91).
M.A.
Masters
Sciences
6

Babu, Saravana Prashanth Murali, and 巴. 神. 樂. "Multi-Sensing Intention Prediction of a Human Wearing a Powered Lower Limb Exoskeleton." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/f33uya.

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碩士
國立交通大學
電機資訊國際學程
106
We propose an exoskeleton’s intention prediction method for adaptive learning of assistive joint torque profiles in periodic tasks. Assistive devices, like exoskeletons or prosthesis, often make use of physiological data that allow the detection or prediction of movement onset. Movement onset can be detected at the executing site, the skeletal muscles, by means of EMG or inertial sensors. The ultimate goal of the research is to detect and predict the human movement activity and orientation signaling an assistive joint torque behavior in a way that the movement activity of the exoskeleton system can be modified. An experimental investigation is carried out with the placement of IMU sensors at the lower limb positions to acquire the orientation, shift in the center of mass (COM) and the change in velocity of the human-robot interaction to know the muscle activity during human locomotion. Force sensors are placed at the bottom of the foot to acquire the center of pressure (COP) and the ground reaction force (GRF) in alignment to exoskeleton and human locomotion. Based on the acquired data indigenous gait algorithm is built for the maximum possible walking patterns. Our proposed learning system uses GAIT algorithm as a trajectory generator, and parameters of GAIT are modulated using linear regression. Then, in the future, the learning system will be combined with the dynamics of the human-robot structure to alter the desired dynamics as the final command to the main controller. The advantage of the proposed method is that it does not require specific biomechanical models as the system can adapt itself to predict the intention of the user to have efficient human robot interaction between the human and exoskeleton robot.
7

Biswas, Kumar Krishna. "Predicting the intention of top managers in Bangladesh to appoint women to senior management positions: an examination and extension of the theory of planned behaviour." Thesis, 2013. http://hdl.handle.net/1959.13/940609.

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Research Doctorate - Doctor of Philosophy (PhD)
There is a consensus that women are underrepresented in senior management positions across the world. Since the early 1970s, researchers have been exploring the factors and forces contributing to the low presence of women in senior management roles. Theoretical and empirical scholarship suggests that women’s advancement to senior management positions is not only affected by personal factors such as qualifications, experiences and aspiration to ascend to senior leadership positions but also by the positive effect of structural factors such as human resource policies and practices, organisational climate and attitudinal factors such as stereotypical attitudes toward women as managers. At the organisational level, most prior studies have identified both structural and attitudinal factors that create barriers to women advancing to senior management positions; however, there is a knowledge gap concerning how these organisational factors influence the intention of top managers to promote women to senior management positions. Ajzen’s Theory of Planned Behaviour (TPB) suggests that people’s (behavioural) intention is an immediate determinant of enacting the behaviour in question. To predict and understand the future pattern of women’s presence in senior management positions, it is imperative to examine the intention of top managers to promote women to senior management positions that leads to actual behaviour associated with promoting women. Therefore, this study adopts a positivist-quantitative research paradigm and develops a TPB-based research model. To examine this model, primary data were collected from 182 human resource managers in Bangladesh through the use of a cross-sectional, self-administered survey (online and paper). Partial least squares based structural equation modelling (PLS-SEM) analysis reveals that positive attitudes toward women as managers, anticipated affective reactions, organisational climate, human resources policies and practices, and subjective norms have a significant influence on the intention of top managers to promote women to senior management positions. Additionally, the results of bootstrapped confidence analyses indicate that anticipated affective reactions and attitudes toward the promotion of women to senior management positions mediate the relationship between attitudes toward women as managers and the intention of top managers to promote women to senior management positions. Similarly, subjective norms mediate the relationship between organisational climate and the intention of top managers to promote women as well as the relationship between human resource policies and practices and the intention to promote women. The findings of this study also justify the inclusion of structural and attitudinal variables within the TPB framework. Thus, this study extends and validates the predictive capability of the TPB in the field of human resource management and has implications for initiatives addressing gender equity in relation to senior management roles.
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Smith, Sarah J., C. Souchay, and C. J. A. Moulin. "Metamemory and prospective memory in Parkinson's disease." 2011. http://hdl.handle.net/10454/6198.

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OBJECTIVE: Metamemory is integral for strategizing about memory intentions. This study investigated the prospective memory (PM) deficit in Parkinson's disease (PD) from a metamemory viewpoint, with the aim of examining whether metamemory deficits might contribute to PM deficits in PD. METHOD: Sixteen patients with PD and 16 healthy older adult controls completed a time-based PM task (initiating a key press at two specified times during an ongoing task), and an event-based PM task (initiating a key press in response to animal words during an ongoing task). To measure metamemory participants were asked to predict and postdict their memory performance before and after completing the tasks, as well as complete a self-report questionnaire regarding their everyday memory function. RESULTS: The PD group had no impairment, relative to controls, on the event-based task, but had prospective (initiating the key press) and retrospective (recalling the instructions) impairments on the time-based task. The PD group also had metamemory impairments on the time-based task; they were inaccurate at predicting their performance before doing the task but, became accurate when making postdictions. This suggests impaired metamemory knowledge but preserved metamemory monitoring. There were no group differences regarding PD patients' self-reported PM performance on the questionnaire. CONCLUSIONS: These results reinforce previous findings that PM impairments in PD are dependent on task type. Several accounts of PM failures in time-based tasks are presented, in particular, ways in which mnemonic and metacognitive deficits may contribute to the difficulties observed on the time-based task.

Книги з теми "Human intention prediction":

1

Zawidzki, Tadeusz. The Many Roles of the Intentional Stance. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199367511.003.0003.

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Can the intentional stance play all of the roles Dennett claims that it must play? There is reason for skepticism about the suitability of the intentional stance as an analysis of mature, person-level, intentional concepts. In part this is because of the dynamic and socially situated structure of our interpersonal practices. In part this is because folk-ascriptions of mentality are often guided by regulative concerns with impression management and identity construction. But scientific practice often relies on intentional states that are characterized in terms of their predictive and explanatory roles; and most humans employ tacit cognitive resources with a similar character when they make quick and efficient behavioral anticipations. In light of these considerations, it is unlikely a single set of explanatory norms will be operative in practices of quotidian interpretation, scientific explanation, and philosophical naturalization.

Частини книг з теми "Human intention prediction":

1

Lee, Seungyup, Juwan Yoo, and Da Young Ju. "Data Preloading Technique using Intention Prediction." In Human-Computer Interaction. Applications and Services, 32–41. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07227-2_4.

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Manstetten, Dietrich. "Behaviour Prediction and Intention Detection in UR:BAN VIE – Overview and Introduction." In UR:BAN Human Factors in Traffic, 153–62. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-15418-9_8.

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Ahmadi, Ehsan, Ali Ghorbandaei Pour, Alireza Siamy, Alireza Taheri, and Ali Meghdari. "Playing Rock-Paper-Scissors with RASA: A Case Study on Intention Prediction in Human-Robot Interactive Games." In Social Robotics, 347–57. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35888-4_32.

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von Neuforn, Daniela Stokar, and Katrin Franke. "Reading Between the Lines: Human-centred Classification of Communication Patterns and Intentions." In Social Computing, Behavioral Modeling, and Prediction, 218–28. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-77672-9_24.

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Dutta, Vibekananda, and Teresa Zielinska. "Predicting the Intention of Human Activities for Real-Time Human-Robot Interaction (HRI)." In Social Robotics, 723–34. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47437-3_71.

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6

Galluccio, Carla, Rosa Fabbricatore, and Daniela Caso. "Exploring the intention to walk: a study on undergraduate students using item response theory and theory of planned behaviour." In Proceedings e report, 153–58. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.30.

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Physical activity is one of the most basic human functions, and it is an important foundation of health throughout life. Physical activity apports benefit on both physical and mental health, reducing the risk of several diseases and lowering stress reactions, anxiety and depression. More specifically, physical activity is defined as "any bodily movement produced by skeletal muscles that require energy expenditure" (World Health Organization), including in this definition several activities. Among them, walking has been shown to improve physical and mental well-being in every age group. Despite that, insufficient walking among university students has been increasingly reported, requiring walking promotion intervention. In order to do this, dividing students based on their intention to walk might be useful since the intention is considered as the best predictor of behaviour. In this work, we carried out a study on university students' intention to walk and some of its predictors by exploiting Item Response Theory (IRT) models. In particular, we inspected the predictors of intention by mean of Rating Scale Graded Response Model (RS-GRM). Then we used the Latent Class IRT model to divide students according to their intention to walk, including predictors' scores as covariates. We chose the intention's predictors according to an extension of the Theory of Planned Behaviour (TPB), with both classic and additional variables. The formers are attitude toward behaviour, subjective norms, and perceived behavioural control, whereas we used risk perception, self-efficacy, anticipation, self-identity and anticipated regret as additional variables. Data was collected administrating a self-report questionnaire to undergraduate students enrolled in the Psychology course at Federico II University of Naples.
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Casallas, Juan Sebastián, James H. Oliver, Jonathan W. Kelly, Frédéric Merienne, and Samir Garbaya. "Towards a Model for Predicting Intention in 3D Moving-Target Selection Tasks." In Engineering Psychology and Cognitive Ergonomics. Understanding Human Cognition, 13–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39360-0_2.

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8

Clodic, Aurelie, and Rachid Alami. "What Is It to Implement a Human-Robot Joint Action?" In Robotics, AI, and Humanity, 229–38. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54173-6_19.

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AbstractJoint action in the sphere of human–human interrelations may be a model for human–robot interactions. Human–human interrelations are only possible when several prerequisites are met, inter alia: (1) that each agent has a representation within itself of its distinction from the other so that their respective tasks can be coordinated; (2) each agent attends to the same object, is aware of that fact, and the two sets of “attentions” are causally connected; and (3) each agent understands the other’s action as intentional. The authors explain how human–robot interaction can benefit from the same threefold pattern. In this context, two key problems emerge. First, how can a robot be programed to recognize its distinction from a human subject in the same space, to detect when a human agent is attending to something, to produce signals which exhibit their internal state and make decisions about the goal-directedness of the other’s actions such that the appropriate predictions can be made? Second, what must humans learn about robots so they are able to interact reliably with them in view of a shared goal? This dual process is here examined by reference to the laboratory case of a human and a robot who team up in building a stack with four blocks.
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Wernsdorfer, Mark, and Ute Schmid. "From Streams of Observations to Knowledge-Level Productive Predictions." In Human Behavior Recognition Technologies, 268–81. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3682-8.ch013.

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The benefit to be gained by Ambient Assisted Living (AAL) systems depends heavily on the successful recognition of human intentions. Important indicators for specific intentions are behavior and situational context. Once a sequence of actions can be associated with a specific intention, assistance may be provided by anticipating the next individual step and supporting the human in its execution. The authors present a combination of Sequence Abstraction Networks (SAN) and IGOR to guarantee early and impartial predictions with a powerful detection for symbolic regularities. They first generate a hierarchy of abstract action sequences, where individual contexts represent subgoals or minor intentions. Afterwards, they enrich this hierarchy by recursive induction. An example scenario is presented where a table needs to be set for several guests. It turns out that correct predictions can be made while still executing the observed sequence for the first time. Support can therefore be completely individual to the person being assisted but nonetheless be very dynamic and quick in anticipating the next steps.
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Debowski, Lukasz. "Entropic Subextensivity in Language and Learning." In Nonextensive Entropy. Oxford University Press, 2004. http://dx.doi.org/10.1093/oso/9780195159769.003.0024.

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In this chapter, we identify possible links between theoretical computer science, coding theory, and statistics reinforced by subextensivity of Shannon entropy. Our specific intention is to address these links in a way that may arise from a rudimentary theory of human learning from language communication. The semi-infinite stream of language production that a human being experiences during his or her life will be called simply parole (= "speech," [7]). Although modern computational linguistics tries to explain human language competence in terms of explicit mathematical models in order to enable its machine simulation [17, 20], modeling parole itself (widely known as "language modeling") is not trivial in a very obscure way. When a behavior of parole that improves its prediction is newly observed in a finite portion of the empirical data, it often suggests only minor improvements to the current model. When we use larger portions of parole to test the freshly improved model, this model always fails seriously, but in a different way. How we can provide necessary updates to, with- out harming the integrity of, the model is an important problem that experts must continually solve. Is there any sufficiently good definition of parole that is ready-made for industrial applications? Although not all readers of human texts learn continuously, parole is a product of those who can and often do learn throughout their lives. Thus, we assume that the amount of knowledge generalizable from a finite window of parole should diverge to infinity when the length of the window also tends to infinity. Many linguists assume that a very distinct part of the generalizable knowledge is "linguistic knowledge," which can be finite in principle. Nevertheless, for the sake of good modeling of parole in practical applications, it is useless to restrict ourselves solely to "finite linguistic knowledge" [6, 22]. Inspired by Crutchfield and Feldman [5], we will call any processes (distributions of infinite linear data) "finitary" when the amount of knowledge generalizable from them is finite, and "infinitary" when it is infinite. The crucial point is to accurately define the notion of knowledge generalized from a data sample. According to the principle of minimum description length (MDL), generalizable knowledge is the definition of such representation for the data which yields the shortest total description. In this case, we will define infinitarity as computational infinitarity (CIF).

Тези доповідей конференцій з теми "Human intention prediction":

1

Wang, Junyi, and Xinyu Su. "Enriching Intention of Human Motion Prediction." In ICCDE 2020: 2020 The 6th International Conference on Computing and Data Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3379247.3379295.

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2

Wang, Weitian, Rui Li, Yi Chen, and Yunyi Jia. "Human Intention Prediction in Human-Robot Collaborative Tasks." In HRI '18: ACM/IEEE International Conference on Human-Robot Interaction. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3173386.3177025.

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3

Li, Shengchao, Lin Zhang, and Xiumin Diao. "Improving Human Intention Prediction Using Data Augmentation." In 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, 2018. http://dx.doi.org/10.1109/roman.2018.8525781.

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Phillips, Derek J., Tim A. Wheeler, and Mykel J. Kochenderfer. "Generalizable intention prediction of human drivers at intersections." In 2017 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2017. http://dx.doi.org/10.1109/ivs.2017.7995948.

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Lu, Weifeng, Zhe Hu, and Jia Pan. "Human-Robot Collaboration using Variable Admittance Control and Human Intention Prediction." In 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). IEEE, 2020. http://dx.doi.org/10.1109/case48305.2020.9217040.

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6

Conte, Dean, and Tomonari Furukawa. "Autonomous Robotic Escort Incorporating Motion Prediction and Human Intention." In 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021. http://dx.doi.org/10.1109/icra48506.2021.9561469.

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7

Wang, Shoujin, Liang Hu, Yan Wang, Quan Z. Sheng, Mehmet Orgun, and Longbing Cao. "Intention2Basket: A Neural Intention-driven Approach for Dynamic Next-basket Planning." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/323.

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User purchase behaviours are complex and dynamic, which are usually observed as multiple choice actions across a sequence of shopping baskets. Most of the existing next-basket prediction approaches model user actions as homogeneous sequence data without considering complex and heterogeneous user intentions, impeding deep under-standing of user behaviours from the perspective of human inside drivers and thus reducing the prediction performance. Psychological theories have indicated that user actions are essentially driven by certain underlying intentions (e.g., diet and entertainment). Moreover, different intentions may influence each other while different choices usually have different utilities to accomplish an intention. Inspired by such psychological insights, we formalize the next-basket prediction as an Intention Recognition, Modelling and Accomplishing problem and further design the Intention2Basket (Int2Ba in short) model. In Int2Ba, an Intention Recognizer, a Coupled Intention Chain Net, and a Dynamic Basket Planner are specifically designed to respectively recognize, model and accomplish the heterogeneous intentions behind a sequence of baskets to better plan the next-basket. Extensive experiments on real-world datasets show the superiority of Int2Ba over the state-of-the-art approaches.
8

Luo, Ren C., and Licong Mai. "Human Intention Inference and On-Line Human Hand Motion Prediction for Human-Robot Collaboration." In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019. http://dx.doi.org/10.1109/iros40897.2019.8968192.

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Sung Park, Jae, Chonhyon Park, and Dinesh Manocha. "Intention-Aware Motion Planning Using Learning Based Human Motion Prediction." In Robotics: Science and Systems 2017. Robotics: Science and Systems Foundation, 2017. http://dx.doi.org/10.15607/rss.2017.xiii.045.

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10

Zhang, Lin, Xiumin Diao, and Ou Ma. "A Preliminary Study on a Robot's Prediction of Human Intention." In 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, 2017. http://dx.doi.org/10.1109/cyber.2017.8446086.

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