Human Intention Research Articles

Why God Cannot Do What Sterba Wants

Sterba argues that if God existed, God would allow lower-level evils and suffering but should and would prevent all significant and horrendous evils. Since such serious evils do exist, God does not exist. In reply, I argue that in creating a Sterba world, God would be violating one of God’s central purposes for the world, viz., that human beings be rationally free to deny God’s existence and presence. Given the total absence of horrendous evils in Sterba worlds, despite human intentions to inflict them, it would be obvious that God exists and is at work. There might still be atheists, but atheism would be irrational.

An electromyography signals-based human-robot collaboration system for human motion intention recognition and realization

• A human-robot collaboration system based on Electromyography (EMG) signals is proposed to learn the position adjustment skills of human body facing the change of external force information, so as to achieve human-robot cooperation. • To improve the recognition accuracy of human motion intention, the coupling effect of human output force and joint angle is considered. • To improve the accuracy and comfort of the collaboration, from the perspective of ergonomics, two evaluation methods of control signal execution efficiency and motion stability are proposed. • A model-free intelligent control mothed is constructed based on DDPG reinforcement learning algorithm, which effectively avoids the selection of control parameters. With the development of manufacturing industry to the directions of personalization and flexibility, the advantages of human-robot collaboration attract more and more attention. In order to enable the robot fully understand human motion intention and play the supervision and guidance role of the human tutor, a human-robot collaboration system based on Electromyography (EMG) signals is proposed. In the investigation, first, aiming at the problems that the coupling effect of output force and joint angle is not considered in the previous research, and the extraction accuracy of force information is poor, the Fast Orthogonal Search (FOS) algorithm is modified to improve the recognition accuracy of motion intention by using the correlation coefficient between the extracted feature signal and the arm output force as the optimization parameter. Moreover, aiming at the problems that the traditional control models cannot balance accurate tracking and comfortable collaboration, and the robot motion stability is poor, two evaluation methods of control signal execution efficiency and motion stability are proposed from the perspective of ergonomics. Afterwards, the human-robot end-to-end collaborative control model based on Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm is trained online to improve the accuracy and comfort of the collaboration. As compared with the traditional PD control algorithms based on admittance model, the proposed control method is model-free and intelligent. It effectively avoids the challenge of PD control parameter selection, and behaves good coordination between accurate tracking and comfortable collaboration. Human-robot collaborative sawing experiment shows that the proposed collaboration system can save about 86.2% physical power for the human tutor.

Trajectory Online Adaption Based on Human Motion Prediction for Teleoperation

In this work, a human motion intention prediction method based on an autoregressive (AR) model for teleoperation is developed. Based on this method, the robot’s motion trajectory can be updated in real time through updating the parameters of the AR model. In the teleoperated robot’s control loop, a virtual force model is defined to describe the interaction profile and to correct the robot’s motion trajectory in real time. The proposed human motion prediction algorithm acts as a feedforward model to update the robot’s motion and to revise this motion in the process of human–robot interaction (HRI). The convergence of this method is analyzed theoretically. Comparative studies demonstrate the enhanced performance of the proposed approach. Note to Practitioners—In general, the robot trajectory is predetermined and it does not consider the influence of the interaction profiles in terms of position and interaction force between the human and the robot. In addition, it is hard to quantify the influence of interaction profile for the robot trajectory. For teleoperation, an AR-based model is proposed to predict the trajectory of the human and then to update the trajectory of the robot. The developed method includes the following aspects: 1) the robot trajectory can be regulated based on the interaction profiles; 2) the feedforward model can estimate the trajectory of the human to achieve the purpose of human intention recognition in advance for the robot; and 3) the proposed method can be potentially utilized for telerehabilitation, microsurgery, and so on.

WITH LIBERTY AND JUSTICE FOR ALL: SYSTEMS-INVOLVED YOUTH, PAST, PRESENT, AND FUTURE

To date, the United States has not ratified the United Nations Convention on the Rights of the Child, known as the most widely ratified human rights treaty in history. This sentiment is reflected in the historical treatment of youth generally from past to present and in particular for systems-involved youth. Despite the best intentions of the individual humans involved, the systems often fall far short of providing true treatment, education, or rehabilitation for youth, and at times fail even to provide a safe and stable environment or ensure that basic needs are met.

Human-object integrated assembly intention recognition for context-aware human-robot collaborative assembly

Human-robot collaborative (HRC) assembly combines the advantages of robot's operation consistency with human's cognitive ability and adaptivity, which provides an efficient and flexible way for complex assembly tasks. In the process of HRC assembly, the robot needs to understand the operator's intention accurately to assist the collaborative assembly tasks. At present, operator intention recognition considering context information such as assembly objects in a complex environment remains challenging. In this paper, we propose a human-object integrated approach for context-aware assembly intention recognition in the HRC, which integrates the recognition of assembly actions and assembly parts to improve the accuracy of the operator's intention recognition. Specifically, considering the real-time requirements of HRC assembly, spatial-temporal graph convolutional networks (ST-GCN) model based on skeleton features is utilized to recognize the assembly action to reduce unnecessary redundant information. Considering the disorder and occlusion of assembly parts, an improved YOLOX model is proposed to improve the focusing capability of network structure on the assembly parts that are difficult to recognize. Afterwards, taking decelerator assembly tasks as an example, a rule-based reasoning method that contains the recognition information of assembly actions and assembly parts is designed to recognize the current assembly intention. Finally, the feasibility and effectiveness of the proposed approach for recognizing human intentions are verified. The integration of assembly action recognition and assembly part recognition can facilitate the accurate operator's intention recognition in the complex and flexible HRC assembly environment.

An Intention Prediction Based Shared Control System for Point-to-Point Navigation of a Robotic Wheelchair

Shared control approaches for robotic wheelchairs aim to provide navigation assistance to humans by utilizing robot’s intelligence in environment perception and motion planning. They can be broadly classified into two categories based on human intention prediction. Without human intention prediction, control authority lies with humans and assistance is provided only to avoid collisions. This can cause difficulty in cases where fine motor control is required, such as when entering narrow doorways, especially for users with severe upper limb disability. Intention prediction based approaches are able to better assist with such tasks but do not give enough control authority to the user as possible user intentions are pre-defined. In this work, we present an intention prediction based shared control system for point-to-point navigation of wheelchair which gives control authority to the user and also assists in fine motor control tasks. We compute various possible user intentions online using generalized Voronoi diagram, link them across time steps using their <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">homotopy class</i> and thus are able to calculate their probability given user input history. A shared local path planner then steers user towards the most likely path. This allows the user to follow any path. Our simulation experiments with 18 healthy subjects and both simulation and real wheelchair experiments, with 2 Cerebral Palsy (CP) subjects, show that our system can improve navigation outcome for people with disability and in general leads to around 10% faster completion of the task for even healthy people as compared to a local obstacle avoidance system while allowing users to follow their desired path with similar accuracy.

Subject-Independent Classification of P300 Event-Related Potentials Using a Small Number of Training Subjects

The intersubject variability present in electroencephalographic (EEG) signals can affect the performance of the brain–computer interface (BCI) systems. Despite the significant progress in the field, the variability in neural data remains one of the most critical challenges in constructing accurate predictive models of human intention. As a result, the majority of the previous studies have focused either on devising subject-specific signal processing and machine learning algorithms, used some data from a target user to update and calibrate a pretrained classifier, or have used data collected from a relatively large number of training subjects to construct generic classifiers for new subjects. In this work, we investigate the feasibility of using a relatively small number of training subjects to achieve subject-independent classification of event-related potentials (ERPs) in P300-based BCIs. To this end, we employ convolutional neural networks (CNNs) and propose a leave-one-subject-out cross-validation (LOSO-CV) for model selection; that is to say, for tuning CNN hyperparameters including number of layers, filters, kernel size, and epoch. The utility of the proposed model selection is warranted because LOSO-CV simulates the effect of subject-independent classification within the training data. The entire process of training (including model selection) is validated by applying another LOSO-CV external to the training process. Our empirical results obtained on four publicly available datasets confirm the capability of LOSO-CV model selection with CNN to capture intrinsic ERP features from a small group of subjects to classify observations collected from unseen subjects.

SGL: Symbolic Goal Learning in a Hybrid, Modular Framework for Human Instruction Following

This paper investigates human instruction following for robotic manipulation via a hybrid, modular system with symbolic and connectionist elements. Symbolic methods build modular systems with semantic parsing and task planning modules for producing sequences of actions from natural language requests. Modern connectionist methods employ deep neural networks that learn visual and linguistic features for mapping inputs to a sequence of low-level actions, in an end-to-end fashion. The hybrid, modular system blends these two approaches to create a modular framework: it formulates instruction following as symbolic goal learning via deep neural networks followed by task planning via symbolic planners. Connectionist and symbolic modules are bridged with Planning Domain Definition Language. The vision-and-language learning network predicts its goal representation, which is sent to a planner for producing a task-completing action sequence. For improving the flexibility of natural language, we further incorporate implicit human intents with explicit human instructions. To learn generic features for vision and language, we propose to separately pretrain vision and language encoders on scene graph parsing and semantic textual similarity tasks. Benchmarking evaluates the impacts of different components of, or options for, the vision-and-language learning model and shows the effectiveness of pretraining strategies. Manipulation experiments conducted in the simulator AI2THOR show the robustness of the framework to novel scenarios.

Noise-tolerant zeroing neurodynamic algorithm for upper limb motion intention-based human–robot interaction control in non-ideal conditions

Human–robot interaction control, a crucial technology in industrial manufacturing and rehabilitation robot, has been widely explored in recent years. However, there are still some problems in the existing strategy need to be solved for the rehabilitation robot: First, the human–robot confrontation (HRC) in motion control affects the information interaction. Second, in non-ideal conditions, the safety and accuracy of the control system will be disturbed by external unknown factors. Third, the existing continuous-time controllers are hard to be applied to hardware platforms. In this paper, an upper limb-exoskeleton coupling dynamics model based on human active motion intention is established to overcome HRC, and promote active and natural human–robot interaction control. Specifically, the Elman neural network (ENN) model is devised and utilized to estimate human active motion intention using the surface electromyography (sEMG) signals. On the basis of human active motion intention, the discrete-time controller predicated on noise-tolerant zeroing neurodynamic model (termed as NTZND controller) is proposed for constructing the discrete-time human–robot interaction control system (DTHRICS) under non-ideal conditions. In addition, the zeroing neurodynamic (ZND) controller, the gradient neural network (GNN) and proportional–derivative (PD) controllers are designed for comparisons. Theoretical analyses demonstrate that the 0-stability, consistency and convergence of the designed DTHRICS. The performance indicators root mean square errors (RMSEs) and coefficient of determination (R2) of the DTHRICS with different controllers are analyzed and compared under ideal and non-ideal conditions. Ultimately, numerical results illustrate that the NTZND-based DTHRICS (with the lowest performance RMSEs →0 and the highest performance R2→1, p<0.05) is significantly superior to the DTHRICS with the ZND, the GNN and the PD controllers in terms of control accuracy, noise-tolerant property and real time performance, while utilized for upper limb multi-joint motion control under non-ideal conditions.

Simulasi Kebakaran Hutan dan Lahan di Provinsi Riau Menggunakan Cellular Automata

One of the causes of the large number of forest and land fires in Riau Province is human intent to divert forest functions, so that hotspots appear in areas that have the potential for forest and land fires. The purpose of this study was to simulate forest and land fires that occurred in Riau Province using Cellular Automata by utilizing satellite imagery. Land in Riau Province is divided into 5 states, namely: dry land, wet land, land that is being burned, land that has been burned, and land of water. The method used for simulating forest fires is the Cellular Automata to simulate the movement of fire during a fire. This method is able to explain the pattern of fire spread of the main cell fire from neighboring cells. At the end of the study, the simulation results and the movement of fire from where the hotspots appear on satellite images of Riau Province will be shown according to the state of the land cover. The results of this study are a description of the real situation of fires that occurred in Riau Province based on the emergence of hotspots and their movement patterns.

A multi‐priority hierarchical optimization method for double‐layer model predictive control

AbstractConsidering the demand for the sequential regulation of manipulated variables in actual industrial process control, the conventional solution of double‐layer model predictive control faces the problem that the weight coefficients are difficult to tune. This paper proposes an improved hierarchical optimization method for manipulated variables in the steady‐state optimization layer of double‐layer model predictive control. The proposed method can adjust the manipulated variables sequentially without an accurate weight coefficient to avoid difficulty in tuning the weight coefficients. The relation between the optimal solution and the feasible region of the steady‐state optimization layer is analysed to describe the reoptimization of the key manipulated variables. The impact of the economic cost coefficient on the optimal solution with the sensitivity analysis method is studied, and the complexity of using the weight coefficient to solve the priority optimization problem of the manipulated variables is assessed. The steady‐state optimization solution procedure is improved based on the theory of the multiobjective complete hierarchical method. The hierarchical and sequential optimization of the manipulated variables results in expanding the space and freedom of the key manipulated variables, increasing efficiency, reducing consumption, and improving economic performance. The improved hierarchical optimization method is direct and simple in achieving optimization sequentially and satisfies the need for adjusting the manipulated variables according to human intentions.

Electroencephalogram and surface electromyogram fusion-based precise detection of lower limb voluntary movement using convolution neural network-long short-term memory model.

The electroencephalogram (EEG) and surface electromyogram (sEMG) fusion has been widely used in the detection of human movement intention for human–robot interaction, but the internal relationship of EEG and sEMG signals is not clear, so their fusion still has some shortcomings. A precise fusion method of EEG and sEMG using the CNN-LSTM model was investigated to detect lower limb voluntary movement in this study. At first, the EEG and sEMG signal processing of each stage was analyzed so that the response time difference between EEG and sEMG can be estimated to detect lower limb voluntary movement, and it can be calculated by the symbolic transfer entropy. Second, the data fusion and feature of EEG and sEMG were both used for obtaining a data matrix of the model, and a hybrid CNN-LSTM model was established for the EEG and sEMG-based decoding model of lower limb voluntary movement so that the estimated value of time difference was about 24 ∼ 26 ms, and the calculated value was between 25 and 45 ms. Finally, the offline experimental results showed that the accuracy of data fusion was significantly higher than feature fusion-based accuracy in 5-fold cross-validation, and the average accuracy of EEG and sEMG data fusion was more than 95%; the improved average accuracy for eliminating the response time difference between EEG and sEMG was about 0.7 ± 0.26% in data fusion. In the meantime, the online average accuracy of data fusion-based CNN-LSTM was more than 87% in all subjects. These results demonstrated that the time difference had an influence on the EEG and sEMG fusion to detect lower limb voluntary movement, and the proposed CNN-LSTM model can achieve high performance. This work provides a stable and reliable basis for human–robot interaction of the lower limb exoskeleton.

Rumination of Ecological Ethics in Green Hills of Africa: Hemingway’s Instrumentalized Ego and the Ecologist Val Plumwood’s Ecological Ego

This paper tends to analyze human instrumental intentionality in Earnest Hemingway’s Green Hills of Africa in accordance with Australian ecologist, Val Plumwood’s argument over “instrumentalized ego.” By probing into Hemingway’s African hunting memoir defined as ecological literary demonstration, the following content will be positioned with three orientations: first, criticizing human instrumentalized intention relating to egoism; second, examining how human beings are bound with profit intention with fictitious anthropocentric attitude toward other creatures on earth; third, exploring possible solutions to cope with human-centered crisis for maintaining amicable correlation between humans and nonhumans in ecological system.

Dynamic Gesture Recognition of A-Mode Ultrasonic Based on the DTW Algorithm

At present, the research on gesture recognition based on ultrasonic signals is mostly about static gestures. Compared with static gestures, dynamic gestures reflect human intent more intuitively. However, the speed difference of dynamic gestures will cause a mismatch in time series. To tackle this problem, we proposed a dynamic gesture recognition scheme based on the dynamic time warping (DTW) algorithm. First, the ultrasonic signals of the forearm muscles were acquired by four A-mode ultrasonic transducers. Second, the original signals underwent Gaussian filtering, feature extraction, normalization, and principal component analysis (PCA) dimensionality reduction to obtain the main feature information and convert it into time series. Finally, the time series of dynamic gestures were matched based on the DTW algorithm, and the minimum Euclidean distance between different time series was calculated for prediction. We tested five dynamic gestures on ten subjects. The experiments were conducted for a total of ten rounds, and only one round was used as the training set. Results reveal that using the max feature can get an accuracy of 88.0% ± 5.9%. In addition, the energy feature (87.5% ± 6.4%) and the mean feature (85.2% ± 6.9%) also performed well. This research realizes the dynamic gesture recognition of A-mode ultrasonic for the first time, proving the potential of ultrasonic signals, and providing a wider range of use scenarios.

An enhanced EMG-driven Musculoskeletal model based on non-negative matrix factorization

In recent years, electromyography (EMG) signals have been widely used in human–machine interfaces (HMIs). Musculoskeletal models (MMs) have been employed to decode human movement intentions from EMG signals in HMIs. Non-negative matrix factorization (NMF) has been adopted to extract neural control information from surface EMG signals. The aim of this study was to investigate if the NMF could extract the control information from surface EMG signals to substitute muscle activations of the deep muscles of the MM. An enhanced MM based on NMF (NMF-MM) was proposed to predict wrist pronation/supination (Pro/Sup) joint angles from surface EMG signals. We extracted the muscle activations of a pair of virtual agonist–antagonistic muscles with NMF from multi-channel EMG signals and inputted the extracted signals into the MM. Eight able-bodied subjects were recruited and tested in four different upper limb postures. Eight bipolar electrodes were attached to the upper forearm to record EMG signals. The Pearson’s correlation coefficient (r) and the normalized root mean square error (NRMSE) between measured and predicted joint angles were adopted to quantify the performance of the decoding methods. The performance of the proposed NMF-MM was compared with that of the NMF, linear regression (LR), artificial neural network (ANN), LR based on NMF (NMF-LR), ANN based on NMF (NMF-ANN), LR based on time-domain features (TD-LR), and ANN based on time-domain features (TD-ANN). The results demonstrated that the NMF-MM outperformed the other methods with higher prediction accuracy and better robustness. This study proposed a promising method for predicting continuous and coordinated movements in HMIs.

Concealed Information Detection Using EEG for Lie Recognition by ERP P300 in Response to Visual Stimuli: a Review

Nowadays, lie detection based on electroencephalography (EEG) is a popular area of research. Current lie detectors can be controlled voluntarily and have several disadvantages. EEG-based lie detectors have become popular over polygraphs because human intentions cannot control them, are not based on subjective interpretation, and can therefore detect lies better. This paper's main objective was to give an overview of the scientific works on the recognition of concealed information using EEG for lie detection in response to visual stimuli of faces, as there is no existing review in this area. These were selected publications from the Web of Science (WoS) database published over the last five years. It was found that the Event-Related Potential (ERP) P300 is the most often used method for this purpose. The article contains a detailed overview of the methods used in scientific research in EEG-based lie detection using the ERP P300 component in response to known and unknown faces.

Adaptive cooperative control of a soft elbow rehabilitation exoskeleton based on improved joint torque estimation

A current challenge with robot-assisted rehabilitation is to combine the active involvement and voluntary participation of patients into the rehabilitation training process to enhance therapy outcome. This paper presents a soft elbow exoskeleton for the rehabilitation training of disabled patients. An adaptive cooperative control strategy is investigated to promote active participation based on an improved joint torque estimation method and a time-delay sliding mode control scheme. The surface electromyography signals from biceps and triceps are input into a Hill-type musculoskeletal model and a Gaussian radial basis functional network to estimate human elbow joint torque and motion intention. The joint torque estimation experiment, intention-based trajectory following experiment and patient-active free-motion training experiment are carried out by five healthy volunteers and five stroke patients to evaluate the performance of proposed control strategy. The results indicate that the proposed scheme can guarantee the joint torque estimation accuracy and position control accuracy. Moreover, it allow patients to actively dominate the training path based on their motion intention with different training intensity.

A Novel Noise Suppression and Artifact Removal Method of Mechanomyography Based on RLS, IGWO-VMD, and CEEMDAN

Mechanomyography (MMG) signals have extensive applications in muscle function assessment and human intention recognition. However, during signal acquisition, MMG signals are easily contaminated by noise and artifacts, which seriously affects the recognition of their characteristics. To address these issues, a novel noise suppression and artifact removal method based on recursive least square (RLS), improved Gray Wolf Optimizer-optimized variable mode decomposition (IGWO-VMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is proposed. In this paper, the RLS algorithm is first applied to adaptively filter out the power line interference (PLI). Then, IGWO is designed to select the appropriate VMD parameters and use the VMD to decompose the noisy signal into band-limited intrinsic mode functions (BLIMFs). In addition, the BLIMFs are classified into the low-frequency part and high-frequency part according to the given correlation coefficient (CC) threshold value. The effective components of the low-frequency part are identified by the center frequency. Meanwhile, the high-frequency part is decomposed by CEEMDAN, and its effective components are obtained according to the proposed sample entropy threshold range. Finally, the effective components of the low and high-frequency parts are reconstructed to obtain the denoised signal to realize the extraction of useful signals. Simulation experiment results demonstrate that the proposed method outperforms the classical methods and the designed IGWO-VMD method in terms of denoising performance. The effectiveness of the proposed method is verified through the measured MMG signal experiments. The proposed method not only effectively suppresses noise and artifacts but also overcomes the limitations of VMD and CCEMDAN.

Deep Learning-based human activity recognition using RGB images in Human-robot collaboration

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.

Recognition of Human Motion Intentions Based on Bayesian-Optimized XGBOOST Algorithm

In order to improve the recognition rate for lower extremity motion patterns, this study designs a recognition method for such patterns, which integrates electromyography (EMG) and inertial measurement unit (IMU) signals in three posture modes, including walking on the ground, squatting, and extending seated legs, to address the difficulty with obtaining high signal-to-noise ratio EMG and IMU signals synchronously. Besides, this study proposes a synchronous analysis method for EMG and IMU dual-mode information to correct antipower frequency interference accelerometer signals. The collected signals are preprocessed to extract eigenvalues. And by using the kernel principal component analysis (KPCA), the information on these eigenvalues is fused. Finally, according to the characteristics of the data, a Bayesian-optimized XGBOOST algorithm is designed. Lower-limb movement patterns are classified with the feature vector put into the optimization algorithm. Multiperson experimental results show that the average recognition accuracy for different poses can reach 94.42%, the average F 1 value 95.33%, and the average return value 95.68%, proving that the model proposed can be used to identify human motion intentions and its generalization ability can detect individual differences in human bodies.