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Journal articles on the topic 'Human intention prediction'
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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).
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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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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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Chen, Ting, Youjing Chen, Hao Li, Tao Gao, Huizhao Tu, and Siyu Li. "Driver Intent-Based Intersection Autonomous Driving Collision Avoidance Reinforcement Learning Algorithm." Sensors 22, no. 24 (December 16, 2022): 9943. http://dx.doi.org/10.3390/s22249943.
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With the rapid development of artificial intelligent technology, the deep learning method is widely applied to predict human driving intentions due to its relative accuracy of prediction, which is one of critical links for security guarantee in the distributed, mixed driving scenario. In order to sense the intention of human-driven vehicles and reduce the self-driving collision avoidance rate, an improved intention prediction method for human-driving vehicles based on unsupervised, deep inverse reinforcement learning is proposed. Firstly, a contrast discriminator module was proposed to extract richer features. Then, the residual module was created to overcome the drawbacks of gradient disappearance and network degradation with the increase in network layers. Furthermore, the dropout layer was generated to prevent the over-fitting phenomenon in the whole training process of the GRU network, so as to improve the generalization ability of the network model. Finally, abundant experiments were conducted on datasets to evaluate our proposed method. The pass rate of self-driving vehicles with conservative driver probabilities of p = 0.25, p = 0.4, and p = 0.6 improved by a maximum of 8%, 10%, and 3%, compared with the classical method LSTM and VAE + RNN. It indicates that the prediction results of our proposed method fit more with the basic structure of the given traffic scenario in a long-term prediction range, which verifies the effectiveness of our proposed method.
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Jin, Canghong, Zhiwei Lin, and Minghui Wu. "Augmented Intention Model for Next-Location Prediction from Graphical Trajectory Context." Wireless Communications and Mobile Computing 2019 (December 26, 2019): 1–12. http://dx.doi.org/10.1155/2019/2860165.
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Human trajectory prediction is an essential task for various applications such as travel recommendation, location-sensitive advertisement, and traffic planning. Most existing approaches are sequential-model based and produce a prediction by mining behavior patterns. However, the effectiveness of pattern-based methods is not as good as expected in real-life conditions, such as data sparse or data missing. Moreover, due to the technical limitations of sensors or the traffic situation at the given time, people going to the same place may produce different trajectories. Even for people traveling along the same route, the observed transit records are not exactly the same. Therefore trajectories are always diverse, and extracting user intention from trajectories is difficult. In this paper, we propose an augmented-intention recurrent neural network (AI-RNN) model to predict locations in diverse trajectories. We first propose three strategies to generate graph structures to demonstrate travel context and then leverage graph convolutional networks to augment user travel intentions under graph view. Finally, we use gated recurrent units with augmented node vectors to predict human trajectories. We experiment with two representative real-life datasets and evaluate the performance of the proposed model by comparing its results with those of other state-of-the-art models. The results demonstrate that the AI-RNN model outperforms other methods in terms of top-k accuracy, especially in scenarios with low similarity.
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Fan, Rong, Suqin Sun, Yuanli Shan, Qingjiang Zhang, Jiazhuo Li, Guoqing Ma, and Limin Pan. "The Metabolism Grey Prediction Model Based on Big Data and Internet of Things Technology." Wireless Communications and Mobile Computing 2022 (April 14, 2022): 1–9. http://dx.doi.org/10.1155/2022/6106995.
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In view of the uncertainty and diversity of the metabolic grey prediction model in the prediction process, resulting in poor prediction effect, a metabolic grey prediction model based on big data and Internet of things technology is constructed. Establish the aerobic metabolic process of human telecontrol and put forward the detection index of aerobic metabolic cycle function in human telecontrol; on this basis, use the metabolic grey prediction model analysis algorithm to determine the active intrusion intention of complex network, establish the intrusion intention attack behavior set function, establish the internal operation architecture under the technology of big data and Internet of things, and realize the construction of metabolic grey prediction model. The experimental results show that the constructed model can realize data prediction, with high confidence level and good effect.
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Song, Weilong, Guangming Xiong, and Huiyan Chen. "Intention-Aware Autonomous Driving Decision-Making in an Uncontrolled Intersection." Mathematical Problems in Engineering 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/1025349.
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Autonomous vehicles need to perform social accepted behaviors in complex urban scenarios including human-driven vehicles with uncertain intentions. This leads to many difficult decision-making problems, such as deciding a lane change maneuver and generating policies to pass through intersections. In this paper, we propose an intention-aware decision-making algorithm to solve this challenging problem in an uncontrolled intersection scenario. In order to consider uncertain intentions, we first develop a continuous hidden Markov model to predict both the high-level motion intention (e.g., turn right, turn left, and go straight) and the low level interaction intentions (e.g., yield status for related vehicles). Then a partially observable Markov decision process (POMDP) is built to model the general decision-making framework. Due to the difficulty in solving POMDP, we use proper assumptions and approximations to simplify this problem. A human-like policy generation mechanism is used to generate the possible candidates. Human-driven vehicles’ future motion model is proposed to be applied in state transition process and the intention is updated during each prediction time step. The reward function, which considers the driving safety, traffic laws, time efficiency, and so forth, is designed to calculate the optimal policy. Finally, our method is evaluated in simulation with PreScan software and a driving simulator. The experiments show that our method could lead autonomous vehicle to pass through uncontrolled intersections safely and efficiently.
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Park, Jae Sung, Chonhyon Park, and Dinesh Manocha. "I-Planner: Intention-aware motion planning using learning-based human motion prediction." International Journal of Robotics Research 38, no. 1 (November 30, 2018): 23–39. http://dx.doi.org/10.1177/0278364918812981.
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We present a motion planning algorithm to compute collision-free and smooth trajectories for high-degree-of-freedom (high-DOF) robots interacting with humans in a shared workspace. Our approach uses offline learning of human actions along with temporal coherence to predict the human actions. Our intention-aware online planning algorithm uses the learned database to compute a reliable trajectory based on the predicted actions. We represent the predicted human motion using a Gaussian distribution and compute tight upper bounds on collision probabilities for safe motion planning. We also describe novel techniques to account for noise in human motion prediction. We highlight the performance of our planning algorithm in complex simulated scenarios and real-world benchmarks with 7-DOF robot arms operating in a workspace with a human performing complex tasks. We demonstrate the benefits of our intention-aware planner in terms of computing safe trajectories in such uncertain environments.
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Weinreuter, Hannes, Jonas Imbsweiler, Nadine-Rebecca Strelau, Barbara Deml, and Fernando Puente León. "Prediction of human driver intentions at a narrow passage in inner city traffic / Intentionsprädiktion menschlicher Fahrer an einer Engstelle im innerstädtischen Straßenverkehr." tm - Technisches Messen 86, s1 (September 1, 2019): 127–31. http://dx.doi.org/10.1515/teme-2019-0063.
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AbstractAutonomous vehicles have to be able to predict whether a human driver will wait at an unregulated inner city narrow passage or not to adapt its behaviour accordingly. To this end, a driving simulator study was conducted in which participiants were subjected to different cooperation behaviours during their approach to a narrow passage. They were asked to rate their intention afterwards. From the recorded trajectories, features which are specific to the scenario are derived. Therewith, Random Forest and Conditional Random Field classifiers for both intention and behaviour prediction are trained. The results show that robust prediction of driver intention and behaviour is possible.
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Zunino, Andrea, Jacopo Cavazza, Riccardo Volpi, Pietro Morerio, Andrea Cavallo, Cristina Becchio, and Vittorio Murino. "Predicting Intentions from Motion: The Subject-Adversarial Adaptation Approach." International Journal of Computer Vision 128, no. 1 (September 18, 2019): 220–39. http://dx.doi.org/10.1007/s11263-019-01234-9.
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Abstract This paper aims at investigating the action prediction problem from a pure kinematic perspective. Specifically, we address the problem of recognizing future actions, indeed human intentions, underlying a same initial (and apparently unrelated) motor act. This study is inspired by neuroscientific findings asserting that motor acts at the very onset are embedding information about the intention with which are performed, even when different intentions originate from a same class of movements. To demonstrate this claim in computational and empirical terms, we designed an ad hoc experiment and built a new 3D and 2D dataset where, in both training and testing, we analyze a same class of grasping movements underlying different intentions. We investigate how much the intention discriminants generalize across subjects, discovering that each subject tends to affect the prediction by his/her own bias. Inspired by the domain adaptation problem, we propose to interpret each subject as a domain, leading to a novel subject adversarial paradigm. The proposed approach favorably copes with our new problem, boosting the considered baseline features encoding 2D and 3D information and which do not exploit the subject information.
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Hwang, Sunwoo, Joouk Kim, Hagseoung Kim, Hyungchul Kim, and Youngmin Kim. "Design of Human Adaptive Mechatronics Controller for Upper Limb Motion Intention Prediction." Computers, Materials & Continua 71, no. 1 (2022): 1171–88. http://dx.doi.org/10.32604/cmc.2022.021667.
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Li, Shengchao, Lin Zhang, and Xiumin Diao. "Deep-Learning-Based Human Intention Prediction Using RGB Images and Optical Flow." Journal of Intelligent & Robotic Systems 97, no. 1 (July 12, 2019): 95–107. http://dx.doi.org/10.1007/s10846-019-01049-3.
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Mohd Khairuddin, Ismail, Shahrul Naim Sidek, Anwar P.P. Abdul Majeed, Mohd Azraai Mohd Razman, Asmarani Ahmad Puzi, and Hazlina Md Yusof. "The classification of movement intention through machine learning models: the identification of significant time-domain EMG features." PeerJ Computer Science 7 (February 25, 2021): e379. http://dx.doi.org/10.7717/peerj-cs.379.
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Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have often been concentrated on either classification and regression models of muscle activity. In this study, we leverage the information from the EMG signals, to detect the subject’s intentions in generating motion commands for a robot-assisted upper limb rehabilitation platform. The EMG signals are recorded from ten healthy subjects’ biceps muscle, and the movements of the upper limb evaluated are voluntary elbow flexion and extension along the sagittal plane. The signals are filtered through a fifth-order Butterworth filter. A number of features were extracted from the filtered signals namely waveform length (WL), mean absolute value (MAV), root mean square (RMS), standard deviation (SD), minimum (MIN) and maximum (MAX). Several different classifiers viz. Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) were investigated on its efficacy to accurately classify the pre-intention and intention classes based on the significant features identified (MIN and MAX) via Extremely Randomised Tree feature selection technique. It was observed from the present investigation that the DT classifier yielded an excellent classification with a classification accuracy of 100%, 99% and 99% on training, testing and validation dataset, respectively based on the identified features. The findings of the present investigation are non-trivial towards facilitating the rehabilitation phase of patients based on their actual capability and hence, would eventually yield a more active participation from them.
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Alt, Florian, Daniel Buschek, David Heuss, and Jörg Müller. "Orbuculum - Predicting When Users Intend to Leave Large Public Displays." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 1 (March 19, 2021): 1–16. http://dx.doi.org/10.1145/3448075.
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We present a system, predicting the point in time when users of a public display are about to leave. The ability to react to users' intention to leave is valuable for researchers and practitioners alike: users can be presented additional content with the goal to maximize interaction times; they can be offered a discount coupon for redemption in a nearby store hence enabling new business models; or feedback can be collected from users right after they have finished interaction without interrupting their task. Our research consists of multiple steps: (1) We identified features that hint at users' intention to leave from observations and video logs. (2) We implemented a system capable of detecting such features from Microsoft Kinect's skeleton data and subsequently make a prediction. (3) We trained and deployed a prediction system with a Quiz game which reacts when users are about to leave (N=249), achieving an accuracy of 78%. The majority of users indeed reacted to the presented intervention.
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Feleke, Aberham Genetu, Luzheng Bi, and Weijie Fei. "EMG-Based 3D Hand Motor Intention Prediction for Information Transfer from Human to Robot." Sensors 21, no. 4 (February 12, 2021): 1316. http://dx.doi.org/10.3390/s21041316.
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(1) Background: Three-dimensional (3-D) hand position is one of the kinematic parameters that can be inferred from Electromyography (EMG) signals. The inferred parameter is used as a communication channel in human–robot collaboration applications. Although its application from the perspective of rehabilitation and assistive technologies are widely studied, there are few papers on its application involving healthy subjects such as intelligent manufacturing and skill transfer. In this regard, for tasks associated with complex hand trajectories without the consideration of the degree of freedom (DOF), the prediction of 3-D hand position from EMG signal alone has not been addressed. (2) Objective: The primary aim of this study is to propose a model to predict human motor intention that can be used as information from human to robot. Therefore, the prediction of a 3-D hand position directly from the EMG signal for complex trajectories of hand movement, without the direct consideration of joint movements, is studied. In addition, the effects of slow and fast motions on the accuracy of the prediction model are analyzed. (3) Methods: This study used the EMG signal that is collected from the upper limb of healthy subjects, and the position signal of the hand while the subjects manipulate complex trajectories. We considered and analyzed two types of tasks with complex trajectories, each with quick and slow motions. A recurrent fuzzy neural network (RFNN) model was constructed to predict the 3-D position of the hand from the features of EMG signals alone. We used the Pearson correlation coefficient (CC) and normalized root mean square error (NRMSE) as performance metrics. (4) Results: We found that 3-D hand positions of the complex movement can be predicted with the mean performance of CC = 0.85 and NRMSE = 0.105. The 3-D hand position can be predicted well within a future time of 250 ms, from the EMG signal alone. Even though tasks performed under quick motion had a better prediction performance; the statistical difference in the accuracy of prediction between quick and slow motion was insignificant. Concerning the prediction model, we found that RFNN has a good performance in decoding for the time-varying system. (5) Conclusions: In this paper, irrespective of the speed of the motion, the 3-D hand position is predicted from the EMG signal alone. The proposed approach can be used in human–robot collaboration applications to enhance the natural interaction between a human and a robot.
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Little, Kieran, Bobby K Pappachan, Sibo Yang, Bernardo Noronha, Domenico Campolo, and Dino Accoto. "Elbow Motion Trajectory Prediction Using a Multi-Modal Wearable System: A Comparative Analysis of Machine Learning Techniques." Sensors 21, no. 2 (January 12, 2021): 498. http://dx.doi.org/10.3390/s21020498.
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Motion intention detection is fundamental in the implementation of human-machine interfaces applied to assistive robots. In this paper, multiple machine learning techniques have been explored for creating upper limb motion prediction models, which generally depend on three factors: the signals collected from the user (such as kinematic or physiological), the extracted features and the selected algorithm. We explore the use of different features extracted from various signals when used to train multiple algorithms for the prediction of elbow flexion angle trajectories. The accuracy of the prediction was evaluated based on the mean velocity and peak amplitude of the trajectory, which are sufficient to fully define it. Results show that prediction accuracy when using solely physiological signals is low, however, when kinematic signals are included, it is largely improved. This suggests kinematic signals provide a reliable source of information for predicting elbow trajectories. Different models were trained using 10 algorithms. Regularization algorithms performed well in all conditions, whereas neural networks performed better when the most important features are selected. The extensive analysis provided in this study can be consulted to aid in the development of accurate upper limb motion intention detection models.
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Abou Zeid, Elias, and Tom Chau. "Electrode Fusion for the Prediction of Self-Initiated Fine Movements from Single-Trial Readiness Potentials." International Journal of Neural Systems 25, no. 04 (May 25, 2015): 1550014. http://dx.doi.org/10.1142/s0129065715500148.
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Current human-machine interfaces (HMIs) for users with severe disabilities often have difficulty distinguishing between intentional and inadvertent activations. Pre-movement neuro-cortical activity may aid in this elusive discrimination task but has not been exploited in HMIs. This work investigates the utility of the readiness potential (RP), a slow negative cortical potential preceding voluntary movement, for detecting the intention of self-initiated fine movements prior to their motoric realization. We recorded electroencephalography from the frontal, central, parietal and occipital lobes of 10 participants using a self-initiated switch activation protocol. Eye movement artifacts were removed by regression and the RP was detected on a single-trial basis, in a narrow frequency range (0.1–1 Hz). Common average reference was applied prior to windowed-averaging for feature extraction. Electrodes were selected according to a separability measure based on Fisher projection. Our findings demonstrate that feature fusion from an optimal number of electrodes achieves a statistically significant lower classification error than the best single classifier. Finally, voluntary fine movement intention was detected on a single-trial basis at above-chance levels approximately 396 ms before physical switch activation. These findings encourage the development of rapid-response, intention-aware HMIs for individuals with severe disabilities who struggle with executing voluntary fine motor movements.
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Analia, Riska, Jan Hong, Joshua Mangkey, Susanto, Daniel Pamungkas, Hendawan Soebhakti, and Abdullah Sani. "Use of the Human Walking Gait Cycle for Assistive Torque Generation for the Hip Joint Exoskeleton." Journal of Robotics 2021 (December 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/5561600.
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The development of an assistive robot to assist human beings in walking normally is a difficult task. One of the main challenges lies in understanding the intention to walk, as an initial phase before walking commences. In this work, we classify the human gait cycle based on data from an inertial moment unit sensor and information on the angle of the hip joint and use the results as initial signals to produce a suitable assistive torque for a lower limb exoskeleton. A neural network module is used as a prediction module to identify the intention to walk based on the gait cycle. A decision tree method is implemented in our system to generate the assistive torque, and a prediction of the human gait cycle is used as a reference signal. Real-time experiments are carried out to verify the performance of the proposed method, which can differentiate between various types of walking. The results show that the proposed method is able to predict the intention to walk as an initial phase and is also able to provide an assistive torque based on the information predicted for this phase.
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Zha, Shijia, Tianyi Li, Lidan Cheng, Jihua Gu, Wei Wei, Xichuan Lin, and Shaofei Gu. "Exoskeleton Follow-Up Control Based on Parameter Optimization of Predictive Algorithm." Applied Bionics and Biomechanics 2021 (January 21, 2021): 1–13. http://dx.doi.org/10.1155/2021/8850348.
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The prediction of sensor data can help the exoskeleton control system to get the human motion intention and target position in advance, so as to reduce the human-machine interaction force. In this paper, an improved method for the prediction algorithm of exoskeleton sensor data is proposed. Through an algorithm simulation test and two-link simulation experiment, the algorithm improves the prediction accuracy by 14.23 ± 0.5%, and the sensor data is smooth. Input the predicted signal into the two-link model, and use the calculated torque method to verify the prediction accuracy data and smoothness. The simulation results showed that the algorithm can predict the joint angle of the human body and can be used for the follow-up control of the swinging legs of the exoskeleton.
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Soriano, Marco, Andrea Cavallo, Alessandro D’Ausilio, Cristina Becchio, and Luciano Fadiga. "Movement kinematics drive chain selection toward intention detection." Proceedings of the National Academy of Sciences 115, no. 41 (September 21, 2018): 10452–57. http://dx.doi.org/10.1073/pnas.1809825115.
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The ability to understand intentions based on another’s movements is crucial for human interaction. This ability has been ascribed to the so-called motor chaining mechanism: anytime a motor chain is activated (e.g., grasp-to-drink), the observer attributes to the agent the corresponding intention (i.e., to drink) from the first motor act (i.e., the grasp). However, the mechanisms by which a specific chain is selected in the observer remain poorly understood. In the current study, we investigate the possibility that in the absence of discriminative contextual cues, slight kinematic variations in the observed grasp inform mapping to the most probable chain. Chaining of motor acts predicts that, in a sequential grasping task (e.g., grasp-to-drink), electromyographic (EMG) components that are required for the final act [e.g., the mouth-opening mylohyoid (MH) muscle] show anticipatory activation. To test this prediction, we used MH EMG, transcranial magnetic stimulation (TMS; MH motor-evoked potentials), and predictive models of movement kinematics to measure the level and timing of MH activation during the execution (Experiment 1) and the observation (Experiment 2) of reach-to-grasp actions. We found that MH-related corticobulbar excitability during grasping observation varied as a function of the goal (to drink or to pour) and the kinematics of the observed grasp. These results show that subtle changes in movement kinematics drive the selection of the most probable motor chain, allowing the observer to link an observed act to the agent’s intention.
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Hasan, S. M. Shafiul, Masudur R. Siddiquee, and Ou Bai. "Asynchronous Prediction of Human Gait Intention in a Pseudo Online Paradigm Using Wavelet Transform." IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 7 (July 2020): 1623–35. http://dx.doi.org/10.1109/tnsre.2020.2998778.
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Schneemann, Friederike, and Frederik Diederichs. "Action prediction with the Jordan model of human intention: a contribution to cooperative control." Cognition, Technology & Work 21, no. 4 (November 29, 2018): 711–21. http://dx.doi.org/10.1007/s10111-018-0536-5.
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Mengmeng, Jiang, Jakaria Dasan, and Li Xiaoxiao. "Research on Job Burnout Evaluation and Turnover Tendency Prediction of Knowledge Workers Based on BP Neural Network." Security and Communication Networks 2022 (May 2, 2022): 1–6. http://dx.doi.org/10.1155/2022/6370886.
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The traditional dimission prediction method for knowledge workers does not take into account the impact of job burnout on employees’ dimission tendency, resulting in low accuracy of dimission prediction. In view of the above problems, this paper introduces the employee burnout evaluation and researches the knowledge employee burnout evaluation and turnover tendency prediction method based on BP neural network. After analyzing the influencing factors of job burnout of knowledge workers, the evaluation index system of job burnout is established. The weight of the job burnout evaluation index was determined by fuzzy hierarchy, and BP neural network model was established. Boosting method added the fusion layer of the correlation analysis of job burnout and turnover intention to the neural network model to predict the turnover intention of employees. In the method test, the accuracy rate of employee turnover tendency prediction is higher than 90%, the reliability of employee burnout evaluation is higher, and it is more helpful for human resource management.
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Bi, Luzheng, Aberham >Genetu Feleke, and Cuntai Guan. "A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration." Biomedical Signal Processing and Control 51 (May 2019): 113–27. http://dx.doi.org/10.1016/j.bspc.2019.02.011.
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KONG, Dezhi, Wendong WANG, Dong GUO, and Yikai SHI. "Study on upper limb joint angle prediction method based on sEMG." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 40, no. 4 (August 2022): 764–70. http://dx.doi.org/10.1051/jnwpu/20224040764.
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Aiming at the problems of insufficient human-computer interaction and human-machine coupling in the rehabilitation training process, a prediction model of upper limb joint angle is proposed and verified by experiments. Firstly, a mixture vector that can well represent the motion intention of the upper limbs is obtained based on sEMG; secondly, the signal preprocessing, feature optimization and extraction of temporal eigenvalues are completed; finally, for the problems of unsatisfactory prediction accuracy and slow prediction speed of the current models in the field of motion control, the least square method (LSM) is adopted. The upper limb joint angle prediction is realized by multiplying the support vector machine (LSSVM) first. The experimental results show that the prediction model proposed in this paper can well predict the motion trajectory of the upper limb joints of the human body according to the sEMG and attitude information, effectively reduce the prediction time delay and error, and has certain advantages.
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Mowchan, Michael, D. Jordan Lowe, and Philip M. J. Reckers. "Antecedents to Unethical Corporate Conduct: Characteristics of the Complicit Follower." Behavioral Research in Accounting 27, no. 2 (June 1, 2015): 95–126. http://dx.doi.org/10.2308/bria-51186.
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ABSTRACT Appropriately, researchers are devoting increased attention to the behavioral antecedents to unethical conduct in business. Prior management research, however, has focused primarily on destructive leaders while little research has focused on complicit followers, especially outside of the management literature. Thus, this paper examines three individual characteristics of followers—impulsivity, authoritarianism, and proactivity—in an accounting context. Two quasi-experiments are conducted to determine whether each characteristic influences an individual's intention for unethical complicity (Study 1) and ability to identify ethical dilemmas (Study 2). Results reveal that both high impulsivity and low authoritarianism lead to greater intention for unethical complicity and reduced ability to identify ethical dilemmas. Consistent with our prediction, high impulsivity and low authoritarianism interactively lead to the greatest intention for unethical complicity and lowest ability to identify ethical dilemmas. Additionally, in Study 1, high proactivity and high authoritarianism interactively lead to the greatest intention to resist supervisors' requests for compliant misconduct. Important contributions and implications for both theory and practice are also discussed.
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You, Fang, Xu Yan, Jun Zhang, and Wei Cui. "Design Factors of Shared Situation Awareness Interface in Human–Machine Co-Driving." Information 13, no. 9 (September 16, 2022): 437. http://dx.doi.org/10.3390/info13090437.
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Automated vehicles can perceive their environment and control themselves, but how to effectively transfer the information perceived by the vehicles to human drivers through interfaces, or share the awareness of the situation, is a problem to be solved in human–machine co-driving. The four elements of the shared situation awareness (SSA) interface, namely human–machine state, context, current task status, and plan, were analyzed and proposed through an abstraction hierarchy design method to guide the output of the corresponding interface design elements. The four elements were introduced to visualize the interface elements and design the interface prototype in the scenario of “a vehicle overtaking with a dangerous intention from the left rear”, and the design schemes were experimentally evaluated. The results showed that the design with the four elements of an SSA interface could effectively improve the usability of the human–machine interface, increase the levels of human drivers’ situational awareness and prediction of dangerous intentions, and boost trust in the automatic systems, thereby providing ideas for the design of human–machine collaborative interfaces that enhance shared situational awareness in similar scenarios.
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Pan, Yunxian, Qinyu Zhang, Yifan Zhang, Xianliang Ge, Xiaoqing Gao, Shiyan Yang, and Jie Xu. "Lane-change intention prediction using eye-tracking technology: A systematic review." Applied Ergonomics 103 (September 2022): 103775. http://dx.doi.org/10.1016/j.apergo.2022.103775.
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Newman, Benjamin A., Reuben M. Aronson, Siddhartha S. Srinivasa, Kris Kitani, and Henny Admoni. "HARMONIC: A multimodal dataset of assistive human–robot collaboration." International Journal of Robotics Research 41, no. 1 (December 7, 2021): 3–11. http://dx.doi.org/10.1177/02783649211050677.
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We present the Human And Robot Multimodal Observations of Natural Interactive Collaboration (HARMONIC) dataset. This is a large multimodal dataset of human interactions with a robotic arm in a shared autonomy setting designed to imitate assistive eating. The dataset provides human, robot, and environmental data views of 24 different people engaged in an assistive eating task with a 6-degree-of-freedom (6-DOF) robot arm. From each participant, we recorded video of both eyes, egocentric video from a head-mounted camera, joystick commands, electromyography from the forearm used to operate the joystick, third-person stereo video, and the joint positions of the 6-DOF robot arm. Also included are several features that come as a direct result of these recordings, such as eye gaze projected onto the egocentric video, body pose, hand pose, and facial keypoints. These data streams were collected specifically because they have been shown to be closely related to human mental states and intention. This dataset could be of interest to researchers studying intention prediction, human mental state modeling, and shared autonomy. Data streams are provided in a variety of formats such as video and human-readable CSV and YAML files.
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Wang, Wen-Bao, Chich-Jen Shieh, Hamza Mohammed Ridha Al-Khafaji, Andrei Sevbitov, Aras Masood Ismael, Paitoon Chetthamrongchai, Wanich Suksatan, and Parvaneh Bahrami. "Predicting Antecedents of Employee Smart Work Adoption Using SEM-Multilayer Perceptron Approach." Human Behavior and Emerging Technologies 2023 (January 4, 2023): 1–9. http://dx.doi.org/10.1155/2023/7623801.
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The COVID-19 pandemic forced many organizations to move to telework and smart work (SW), and this practice is expected to continue even later in the postpandemic period. Hence, it is very important for managers and organizations to identify the motivating and deterrent factors in adopting smart work and plan to manage them. Therefore, the present study using an innovative methodology tried to identify and prioritize the factors influencing employee SW adoption. In the first stage, the conceptual model of the research was designed, inspired by the literature. In the next step, using structural equation modeling (SEM), antecedents whose effects on employee SW adoption were confirmed were identified. Finally, the output of the SEM model was considered as the input of the multilayer perceptron (MLP) model, which is an artificial neural network model, to determine the importance of each antecedent in the prediction of employee behavior. The present study provides quantitative empirical evidence that perceived value, institutional and technological support, perceived limited communication, and perceived cost are antecedents of employee SW adoption that are, respectively, important in predicting the behavioral intentions of employees in acceptance of SW. The findings of this study contribute to both the SW and the behavioral intention theory literature.
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Delfino, Emanuela, Aldo Pastore, Elena Zucchini, Maria Francisca Porto Cruz, Tamara Ius, Maria Vomero, Alessandro D’Ausilio, et al. "Prediction of Speech Onset by Micro-Electrocorticography of the Human Brain." International Journal of Neural Systems 31, no. 07 (June 14, 2021): 2150025. http://dx.doi.org/10.1142/s0129065721500258.
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Recent technological advances show the feasibility of offline decoding speech from neuronal signals, paving the way to the development of chronically implanted speech brain computer interfaces (sBCI). Two key steps that still need to be addressed for the online deployment of sBCI are, on the one hand, the definition of relevant design parameters of the recording arrays, on the other hand, the identification of robust physiological markers of the patient’s intention to speak, which can be used to online trigger the decoding process. To address these issues, we acutely recorded speech-related signals from the frontal cortex of two human patients undergoing awake neurosurgery for brain tumors using three different micro-electrocorticographic ([Formula: see text]ECoG) devices. First, we observed that, at the smallest investigated pitch (600[Formula: see text][Formula: see text]m), neighboring channels are highly correlated, suggesting that more closely spaced electrodes would provide some redundant information. Second, we trained a classifier to recognize speech-related motor preparation from high-gamma oscillations (70–150[Formula: see text]Hz), demonstrating that these neuronal signals can be used to reliably predict speech onset. Notably, our model generalized both across subjects and recording devices showing the robustness of its performance. These findings provide crucial information for the design of future online sBCI.
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Xu, Wenxiang, Junhua Wang, Ting Fu, Hongren Gong, and Anae Sobhani. "Aggressive driving behavior prediction considering driver’s intention based on multivariate-temporal feature data." Accident Analysis & Prevention 164 (January 2022): 106477. http://dx.doi.org/10.1016/j.aap.2021.106477.
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Zhang, Hailun, and Rui Fu. "A Hybrid Approach for Turning Intention Prediction Based on Time Series Forecasting and Deep Learning." Sensors 20, no. 17 (August 28, 2020): 4887. http://dx.doi.org/10.3390/s20174887.
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At an intersection with complex traffic flow, the early detection of the intention of drivers in surrounding vehicles can enable advanced driver assistance systems (ADAS) to warn the driver in advance or prompt its subsystems to assess the risk and intervene early. Although different drivers show various driving characteristics, the kinematic parameters of human-driven vehicles can be used as a predictor for predicting the driver’s intention within a short time. In this paper, we propose a new hybrid approach for vehicle behavior recognition at intersections based on time series prediction and deep learning networks. First, the lateral position, longitudinal position, speed, and acceleration of the vehicle are predicted using the online autoregressive integrated moving average (ARIMA) algorithm. Next, a variant of the long short-term memory network, called the bidirectional long short-term memory (Bi-LSTM) network, is used to detect the vehicle’s turning behavior using the predicted parameters, as well as the derived parameters, i.e., the lateral velocity, lateral acceleration, and heading angle. The validity of the proposed method is verified at real intersections using the public driving data of the next generation simulation (NGSIM) project. The results of the turning behavior detection show that the proposed hybrid approach exhibits significant improvement over a conventional algorithm; the average recognition rates are 94.2% and 93.5% at 2 s and 1 s, respectively, before initiating the turning maneuver.
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Boudrias, Valérie, Sarah-Geneviève Trépanier, Annie Foucreault, Clayton Peterson, and Claude Fernet. "Investigating the role of psychological need satisfaction as a moderator in the relationship between job demands and turnover intention among nurses." Employee Relations: The International Journal 42, no. 1 (January 6, 2020): 213–31. http://dx.doi.org/10.1108/er-10-2018-0277.
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Purpose Job demands can contribute to nurses’ turnover intention and this can have an impact on health services among the general population. It appears important to identify the work environment factors associated with turnover intention, as well as the psychological resources liable to act on this relationship. Drawing on self-determination theory (SDT), the purpose of this study (n=1179) is to investigate the relationship between two job demands (role ambiguity and role conflict) and turnover intention, as well as the moderating role of basic need satisfaction (autonomy, competence and relatedness) within these relationships. Design/methodology/approach This cross-sectional study was conducted among nurses (Québec, Canada). Nurses completed an online questionnaire. To test the proposed moderating effect of satisfaction of the three psychological needs (i.e. autonomy, competence and relatedness) in the relationship between job demands (i.e. role ambiguity and role conflict) and turnover intention, path analysis was conducted using Mplus v.8 (Muthén and Muthen, 2017). Two models, one for each demand, were tested. Findings As expected, role ambiguity and role conflict are positively related to turnover intention. Results reveal a significant interaction between role ambiguity and satisfaction of the need for autonomy in the prediction of turnover intention. The satisfaction of the need for competence and the satisfaction of the need for relatedness did not moderate the relationship between role ambiguity and turnover intention. Satisfaction of the need for autonomy moderated the relationship between role conflict and turnover intention. Moreover, results revealed a significant interaction between role conflict and satisfaction of the need for competence in the prediction of turnover intention. Satisfaction of the need for relatedness did not moderate the relationship between role conflict and turnover intention. Research limitations/implications The results align with the theoretical propositions of several leading theories in occupational health which state that workers’ psychological functioning derives not only from the job characteristics of their work environment, but also from the psychological resources at their disposal. The study contributes to SDT. First, to date, this is the first study to investigate basic psychological need satisfaction as a moderator in the relationship between contextual factors and workers’ functioning. Second, the findings revealed the importance of assessing psychological needs separately, as each contributes in a specific way to workers’ work-related attitudes and adaptation to their professional environment. Practical implications Perceptions of autonomy and competence act as key psychological resources for nurses. Managerial support for autonomy (e.g. providing nurses with meaningful information regarding their work) and competence (e.g. providing nurses with frequent positive feedback regarding their work efforts) constitutes a series of key management practices that can foster perceptions of autonomy and competence. The findings show that two role stressors predict nurses’ turnover intention. As such, health care establishments are encouraged to focus on interventions that reduce uncertainties and conflicting situations from nurses (provide clear job descriptions and effective communication). Social implications By promoting a sense of effectiveness and feelings of self-endorsement at work, health care establishments can reduce nurses’ turnover intention and help prevent staffing shortages among this important work group. Originality/value Although past research shows that workers’ motivational profile can modulate the relationship between characteristics within the work environment and workers’ functioning, studying the quality of work motivation is not sufficient to completely understand the factors that can influence workers’ reactions to job demands. Need satisfaction is crucial to the development and maintenance of high quality motivation. Evaluating need satisfaction as a moderator in the stressor–strain relationship could offer a better understanding of the psychological experiences that can promote workers’ adaptation to their work environment. To date, no study has investigated the buffering role of psychological needs in the stressor–strain relationship.
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Wang, Fei, Jian Lu, Zhibo Fan, Chuanjian Ren, and Xin Geng. "Continuous motion estimation of lower limbs based on deep belief networks and random forest." Review of Scientific Instruments 93, no. 4 (April 1, 2022): 044106. http://dx.doi.org/10.1063/5.0057478.
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Due to the lag problem of traditional sensor acquisition data, the following movement of exoskeleton robots can affect the comfort of the wearer and even the normal movement pattern of the wearer. In order to solve the problem of lag in exoskeleton motion control, this paper designs a continuous motion estimation method for lower limbs based on the human surface electromyographic (sEMG) signal and achieves the recognition of the motion intention of the wearer through a combination of the deep belief network (DBN) and random forest (RF) algorithm. First, the motion characteristics of human lower limbs are analyzed, and the hip–knee angle and sEMG signal related to lower limb motion are collected and extracted; then, the DBN is used in the dimensionality reduction of the sEMG signal feature values; finally, the motion intention of the wearer is predicted using the RF model optimized by the genetic algorithm. The experimental results show that the root mean square error of knee and hip prediction results of the combined algorithm proposed in this article improved by 0.2573° and 0.3375°, respectively, compared to the algorithm with dimensionality reduction by principal component analysis, and the single prediction time is 0.28 ms less than that before dimensionality reduction, provided that other conditions are exactly the same.
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Kong, Dezhi, Wendong Wang, Dong Guo, and Yikai Shi. "RBF Sliding Mode Control Method for an Upper Limb Rehabilitation Exoskeleton Based on Intent Recognition." Applied Sciences 12, no. 10 (May 15, 2022): 4993. http://dx.doi.org/10.3390/app12104993.
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Aiming at the lack of active willingness of patients to participate in the current upper limb exoskeleton rehabilitation training control methods, this study proposed a radial basis function (RBF) sliding mode impedance control method based on surface electromyography (sEMG) to identify the movement intention of upper limb rehabilitation. The proposed control method realizes the process of active and passive rehabilitation training according to the wearer’s movement intention. This study first established a joint angle prediction model based on sEMG for the problem of poor human–machine coupling and used the least-squares support vector machine method (LSSVM) to complete the upper limb joint angle prediction. In addition, in view of the problem of poor compliance in the rehabilitation training process, an adaptive sliding mode controller based on the RBF network approximation system model was proposed. In the process of active training, an impedance model was added based on the position loop control, which could dynamically adjust the motion trajectory according to the interaction force. The experiment results showed that the impedance control method based on the RBF could effectively reduce the interaction force between the human and machine to improve the compliance of the exoskeleton manipulator and achieve the purpose of stabilizing the impedance characteristics of the system.
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Griol, David, and Zoraida Callejas. "A Neural Network Approach to Intention Modeling for User-Adapted Conversational Agents." Computational Intelligence and Neuroscience 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/8402127.
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Spoken dialogue systems have been proposed to enable a more natural and intuitive interaction with the environment and human-computer interfaces. In this contribution, we present a framework based on neural networks that allows modeling of the user’s intention during the dialogue and uses this prediction to dynamically adapt the dialogue model of the system taking into consideration the user’s needs and preferences. We have evaluated our proposal to develop a user-adapted spoken dialogue system that facilitates tourist information and services and provide a detailed discussion of the positive influence of our proposal in the success of the interaction, the information and services provided, and the quality perceived by the users.
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Zhang, Yifan, Jinghuai Zhang, Jindi Zhang, Jianping Wang, Kejie Lu, and Jeff Hong. "A Novel Learning Framework for Sampling-Based Motion Planning in Autonomous Driving." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 1202–9. http://dx.doi.org/10.1609/aaai.v34i01.5473.
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Sampling-based motion planning (SBMP) is a major trajectory planning approach in autonomous driving given its high efficiency in practice. As the core of SBMP schemes, sampling strategy holds the key to whether a smooth and collision-free trajectory can be found in real-time. Although some bias sampling strategies have been explored in the literature to accelerate SBMP, the trajectory generated under existing bias sampling strategies may lead to sharp lane changing. To address this issue, we propose a new learning framework for SBMP. Specifically, we develop a novel automatic labeling scheme and a 2-Stage prediction model to improve the accuracy in predicting the intention of surrounding vehicles. We then develop an imitation learning scheme to generate sample points based on the experience of human drivers. Using the prediction results, we design a new bias sampling strategy to accelerate the SBMP algorithm by strategically selecting necessary sample points that can generate a smooth and collision-free trajectory and avoid sharp lane changing. Data-driven experiments show that the proposed sampling strategy outperforms existing sampling strategies, in terms of the computing time, traveling time, and smoothness of the trajectory. The results also show that our scheme is even better than human drivers.
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Park, Jinwan, Jungsik Jeong, and Youngsoo Park. "Ship Trajectory Prediction Based on Bi-LSTM Using Spectral-Clustered AIS Data." Journal of Marine Science and Engineering 9, no. 9 (September 21, 2021): 1037. http://dx.doi.org/10.3390/jmse9091037.
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According to the statistics of maritime accidents, most collision accidents have been caused by human factors. In an encounter situation, the prediction of ship’s trajectory is a good way to notice the intention of the other ship. This paper proposes a methodology for predicting the ship’s trajectory that can be used for an intelligent collision avoidance algorithm at sea. To improve the prediction performance, the density-based spatial clustering of applications with noise (DBSCAN) has been used to recognize the pattern of the ship trajectory. Since the DBSCAN is a clustering algorithm based on the density of data points, it has limitations in clustering the trajectories with nonlinear curves. Thus, we applied the spectral clustering method that can reflect a similarity between individual trajectories. The similarity measured by the longest common subsequence (LCSS) distance. Based on the clustering results, the prediction model of ship trajectory was developed using the bidirectional long short-term memory (Bi-LSTM). Moreover, the performance of the proposed model was compared with that of the long short-term memory (LSTM) model and the gated recurrent unit (GRU) model. The input data was obtained by preprocessing techniques such as filtering, grouping, and interpolation of the automatic identification system (AIS) data. As a result of the experiment, the prediction accuracy of Bi-LSTM was found to be the highest compared to that of LSTM and GRU.
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Gardner, Marcus, C. Sebastian Mancero Castillo, Samuel Wilson, Dario Farina, Etienne Burdet, Boo Cheong Khoo, S. Farokh Atashzar, and Ravi Vaidyanathan. "A Multimodal Intention Detection Sensor Suite for Shared Autonomy of Upper-Limb Robotic Prostheses." Sensors 20, no. 21 (October 27, 2020): 6097. http://dx.doi.org/10.3390/s20216097.
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Neurorobotic augmentation (e.g., robotic assist) is now in regular use to support individuals suffering from impaired motor functions. A major unresolved challenge, however, is the excessive cognitive load necessary for the human–machine interface (HMI). Grasp control remains one of the most challenging HMI tasks, demanding simultaneous, agile, and precise control of multiple degrees-of-freedom (DoFs) while following a specific timing pattern in the joint and human–robot task spaces. Most commercially available systems use either an indirect mode-switching configuration or a limited sequential control strategy, limiting activation to one DoF at a time. To address this challenge, we introduce a shared autonomy framework centred around a low-cost multi-modal sensor suite fusing: (a) mechanomyography (MMG) to estimate the intended muscle activation, (b) camera-based visual information for integrated autonomous object recognition, and (c) inertial measurement to enhance intention prediction based on the grasping trajectory. The complete system predicts user intent for grasp based on measured dynamical features during natural motions. A total of 84 motion features were extracted from the sensor suite, and tests were conducted on 10 able-bodied and 1 amputee participants for grasping common household objects with a robotic hand. Real-time grasp classification accuracy using visual and motion features obtained 100%, 82.5%, and 88.9% across all participants for detecting and executing grasping actions for a bottle, lid, and box, respectively. The proposed multimodal sensor suite is a novel approach for predicting different grasp strategies and automating task performance using a commercial upper-limb prosthetic device. The system also shows potential to improve the usability of modern neurorobotic systems due to the intuitive control design.
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Xi, Xugang, Chen Yang, Seyed M. Miran, Yun-Bo Zhao, Shuliang Lin, and Zhizeng Luo. "sEMG-MMG State-Space Model for the Continuous Estimation of Multijoint Angle." Complexity 2020 (February 11, 2020): 1–12. http://dx.doi.org/10.1155/2020/4503271.
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Continuous joint angle estimation plays an important role in motion intention recognition and rehabilitation training. In this study, a surface electromyography- (sEMG-) mechanomyography (MMG) state-space model is proposed to estimate continuous multijoint movements from sEMG and MMG signals accurately. The model combines forward dynamics with a Hill-based muscle model that estimates joint torque only in a nonfeedback form, making the extended model capable of predicting the multijoint motion directly. The sEMG and MMG features, including the Wilson amplitude and permutation entropy, are then extracted to construct a measurement equation to reduce system error and external disturbances. Using the proposed model, a closed-loop prediction-correction approach, unscented particle filtering, is used to estimate the joint angle from sEMG and MMG signals. Comprehensive experiments are conducted on the human elbow and shoulder joint, and remarkable improvements are demonstrated compared with conventional methods.
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Nidhi Sengar and Dr.Amita Goe, Tarun Rahuja. "Heart Disease Prediction Using Machine Learning." International Journal for Modern Trends in Science and Technology 6, no. 12 (December 13, 2020): 290–93. http://dx.doi.org/10.46501/ijmtst061254.
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This paper revolves around a classification use case of machine learning in which the intention is to predict the possibility of a heart disease in an individual given certain parameters. Machine Learning is extensively being used across the world. The healthcare industry has also commenced leveraging these data driven techniques. Machine Learning can play a vital role in predicting the likelihood of locomotor disorders, Heart ailments and more such diseases because machine learning is well known for its use cases in classifying, categorizing and predicting. Such information, if predicted well, can provide key foresight to doctors who can hence mould their diagnosis and course of treatment per patient basis. The main advantage of using machine learning in healthcare is its ability to parse and process huge datasets which are beyond the scope of human abilities, and then accurately convert the derived analysis of that data into clinical insights that can aid medical practitioners round the globe in planning stratergies for providing care to patients, ultimately leading to more promising results, reduced costs of care and last but not the least , increased patient satiation and response/recovery. To simplify and solve this problem, solutions were provided using multiple supervised learning algorithms like logistic regression, Naïve Bayes, random forests, decision trees, support vector machines and K-nearest neighbours. The best accuracy was seen using random forests.
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Zhu, Lei, Shuguang Li, Yaohua Li, Min Wang, Yanyu Li, and Jin Yao. "Study on driver’s braking intention identification based on functional near-infrared spectroscopy." Journal of Intelligent and Connected Vehicles 1, no. 3 (October 1, 2018): 107–13. http://dx.doi.org/10.1108/jicv-09-2018-0007.
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PurposeCooperative driving refers to a notion that intelligent system sharing controlling with human driver and completing driving task together. One of the key technologies is that the intelligent system can identify the driver’s driving intention in real time to implement consistent driving decisions. The purpose of this study is to establish a driver intention prediction model.Design/methodology/approachThe authors used the NIRx device to measure the cerebral cortex activities for identifying the driver’s braking intention. The experiment was carried out in a virtual reality environment. During the experiment, the driving simulator recorded the driving data and the functional near-infrared spectroscopy (fNIRS) device recorded the changes in hemoglobin concentration in the cerebral cortex. After the experiment, the driver’s braking intention identification model was established through the principal component analysis and back propagation neural network.FindingsThe research results showed that the accuracy of the model established in this paper was 80.39 per cent. And, the model could identify the driver’s braking intent prior to his braking operation.Research limitations/implicationsThe limitation of this study was that the experimental environment was ideal and did not consider the surrounding traffic. At the same time, other actions of the driver were not taken into account when establishing the braking intention recognition model. Besides, the verification results obtained in this paper could only reflect the results of a few drivers’ identification of braking intention.Practical implicationsThis study can be used as a reference for future research on driving intention through fNIRS, and it also has a positive effect on the research of brain-controlled driving. At the same time, it has developed new frontiers for intention recognition of cooperative driving.Social implicationsThis study explores new directions for future brain-controlled driving and wheelchairs.Originality/valueThe driver’s driving intention was predicted through the fNIRS device for the first time.